From 11140e7683be14c2266b82459a9c965cd4ecbe6d Mon Sep 17 00:00:00 2001 From: MigueldelaVarga Date: Thu, 13 Jun 2024 17:20:17 +0200 Subject: [PATCH] Update with spremberg --- .idea/.gitignore | 2 + .../02_model_1_bayesian.ipynb | 28 +- .../01_read_OMF_sites.py | 6 +- .../03_model_spremberg_import.py | 222 ++++++++ .../02_model_1_OMF_to_subsurface.ipynb | 2 +- .../04_model_spremberg_building.py | 190 +++++++ .../04_model_spremberg_building.ipynb | 167 ++++++ .../02_model_1_gempy.ipynb | 2 +- .../01_read_OMF_sites.ipynb | 2 +- .../03_model_spremberg_import.ipynb | 277 ++++++++++ .../03_model_2_OMF_to_subsurface.py | 4 +- .../01_model_1_gempy_step_by_step.ipynb | 6 +- .../01_model_1_gempy_step_by_step.py | 7 +- .../01_thickness_problem_gempy.py | 14 +- .../02_model_1_OMF_to_subsurface.py | 2 +- .../02_model_1_bayesian.py | 39 +- .../01_thickness_problem_gempy.ipynb | 26 +- .../03_model_2_OMF_to_subsurface.ipynb | 4 +- .../02_model_1_gempy.py | 2 +- 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changed, 4825 insertions(+), 1457 deletions(-) create mode 100644 _downloads/1aeb58e5a4aa6113a6a48c4d659d2222/03_model_spremberg_import.py create mode 100644 _downloads/299bae2ea23dd4572c01f886c9168e59/04_model_spremberg_building.py create mode 100644 _downloads/4b511070d48395a0f9f67690590ca84d/04_model_spremberg_building.ipynb create mode 100644 _downloads/71916b46938f22a9c23a758261bcd310/03_model_spremberg_import.ipynb create mode 100644 _images/sphx_glr_03_model_spremberg_import_001.png create mode 100644 _images/sphx_glr_03_model_spremberg_import_002.png create mode 100644 _images/sphx_glr_03_model_spremberg_import_003.png create mode 100644 _images/sphx_glr_03_model_spremberg_import_004.png create mode 100644 _images/sphx_glr_03_model_spremberg_import_thumb.png create mode 100644 _images/sphx_glr_04_model_spremberg_building_001.png create mode 100644 _images/sphx_glr_04_model_spremberg_building_002.png create mode 100644 _images/sphx_glr_04_model_spremberg_building_003.png create mode 100644 _images/sphx_glr_04_model_spremberg_building_004.png create mode 100644 _images/sphx_glr_04_model_spremberg_building_005.png create mode 100644 _images/sphx_glr_04_model_spremberg_building_006.png create mode 100644 _images/sphx_glr_04_model_spremberg_building_007.png create mode 100644 _images/sphx_glr_04_model_spremberg_building_008.png create mode 100644 _images/sphx_glr_04_model_spremberg_building_009.png create mode 100644 _images/sphx_glr_04_model_spremberg_building_thumb.png create mode 100644 _sources/examples/02_structural_modeling/03_model_spremberg_import.rst.txt create mode 100644 _sources/examples/02_structural_modeling/04_model_spremberg_building.rst.txt create mode 100644 _sources/examples/sg_execution_times.rst.txt create mode 100644 _sources/sg_execution_times.rst.txt create mode 100644 examples/02_structural_modeling/03_model_spremberg_import.html create mode 100644 examples/02_structural_modeling/04_model_spremberg_building.html create mode 100644 examples/sg_execution_times.html create mode 100644 sg_execution_times.html diff --git a/.idea/.gitignore b/.idea/.gitignore index 13566b8..9f5c49d 100644 --- a/.idea/.gitignore +++ b/.idea/.gitignore @@ -6,3 +6,5 @@ # Datasource local storage ignored files /dataSources/ /dataSources.local.xml +# Harpooner +Harpooner Menu \ No newline at end of file diff --git a/_downloads/15667dfd325a23fa166b9479624ac830/02_model_1_bayesian.ipynb b/_downloads/15667dfd325a23fa166b9479624ac830/02_model_1_bayesian.ipynb index bc3f788..e41b3b2 100644 --- a/_downloads/15667dfd325a23fa166b9479624ac830/02_model_1_bayesian.ipynb +++ b/_downloads/15667dfd325a23fa166b9479624ac830/02_model_1_bayesian.ipynb @@ -26,7 +26,25 @@ }, "outputs": [], "source": [ - "import time\nimport os\nimport numpy as np\nimport xarray as xr\nimport pandas as pd\nimport torch\nimport pyro\nimport pyro.distributions as dist\nimport gempy as gp\nimport gempy_viewer as gpv\nfrom matplotlib import pyplot as plt\nfrom dotenv import dotenv_values\nfrom pyro.infer import MCMC, NUTS, Predictive\nimport arviz as az\n\nfrom gempy_probability.plot_posterior import default_red, default_blue\nfrom vector_geology.bayesian_helpers import calculate_scale_shift, gaussian_kernel\nfrom vector_geology.model_1_builder import initialize_geo_model, setup_geophysics\nfrom vector_geology.omf_to_gempy import process_file\nfrom vector_geology.utils import extend_box\nimport gempy_engine\nfrom gempy_engine.core.backend_tensor import BackendTensor" + "import os\nimport time\n\nimport arviz as az\nimport numpy as np\nimport pyro\nimport pyro.distributions as dist\nimport torch\nimport xarray as xr\nfrom dotenv import dotenv_values\nfrom matplotlib import pyplot as plt\nfrom pyro.infer import MCMC, NUTS, Predictive\n\nimport gempy as gp\nimport gempy_engine\nimport gempy_viewer as gpv\nfrom gempy_engine.core.backend_tensor import BackendTensor\nfrom gempy_probability.plot_posterior import default_red, default_blue\nfrom vector_geology.bayesian_helpers import calculate_scale_shift, gaussian_kernel\nfrom vector_geology.model_1_builder import initialize_geo_model, setup_geophysics\nfrom vector_geology.omf_to_gempy import process_file" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Config\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "seed = 123456\ntorch.manual_seed(seed)\npyro.set_rng_seed(seed)" ] }, { @@ -296,7 +314,7 @@ }, "outputs": [], "source": [ - "y_obs_list = torch.tensor(adapted_observed_grav.values).view(1, 17)\ninterpolation_options.mesh_extraction = False\ninterpolation_options.number_octree_levels = 1\ngeo_model.grid.set_inactive(\"topography\")\ngeo_model.grid.set_inactive(\"regular\")" + "y_obs_list = torch.tensor(adapted_observed_grav.values).view(1, 17)\ninterpolation_options.mesh_extraction = False\ninterpolation_options.number_octree_levels = 1\ngeo_model.grid.active_grids ^= gp.data.Grid.GridTypes.TOPOGRAPHY\ngeo_model.grid.active_grids ^= gp.data.Grid.GridTypes.DENSE" ] }, { @@ -314,7 +332,7 @@ }, "outputs": [], "source": [ - "if True:\n prior = Predictive(model, num_samples=50)(y_obs_list, interpolation_input=geo_model.interpolation_input)\n data = az.from_pyro(prior=prior)\n az.plot_trace(data.prior)\n plt.show()" + "raise NotImplementedError(\"From this point we need to optimize the code again.\")\nif True:\n prior = Predictive(model, num_samples=50)(y_obs_list, interpolation_input=geo_model.interpolation_input_copy)\n data = az.from_pyro(prior=prior)\n az.plot_trace(data.prior)\n plt.show()" ] }, { @@ -332,7 +350,7 @@ }, "outputs": [], "source": [ - "pyro.primitives.enable_validation(is_validate=True)\nnuts_kernel = NUTS(model)\nmcmc = MCMC(nuts_kernel, num_samples=1000, warmup_steps=300)\nmcmc.run(y_obs_list, interpolation_input=geo_model.interpolation_input)" + "pyro.primitives.enable_validation(is_validate=True)\nnuts_kernel = NUTS(model)\nmcmc = MCMC(nuts_kernel, num_samples=1000, warmup_steps=300)\nmcmc.run(y_obs_list, interpolation_input=geo_model.interpolation_input_copy)" ] }, { @@ -350,7 +368,7 @@ }, "outputs": [], "source": [ - "posterior_samples = mcmc.get_samples(50)\nposterior_predictive = Predictive(model, posterior_samples)(y_obs_list, interpolation_input=geo_model.interpolation_input)\ndata = az.from_pyro(\n posterior=mcmc,\n prior=prior,\n posterior_predictive=posterior_predictive\n)\naz.plot_trace(data)\nplt.show()" + "posterior_samples = mcmc.get_samples(50)\nposterior_predictive = Predictive(model, posterior_samples)(y_obs_list, interpolation_input=geo_model.interpolation_input_copy)\ndata = az.from_pyro(\n posterior=mcmc,\n prior=prior,\n posterior_predictive=posterior_predictive\n)\naz.plot_trace(data)\nplt.show()" ] }, { diff --git a/_downloads/182d45f17a36be0fd850895c86d70e9a/01_read_OMF_sites.py b/_downloads/182d45f17a36be0fd850895c86d70e9a/01_read_OMF_sites.py index b9a3b46..52eb6bc 100644 --- a/_downloads/182d45f17a36be0fd850895c86d70e9a/01_read_OMF_sites.py +++ b/_downloads/182d45f17a36be0fd850895c86d70e9a/01_read_OMF_sites.py @@ -14,10 +14,12 @@ import pyvista import subsurface from subsurface import TriSurf -from subsurface.visualization import to_pyvista_mesh, pv_plot -from subsurface.writer import base_structs_to_binary_file from dotenv import dotenv_values +from subsurface.modules.visualization import to_pyvista_mesh, pv_plot +from subsurface.modules.writer import base_structs_to_binary_file + + # %% # Load OMF Project # ---------------- diff --git a/_downloads/1aeb58e5a4aa6113a6a48c4d659d2222/03_model_spremberg_import.py b/_downloads/1aeb58e5a4aa6113a6a48c4d659d2222/03_model_spremberg_import.py new file mode 100644 index 0000000..f05af5e --- /dev/null +++ b/_downloads/1aeb58e5a4aa6113a6a48c4d659d2222/03_model_spremberg_import.py @@ -0,0 +1,222 @@ +""" +Construct Spremberg: Importing borehole data +-------------------------------------------- + +This example demonstrates how to construct a 3D geological model of the Model 1 deposit using GemPy. +It leverages custom APIs to streamline the modeling process. +""" +# %% [markdown] +# Import the necessary libraries for geological modeling and visualization. +# sphinx_gallery_thumbnail_number = -1 +import os +import pandas as pd +import pyvista + +import gempy as gp +import gempy_viewer as gpv +from subsurface.core.geological_formats.boreholes.boreholes import BoreholeSet, MergeOptions +from subsurface.core.geological_formats.boreholes.collars import Collars +from subsurface.core.geological_formats.boreholes.survey import Survey +from subsurface.core.reader_helpers.readers_data import GenericReaderFilesHelper +from subsurface.modules.reader.wells.read_borehole_interface import read_lith, read_survey, read_collar +from subsurface.modules.visualization import to_pyvista_line, to_pyvista_points, init_plotter + +# %% [markdown] +# Initialize the reader for the lithological data. Specify the file path and column mappings. +import dotenv +dotenv.load_dotenv() +reader: GenericReaderFilesHelper = GenericReaderFilesHelper( + file_or_buffer=os.getenv("PATH_TO_SPREMBERG_STRATIGRAPHY"), + columns_map={ + 'hole_id' : 'id', + 'depth_from': 'top', + 'depth_to' : 'base', + 'lit_code' : 'component lith' + } +) + +# Read the lithological data into a DataFrame. +lith: pd.DataFrame = read_lith(reader) + +# %% [markdown] +# Initialize the reader for the survey data. Specify the file path and column mappings. +reader: GenericReaderFilesHelper = GenericReaderFilesHelper( + file_or_buffer=os.getenv("PATH_TO_SPREMBERG_SURVEY"), + columns_map={ + 'depth' : 'md', + 'dip' : 'dip', + 'azimuth': 'azi' + }, +) + +# Read the survey data into a DataFrame. +df = read_survey(reader) + +# %% [markdown] +# Create a Survey object from the DataFrame and update it with lithological data. +survey: Survey = Survey.from_df(df) +survey.update_survey_with_lith(lith) + +# %% [markdown] +# Initialize the reader for the collar data. Specify the file path, header, and column mappings. +reader_collar: GenericReaderFilesHelper = GenericReaderFilesHelper( + file_or_buffer=os.getenv("PATH_TO_SPREMBERG_COLLAR"), + header=0, + usecols=[0, 1, 2, 4], + columns_map={ + "hole_id" : "id", + "X_GK5_incl_inserted": "x", + "Y__incl_inserted" : "y", + "Z_GK" : "z" + } +) + +# Read the collar data into a DataFrame and create a Collars object. +df_collar = read_collar(reader_collar) +collar = Collars.from_df(df_collar) + +# %% [markdown] +# Combine the collar and survey data into a BoreholeSet. +borehole_set = BoreholeSet( + collars=collar, + survey=survey, + merge_option=MergeOptions.INTERSECT +) + +# %% [markdown] +# Visualize the borehole trajectories and collars using PyVista. +well_mesh = to_pyvista_line( + line_set=borehole_set.combined_trajectory, + active_scalar="lith_ids", + radius=10 +) + +collars = to_pyvista_points( + borehole_set.collars.collar_loc, +) + +# Initialize the PyVista plotter. +pyvista_plotter = init_plotter() + +# Define the units limit for thresholding the well mesh. +units_limit = [0, 20] + +# Add the well mesh and collars to the plotter and display. +pyvista_plotter.add_mesh( + well_mesh.threshold(units_limit), + cmap="tab20c", + clim=units_limit +) + +pyvista_plotter.add_mesh( + collars, + point_size=10, + render_points_as_spheres=True +) + +pyvista_plotter.show() + +# %% [markdown] +# Create structural elements from the borehole set for different lithological units. +elements: list[gp.data.StructuralElement] = gp.structural_elements_from_borehole_set( + borehole_set=borehole_set, + elements_dict={ + "Buntsandstein" : { + "id" : 53_300, + "color": "#983999" + }, + "Werra-Anhydrit" : { + "id" : 61_730, + "color": "#00923f" + }, + "Kupfershiefer" : { + "id" : 61_760, + "color": "#da251d" + }, + "Zechsteinkonglomerat": { + "id" : 61_770, + "color": "#f8c300" + }, + "Rotliegend" : { + "id" : 62_000, + "color": "#bb825b" + } + } +) + +# %% [markdown] +# Group the structural elements into a StructuralGroup and create a StructuralFrame. +group = gp.data.StructuralGroup( + name="Stratigraphic Pile", + elements=elements, + structural_relation=gp.data.StackRelationType.ERODE +) +structural_frame = gp.data.StructuralFrame( + structural_groups=[group], + color_gen=gp.data.ColorsGenerator() +) + +# %% [markdown] +# Determine the extent of the geological model from the surface points coordinates. +all_surface_points_coords: gp.data.SurfacePointsTable = structural_frame.surface_points_copy +extent_from_data = all_surface_points_coords.xyz.min(axis=0), all_surface_points_coords.xyz.max(axis=0) + +# %% [markdown] +# Create a GeoModel with the specified extent, grid resolution, and interpolation options. +geo_model = gp.data.GeoModel( + name="Stratigraphic Pile", + structural_frame=structural_frame, + grid=gp.data.Grid( + extent=[extent_from_data[0][0], extent_from_data[1][0], extent_from_data[0][1], extent_from_data[1][1], extent_from_data[0][2], extent_from_data[1][2]], + resolution=(50, 50, 50) + ), + interpolation_options=gp.data.InterpolationOptions( + range=5, + c_o=10, + mesh_extraction=True, + number_octree_levels=3, + ), +) + +# %% [markdown] +# Visualize the 3D geological model using GemPy's plot_3d function. +gempy_plot = gpv.plot_3d( + model=geo_model, + kwargs_pyvista_bounds={ + 'show_xlabels': False, + 'show_ylabels': False, + }, + show=True, + image=True +) + +# %% [markdown] +# Combine all visual elements and display them together. +sp_mesh: pyvista.PolyData = gempy_plot.surface_points_mesh + +pyvista_plotter = init_plotter() +pyvista_plotter.show_bounds(all_edges=True) + +pyvista_plotter.add_mesh( + well_mesh.threshold(units_limit), + cmap="tab20c", + clim=units_limit +) + +pyvista_plotter.add_mesh( + collars, + point_size=10, + render_points_as_spheres=True +) + +pyvista_plotter.add_point_labels( + points=collar.collar_loc.points, + labels=collar.ids, + point_size=10, + shape_opacity=0.5, + font_size=12, + bold=True +) +pyvista_plotter.add_actor(gempy_plot.surface_points_actor) + +pyvista_plotter.show() diff --git a/_downloads/21b5cdd06c29a4da40678038c93313df/02_model_1_OMF_to_subsurface.ipynb b/_downloads/21b5cdd06c29a4da40678038c93313df/02_model_1_OMF_to_subsurface.ipynb index 36cb18b..b1928bf 100644 --- a/_downloads/21b5cdd06c29a4da40678038c93313df/02_model_1_OMF_to_subsurface.ipynb +++ b/_downloads/21b5cdd06c29a4da40678038c93313df/02_model_1_OMF_to_subsurface.ipynb @@ -33,7 +33,7 @@ }, "outputs": [], "source": [ - "import pandas as pd\nimport pyvista\nimport xarray\n\nimport subsurface\nfrom subsurface import TriSurf, LineSet\nfrom subsurface.visualization import to_pyvista_mesh, to_pyvista_line, init_plotter\nfrom vector_geology.utils import load_omf" + "import pandas as pd\nimport pyvista\nimport xarray\n\nimport subsurface\nfrom subsurface import TriSurf, LineSet\nfrom subsurface.modules.visualization import to_pyvista_mesh, to_pyvista_line, init_plotter\nfrom vector_geology.utils import load_omf" ] }, { diff --git a/_downloads/299bae2ea23dd4572c01f886c9168e59/04_model_spremberg_building.py b/_downloads/299bae2ea23dd4572c01f886c9168e59/04_model_spremberg_building.py new file mode 100644 index 0000000..63c3c8c --- /dev/null +++ b/_downloads/299bae2ea23dd4572c01f886c9168e59/04_model_spremberg_building.py @@ -0,0 +1,190 @@ +""" +Construct Spremberg: Building initial model +-------------------------------------------- + +This example demonstrates... + +""" + +# sphinx_gallery_thumbnail_number = -2 +import numpy as np + +import gempy as gp +import gempy_viewer as gpv +from subsurface.modules.visualization import to_pyvista_line, to_pyvista_points +from vector_geology.model_contructor.spremberg import generate_spremberg_model, get_spremberg_borehole_set, add_wells_plot + +# %% +elements_to_gempy = { + "Buntsandstein" : { + "id" : 53_300, + "color": "#983999" + }, + "Werra-Anhydrit" : { + "id" : 61_730, + "color": "#00923f" + }, + # "Kupferschiefer" : { + # "id" : 61_760, + # "color": "#da251d" + # }, + "Zechsteinkonglomerat": { + "id" : 61_770, + "color": "#f8c300" + }, + "Rotliegend" : { + "id" : 62_000, + "color": "#bb825b" + } +} + +spremberg_boreholes = get_spremberg_borehole_set() +geo_model: gp.data.GeoModel = generate_spremberg_model( + borehole_set=spremberg_boreholes, + elements_to_gempy=elements_to_gempy, + plot=False +) + +# %% +# Add one orientation to the model +rotliegend: gp.data.StructuralElement = geo_model.structural_frame.get_element_by_name("Rotliegend") +gp.add_orientations( + geo_model=geo_model, + x=[5_460_077.527386775, 5_450_077.527386775], + y=[5_720_030.2446156405, 5_710_030.2446156405], + z=[-600, -600], + elements_names=["Rotliegend", "Rotliegend"], + pole_vector=[ + np.array([.7, 0.7, 0.2]), + np.array([-1, 0, 0.2]) + ], +) + +# %% +pivot = [5_478_256.5, 5_698_528.946534388] +point_2 = [5_483_077.527386775, 5_710_030.2446156405] +point_3 = [5_474_977.5974836275, 5_712_059.373443342] +section_dict = { + 'section1': (pivot, point_2, [100, 100]), + 'section2': (pivot, point_3, [100, 100]), + 'section3': (point_2, point_3, [100, 100]) +} + + +# %% +gp.set_section_grid(geo_model.grid, section_dict) +gpv.plot_section_traces(geo_model) + +# %% +_ = gpv.plot_3d( + model=geo_model, + ve=10, + image=True, + transformed_data=True, + kwargs_pyvista_bounds={ + 'show_xlabels': False, + 'show_ylabels': False, + }, + kwargs_plot_data={ + 'arrow_size': 0.001000 + } +) + +# %% + +# * Ignore curvature for now +geo_model.interpolation_options.kernel_options.range = 3 +geo_model.interpolation_options.compute_scalar_gradient = False +geo_model.interpolation_options.evaluation_options.curvature_threshold = 0.4 +geo_model.interpolation_options.evaluation_options.number_octree_levels_surface = 5 + +geo_model.interpolation_options.evaluation_options.error_threshold = 1 +geo_model.interpolation_options.evaluation_options.verbose = True + + +gp.compute_model( + gempy_model=geo_model, + engine_config=gp.data.GemPyEngineConfig( + backend=gp.data.AvailableBackends.PYTORCH, + dtype="float64", + ) +) + +# %% +gpv.plot_2d( + model=geo_model, + figsize=(15, 15), + ve=10, + section_names=['section1', 'section2', 'section3'] +) + +# %% +gempy_plot = gpv.plot_3d( + model=geo_model, + show_lith=False, + ve=10, + show=False, + image=False, + transformed_data=False, + kwargs_pyvista_bounds={ + 'show_xlabels': True, + 'show_ylabels': True, + 'show_zlabels': False, + }, + kwargs_plot_data={ + 'arrow_size': 100.001000 + } +) + +well_mesh = to_pyvista_line( + line_set=spremberg_boreholes.combined_trajectory, + active_scalar="lith_ids", + radius=10 +) +units_limit = [0, 20] +collars = to_pyvista_points(spremberg_boreholes.collars.collar_loc) +gempy_plot.p.add_mesh( + well_mesh.threshold(units_limit), + cmap="tab20c", + clim=units_limit +) + +gempy_plot.p.add_mesh( + collars, + point_size=10, + render_points_as_spheres=True +) + +gempy_plot.p.add_point_labels( + points=spremberg_boreholes.collars.collar_loc.points, + labels=spremberg_boreholes.collars.ids, + point_size=3, + shape_opacity=0.5, + font_size=12, + bold=True +) + +gempy_plot.p.show() + + +# %% +# LiquidEarth Integration +# ~~~~~~~~~~~~~~~~~~~~~~~ +# Beyond the classical plotting capabilities introduced in GemPy v3, users can now also upload models to LiquidEarth. +# `LiquidEarth `_ is a collaborative platform designed for 3D visualization, +# developed by many of the main `gempy` maintainers, with a strong focus on collaboration and sharing. +# This makes it an excellent tool for sharing your models with others and viewing them across different platforms. +# To upload a model to LiquidEarth, you must have an account and a user token. Once your model is uploaded, +# you can easily share the link with anyone. + +# # %% +if True: + link = gpv.plot_to_liquid_earth( + geo_model=geo_model, + space_name="Spremberg", + file_name="gempy_model", + user_token=None, # If None, it will try to grab it from the environment + grab_link=True, + ) + + print(f"Generated Link: {link}") diff --git a/_downloads/4b511070d48395a0f9f67690590ca84d/04_model_spremberg_building.ipynb b/_downloads/4b511070d48395a0f9f67690590ca84d/04_model_spremberg_building.ipynb new file mode 100644 index 0000000..872ec92 --- /dev/null +++ b/_downloads/4b511070d48395a0f9f67690590ca84d/04_model_spremberg_building.ipynb @@ -0,0 +1,167 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\nfrom pyvista import set_plot_theme\nset_plot_theme('document')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Construct Spremberg: Building initial model\n\nThis example demonstrates...\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# sphinx_gallery_thumbnail_number = -2\nimport numpy as np\n\nimport gempy as gp\nimport gempy_viewer as gpv\nfrom subsurface.modules.visualization import to_pyvista_line, to_pyvista_points\nfrom vector_geology.model_contructor.spremberg import generate_spremberg_model, get_spremberg_borehole_set, add_wells_plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "elements_to_gempy = {\n \"Buntsandstein\" : {\n \"id\" : 53_300,\n \"color\": \"#983999\"\n },\n \"Werra-Anhydrit\" : {\n \"id\" : 61_730,\n \"color\": \"#00923f\"\n },\n # \"Kupferschiefer\" : {\n # \"id\" : 61_760,\n # \"color\": \"#da251d\"\n # },\n \"Zechsteinkonglomerat\": {\n \"id\" : 61_770,\n \"color\": \"#f8c300\"\n },\n \"Rotliegend\" : {\n \"id\" : 62_000,\n \"color\": \"#bb825b\"\n }\n}\n\nspremberg_boreholes = get_spremberg_borehole_set()\ngeo_model: gp.data.GeoModel = generate_spremberg_model(\n borehole_set=spremberg_boreholes,\n elements_to_gempy=elements_to_gempy,\n plot=False\n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Add one orientation to the model\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "rotliegend: gp.data.StructuralElement = geo_model.structural_frame.get_element_by_name(\"Rotliegend\")\ngp.add_orientations(\n geo_model=geo_model,\n x=[5_460_077.527386775, 5_450_077.527386775],\n y=[5_720_030.2446156405, 5_710_030.2446156405],\n z=[-600, -600],\n elements_names=[\"Rotliegend\", \"Rotliegend\"],\n pole_vector=[\n np.array([.7, 0.7, 0.2]),\n np.array([-1, 0, 0.2])\n ],\n)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pivot = [5_478_256.5, 5_698_528.946534388]\npoint_2 = [5_483_077.527386775, 5_710_030.2446156405]\npoint_3 = [5_474_977.5974836275, 5_712_059.373443342]\nsection_dict = {\n 'section1': (pivot, point_2, [100, 100]),\n 'section2': (pivot, point_3, [100, 100]),\n 'section3': (point_2, point_3, [100, 100])\n}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "gp.set_section_grid(geo_model.grid, section_dict)\ngpv.plot_section_traces(geo_model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "_ = gpv.plot_3d(\n model=geo_model,\n ve=10,\n image=True,\n transformed_data=True,\n kwargs_pyvista_bounds={\n 'show_xlabels': False,\n 'show_ylabels': False,\n },\n kwargs_plot_data={\n 'arrow_size': 0.001000\n }\n)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# * Ignore curvature for now\ngeo_model.interpolation_options.kernel_options.range = 3\ngeo_model.interpolation_options.compute_scalar_gradient = False\ngeo_model.interpolation_options.evaluation_options.curvature_threshold = 0.4\ngeo_model.interpolation_options.evaluation_options.number_octree_levels_surface = 5\n\ngeo_model.interpolation_options.evaluation_options.error_threshold = 1\ngeo_model.interpolation_options.evaluation_options.verbose = True\n\n\ngp.compute_model(\n gempy_model=geo_model,\n engine_config=gp.data.GemPyEngineConfig(\n backend=gp.data.AvailableBackends.PYTORCH,\n dtype=\"float64\",\n )\n)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "gpv.plot_2d(\n model=geo_model,\n figsize=(15, 15),\n ve=10,\n section_names=['section1', 'section2', 'section3']\n)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "gempy_plot = gpv.plot_3d(\n model=geo_model,\n show_lith=False,\n ve=10,\n show=False,\n image=False,\n transformed_data=False,\n kwargs_pyvista_bounds={\n 'show_xlabels': True,\n 'show_ylabels': True,\n 'show_zlabels': False,\n },\n kwargs_plot_data={\n 'arrow_size': 100.001000\n }\n)\n\nwell_mesh = to_pyvista_line(\n line_set=spremberg_boreholes.combined_trajectory,\n active_scalar=\"lith_ids\",\n radius=10\n)\nunits_limit = [0, 20]\ncollars = to_pyvista_points(spremberg_boreholes.collars.collar_loc)\ngempy_plot.p.add_mesh(\n well_mesh.threshold(units_limit),\n cmap=\"tab20c\",\n clim=units_limit\n)\n\ngempy_plot.p.add_mesh(\n collars,\n point_size=10,\n render_points_as_spheres=True\n)\n\ngempy_plot.p.add_point_labels(\n points=spremberg_boreholes.collars.collar_loc.points,\n labels=spremberg_boreholes.collars.ids,\n point_size=3,\n shape_opacity=0.5,\n font_size=12,\n bold=True\n)\n\ngempy_plot.p.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## LiquidEarth Integration\nBeyond the classical plotting capabilities introduced in GemPy v3, users can now also upload models to LiquidEarth. \n[LiquidEarth](https://www.terranigma-solutions.com/liquidearth) is a collaborative platform designed for 3D visualization,\ndeveloped by many of the main `gempy` maintainers, with a strong focus on collaboration and sharing. \nThis makes it an excellent tool for sharing your models with others and viewing them across different platforms.\nTo upload a model to LiquidEarth, you must have an account and a user token. Once your model is uploaded, \nyou can easily share the link with anyone.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# # %%\nif True:\n link = gpv.plot_to_liquid_earth(\n geo_model=geo_model,\n space_name=\"Spremberg\",\n file_name=\"gempy_model\",\n user_token=None, # If None, it will try to grab it from the environment\n grab_link=True,\n )\n\n print(f\"Generated Link: {link}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/_downloads/5d1a84a1ce4edd8dc607289ae695f0a9/02_model_1_gempy.ipynb b/_downloads/5d1a84a1ce4edd8dc607289ae695f0a9/02_model_1_gempy.ipynb index a3511aa..c9acdc1 100644 --- a/_downloads/5d1a84a1ce4edd8dc607289ae695f0a9/02_model_1_gempy.ipynb +++ b/_downloads/5d1a84a1ce4edd8dc607289ae695f0a9/02_model_1_gempy.ipynb @@ -170,7 +170,7 @@ }, "outputs": [], "source": [ - "gp.modify_surface_points(\n geo_model,\n slice=0,\n X=geo_model.surface_points.data[0][0] + 130,\n)" + "gp.modify_surface_points(\n geo_model,\n slice=0,\n X=geo_model.surface_points_copy.data[0][0] + 130,\n)" ] }, { diff --git a/_downloads/69f617885fefd9344a9bb650daa0e46f/01_read_OMF_sites.ipynb b/_downloads/69f617885fefd9344a9bb650daa0e46f/01_read_OMF_sites.ipynb index 2d705b1..ed345c5 100644 --- a/_downloads/69f617885fefd9344a9bb650daa0e46f/01_read_OMF_sites.ipynb +++ b/_downloads/69f617885fefd9344a9bb650daa0e46f/01_read_OMF_sites.ipynb @@ -33,7 +33,7 @@ }, "outputs": [], "source": [ - "import omfvista\nimport pyvista\nimport subsurface\nfrom subsurface import TriSurf\nfrom subsurface.visualization import to_pyvista_mesh, pv_plot\nfrom subsurface.writer import base_structs_to_binary_file\nfrom dotenv import dotenv_values" + "import omfvista\nimport pyvista\nimport subsurface\nfrom subsurface import TriSurf\nfrom dotenv import dotenv_values\n\nfrom subsurface.modules.visualization import to_pyvista_mesh, pv_plot\nfrom subsurface.modules.writer import base_structs_to_binary_file" ] }, { diff --git a/_downloads/71916b46938f22a9c23a758261bcd310/03_model_spremberg_import.ipynb b/_downloads/71916b46938f22a9c23a758261bcd310/03_model_spremberg_import.ipynb new file mode 100644 index 0000000..13e24f1 --- /dev/null +++ b/_downloads/71916b46938f22a9c23a758261bcd310/03_model_spremberg_import.ipynb @@ -0,0 +1,277 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\nfrom pyvista import set_plot_theme\nset_plot_theme('document')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Construct Spremberg: Importing borehole data\n\nThis example demonstrates how to construct a 3D geological model of the Model 1 deposit using GemPy. \nIt leverages custom APIs to streamline the modeling process.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import the necessary libraries for geological modeling and visualization.\nsphinx_gallery_thumbnail_number = -1\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import os\nimport pandas as pd\nimport pyvista\n\nimport gempy as gp\nimport gempy_viewer as gpv\nfrom subsurface.core.geological_formats.boreholes.boreholes import BoreholeSet, MergeOptions\nfrom subsurface.core.geological_formats.boreholes.collars import Collars\nfrom subsurface.core.geological_formats.boreholes.survey import Survey\nfrom subsurface.core.reader_helpers.readers_data import GenericReaderFilesHelper\nfrom subsurface.modules.reader.wells.read_borehole_interface import read_lith, read_survey, read_collar\nfrom subsurface.modules.visualization import to_pyvista_line, to_pyvista_points, init_plotter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Initialize the reader for the lithological data. Specify the file path and column mappings.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import dotenv\ndotenv.load_dotenv()\nreader: GenericReaderFilesHelper = GenericReaderFilesHelper(\n file_or_buffer=os.getenv(\"PATH_TO_SPREMBERG_STRATIGRAPHY\"),\n columns_map={\n 'hole_id' : 'id',\n 'depth_from': 'top',\n 'depth_to' : 'base',\n 'lit_code' : 'component lith'\n }\n)\n\n# Read the lithological data into a DataFrame.\nlith: pd.DataFrame = read_lith(reader)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Initialize the reader for the survey data. Specify the file path and column mappings.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "reader: GenericReaderFilesHelper = GenericReaderFilesHelper(\n file_or_buffer=os.getenv(\"PATH_TO_SPREMBERG_SURVEY\"),\n columns_map={\n 'depth' : 'md',\n 'dip' : 'dip',\n 'azimuth': 'azi'\n },\n)\n\n# Read the survey data into a DataFrame.\ndf = read_survey(reader)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a Survey object from the DataFrame and update it with lithological data.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "survey: Survey = Survey.from_df(df)\nsurvey.update_survey_with_lith(lith)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Initialize the reader for the collar data. Specify the file path, header, and column mappings.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "reader_collar: GenericReaderFilesHelper = GenericReaderFilesHelper(\n file_or_buffer=os.getenv(\"PATH_TO_SPREMBERG_COLLAR\"),\n header=0,\n usecols=[0, 1, 2, 4],\n columns_map={\n \"hole_id\" : \"id\",\n \"X_GK5_incl_inserted\": \"x\",\n \"Y__incl_inserted\" : \"y\",\n \"Z_GK\" : \"z\"\n }\n)\n\n# Read the collar data into a DataFrame and create a Collars object.\ndf_collar = read_collar(reader_collar)\ncollar = Collars.from_df(df_collar)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Combine the collar and survey data into a BoreholeSet.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "borehole_set = BoreholeSet(\n collars=collar,\n survey=survey,\n merge_option=MergeOptions.INTERSECT\n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visualize the borehole trajectories and collars using PyVista.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "well_mesh = to_pyvista_line(\n line_set=borehole_set.combined_trajectory,\n active_scalar=\"lith_ids\",\n radius=10\n)\n\ncollars = to_pyvista_points(\n borehole_set.collars.collar_loc,\n)\n\n# Initialize the PyVista plotter.\npyvista_plotter = init_plotter()\n\n# Define the units limit for thresholding the well mesh.\nunits_limit = [0, 20]\n\n# Add the well mesh and collars to the plotter and display.\npyvista_plotter.add_mesh(\n well_mesh.threshold(units_limit),\n cmap=\"tab20c\",\n clim=units_limit\n)\n\npyvista_plotter.add_mesh(\n collars,\n point_size=10,\n render_points_as_spheres=True\n)\n\npyvista_plotter.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create structural elements from the borehole set for different lithological units.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "elements: list[gp.data.StructuralElement] = gp.structural_elements_from_borehole_set(\n borehole_set=borehole_set,\n elements_dict={\n \"Buntsandstein\" : {\n \"id\" : 53_300,\n \"color\": \"#983999\"\n },\n \"Werra-Anhydrit\" : {\n \"id\" : 61_730,\n \"color\": \"#00923f\"\n },\n \"Kupfershiefer\" : {\n \"id\" : 61_760,\n \"color\": \"#da251d\"\n },\n \"Zechsteinkonglomerat\": {\n \"id\" : 61_770,\n \"color\": \"#f8c300\"\n },\n \"Rotliegend\" : {\n \"id\" : 62_000,\n \"color\": \"#bb825b\"\n }\n }\n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Group the structural elements into a StructuralGroup and create a StructuralFrame.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "group = gp.data.StructuralGroup(\n name=\"Stratigraphic Pile\",\n elements=elements,\n structural_relation=gp.data.StackRelationType.ERODE\n)\nstructural_frame = gp.data.StructuralFrame(\n structural_groups=[group],\n color_gen=gp.data.ColorsGenerator()\n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Determine the extent of the geological model from the surface points coordinates.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "all_surface_points_coords: gp.data.SurfacePointsTable = structural_frame.surface_points_copy\nextent_from_data = all_surface_points_coords.xyz.min(axis=0), all_surface_points_coords.xyz.max(axis=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a GeoModel with the specified extent, grid resolution, and interpolation options.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "geo_model = gp.data.GeoModel(\n name=\"Stratigraphic Pile\",\n structural_frame=structural_frame,\n grid=gp.data.Grid(\n extent=[extent_from_data[0][0], extent_from_data[1][0], extent_from_data[0][1], extent_from_data[1][1], extent_from_data[0][2], extent_from_data[1][2]],\n resolution=(50, 50, 50)\n ),\n interpolation_options=gp.data.InterpolationOptions(\n range=5,\n c_o=10,\n mesh_extraction=True,\n number_octree_levels=3,\n ),\n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visualize the 3D geological model using GemPy's plot_3d function.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "gempy_plot = gpv.plot_3d(\n model=geo_model,\n kwargs_pyvista_bounds={\n 'show_xlabels': False,\n 'show_ylabels': False,\n },\n show=True,\n image=True\n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Combine all visual elements and display them together.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sp_mesh: pyvista.PolyData = gempy_plot.surface_points_mesh\n\npyvista_plotter = init_plotter()\npyvista_plotter.show_bounds(all_edges=True)\n\npyvista_plotter.add_mesh(\n well_mesh.threshold(units_limit),\n cmap=\"tab20c\",\n clim=units_limit\n)\n\npyvista_plotter.add_mesh(\n collars,\n point_size=10,\n render_points_as_spheres=True\n)\n\npyvista_plotter.add_point_labels(\n points=collar.collar_loc.points,\n labels=collar.ids,\n point_size=10,\n shape_opacity=0.5,\n font_size=12,\n bold=True\n)\npyvista_plotter.add_actor(gempy_plot.surface_points_actor)\n\npyvista_plotter.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/_downloads/7ab9d4e8019cc7d15d0fdcf29a8e490c/03_model_2_OMF_to_subsurface.py b/_downloads/7ab9d4e8019cc7d15d0fdcf29a8e490c/03_model_2_OMF_to_subsurface.py index 1603446..86fa758 100644 --- a/_downloads/7ab9d4e8019cc7d15d0fdcf29a8e490c/03_model_2_OMF_to_subsurface.py +++ b/_downloads/7ab9d4e8019cc7d15d0fdcf29a8e490c/03_model_2_OMF_to_subsurface.py @@ -18,7 +18,7 @@ from dotenv import dotenv_values import subsurface from subsurface import TriSurf, LineSet -from subsurface.visualization import to_pyvista_mesh, to_pyvista_line, init_plotter +from subsurface.modules.visualization import to_pyvista_mesh, to_pyvista_line, init_plotter # %% # Load OMF Project @@ -64,7 +64,7 @@ def load_omf(): cell_data = {name: unstruct_pyvista.cell_data[name] for name in unstruct_pyvista.cell_data} # Process based on cell type - match polydata_obj.cell_type(0): + match polydata_obj.get_cell(0).type: case pyvista.CellType.TRIANGLE: # Process triangle mesh cells_pyvista = unstruct_pyvista.cells.reshape(-1, 4)[:, 1:] diff --git a/_downloads/81038bb04125592c3c65856ab7d33bba/01_model_1_gempy_step_by_step.ipynb b/_downloads/81038bb04125592c3c65856ab7d33bba/01_model_1_gempy_step_by_step.ipynb index 93af6ca..40baa07 100644 --- a/_downloads/81038bb04125592c3c65856ab7d33bba/01_model_1_gempy_step_by_step.ipynb +++ b/_downloads/81038bb04125592c3c65856ab7d33bba/01_model_1_gempy_step_by_step.ipynb @@ -26,7 +26,7 @@ }, "outputs": [], "source": [ - "# Import necessary libraries\nimport time\nimport numpy as np\nimport os\nimport xarray as xr\nfrom dotenv import dotenv_values\nfrom vector_geology.omf_to_gempy import process_file\nimport gempy as gp\nimport gempy_viewer as gpv\nfrom vector_geology.model_building_functions import optimize_nuggets_for_group\nfrom gempy_engine.core.data.kernel_classes.kernel_functions import AvailableKernelFunctions\n\n# Start timer to track execution time\nstart_time = time.time()" + "# Import necessary libraries\nimport time\nimport numpy as np\nimport os\nfrom dotenv import dotenv_values\nfrom vector_geology.omf_to_gempy import process_file\nimport gempy as gp\nimport gempy_viewer as gpv\nfrom vector_geology.model_building_functions import optimize_nuggets_for_group\nfrom gempy_engine.core.data.kernel_classes.kernel_functions import AvailableKernelFunctions\n\n# Start timer to track execution time\nstart_time = time.time()" ] }, { @@ -62,7 +62,7 @@ }, "outputs": [], "source": [ - "# Define structural groups and their relationships\nstructural_group_red = gp.data.StructuralGroup(\n name=\"Red\",\n elements=[structural_elements[i] for i in [0, 4, 8]],\n structural_relation=gp.data.StackRelationType.ERODE\n)\nstructural_group_green = gp.data.StructuralGroup(\n name=\"Green\",\n elements=[structural_elements[i] for i in [5]],\n structural_relation=gp.data.StackRelationType.ERODE\n)\nstructural_group_blue = gp.data.StructuralGroup(\n name=\"Blue\",\n elements=[structural_elements[i] for i in [2, 3]],\n structural_relation=gp.data.StackRelationType.ERODE\n)\nstructural_group_intrusion = gp.data.StructuralGroup(\n name=\"Intrusion\",\n elements=[structural_elements[i] for i in [1]],\n structural_relation=gp.data.StackRelationType.ERODE\n)\n\n# Combine structural groups into a frame\nstructural_groups = [structural_group_intrusion, structural_group_green, structural_group_blue, structural_group_red]\nstructural_frame = gp.data.StructuralFrame(\n structural_groups=structural_groups[2:],\n color_gen=color_gen\n)\n\n# Create the GeoModel object\ngeo_model: gp.data.GeoModel = gp.create_geomodel(\n project_name='Tutorial_ch1_1_Basics',\n extent=global_extent,\n resolution=[20, 10, 20],\n refinement=6,\n structural_frame=structural_frame\n)" + "# Define structural groups and their relationships\nstructural_group_red = gp.data.StructuralGroup(\n name=\"Red\",\n elements=[structural_elements[i] for i in [0, 4, 8]],\n structural_relation=gp.data.StackRelationType.ERODE\n)\nstructural_group_green = gp.data.StructuralGroup(\n name=\"Green\",\n elements=[structural_elements[i] for i in [5]],\n structural_relation=gp.data.StackRelationType.ERODE\n)\nstructural_group_blue = gp.data.StructuralGroup(\n name=\"Blue\",\n elements=[structural_elements[i] for i in [2, 3]],\n structural_relation=gp.data.StackRelationType.ERODE\n)\nstructural_group_intrusion = gp.data.StructuralGroup(\n name=\"Intrusion\",\n elements=[structural_elements[i] for i in [1]],\n structural_relation=gp.data.StackRelationType.ERODE\n)\n\n# Combine structural groups into a frame\nstructural_groups = [structural_group_intrusion, structural_group_green, structural_group_blue, structural_group_red]\nstructural_frame = gp.data.StructuralFrame(\n structural_groups=structural_groups[2:],\n color_gen=color_gen\n)\n\n# Create the GeoModel object\ngeo_model: gp.data.GeoModel = gp.create_geomodel(\n project_name='Tutorial_ch1_1_Basics',\n extent=global_extent,\n refinement=5,\n structural_frame=structural_frame\n)" ] }, { @@ -98,7 +98,7 @@ }, "outputs": [], "source": [ - "TRIGGER_OPTIMIZE_NUGGETS = False\nAPPLY_OPTIMIZED_NUGGETS = True\nif TRIGGER_OPTIMIZE_NUGGETS:\n\n # Optimization process for different structural groups\n geo_model.interpolation_options.kernel_options.range = 0.7\n geo_model.interpolation_options.kernel_options.c_o = 4\n optimize_nuggets_for_group(\n geo_model=geo_model,\n structural_group=structural_group_red,\n plot_evaluation=False,\n plot_result=True\n )\n geo_model.interpolation_options.kernel_options.range = 2\n geo_model.interpolation_options.kernel_options.c_o = 4\n optimize_nuggets_for_group(\n geo_model=geo_model,\n structural_group=structural_group_blue,\n plot_evaluation=False,\n plot_result=False\n )\n optimize_nuggets_for_group(\n geo_model=geo_model,\n structural_group=structural_group_green,\n plot_evaluation=False,\n plot_result=True\n )\n\nif APPLY_OPTIMIZED_NUGGETS:\n # Apply optimized nuggets to the model\n loaded_nuggets_red = np.load(\"../temp/nuggets_Red.npy\")\n loaded_nuggets_green = np.load(\"../temp/nuggets_Green.npy\")\n loaded_nuggets_blue = np.load(\"../temp/nuggets_Blue.npy\")\n gp.modify_surface_points(\n geo_model,\n slice=None,\n elements_names=[element.name for element in geo_model.structural_frame.get_group_by_name('Red').elements],\n nugget=loaded_nuggets_red\n )\n if True: # Conditional application of nuggets\n gp.modify_surface_points(\n geo_model,\n slice=None,\n elements_names=[element.name for element in geo_model.structural_frame.get_group_by_name('Blue').elements],\n nugget=loaded_nuggets_blue\n )" + "TRIGGER_OPTIMIZE_NUGGETS = False\nAPPLY_OPTIMIZED_NUGGETS = True\nif TRIGGER_OPTIMIZE_NUGGETS:\n geo_model.interpolation_options.cache_mode = gp.data.InterpolationOptions.CacheMode.NO_CACHE\n \n # Optimization process for different structural groups\n geo_model.interpolation_options.kernel_options.range = 0.7\n geo_model.interpolation_options.kernel_options.c_o = 4\n optimize_nuggets_for_group(\n geo_model=geo_model,\n structural_group=structural_group_red,\n plot_evaluation=False,\n plot_result=True\n )\n geo_model.interpolation_options.kernel_options.range = 2\n geo_model.interpolation_options.kernel_options.c_o = 4\n optimize_nuggets_for_group(\n geo_model=geo_model,\n structural_group=structural_group_blue,\n plot_evaluation=False,\n plot_result=False\n )\n optimize_nuggets_for_group(\n geo_model=geo_model,\n structural_group=structural_group_green,\n plot_evaluation=False,\n plot_result=True\n )\n\nif APPLY_OPTIMIZED_NUGGETS:\n # Apply optimized nuggets to the model\n loaded_nuggets_red = np.load(\"../temp/nuggets_Red.npy\")\n loaded_nuggets_green = np.load(\"../temp/nuggets_Green.npy\")\n loaded_nuggets_blue = np.load(\"../temp/nuggets_Blue.npy\")\n gp.modify_surface_points(\n geo_model,\n slice=None,\n elements_names=[element.name for element in geo_model.structural_frame.get_group_by_name('Red').elements],\n nugget=loaded_nuggets_red\n )\n if True: # Conditional application of nuggets\n gp.modify_surface_points(\n geo_model,\n slice=None,\n elements_names=[element.name for element in geo_model.structural_frame.get_group_by_name('Blue').elements],\n nugget=loaded_nuggets_blue\n )" ] }, { diff --git a/_downloads/9a11f8f0374b9be93df13a38a958da8a/01_model_1_gempy_step_by_step.py b/_downloads/9a11f8f0374b9be93df13a38a958da8a/01_model_1_gempy_step_by_step.py index c8b676f..8fe40d3 100644 --- a/_downloads/9a11f8f0374b9be93df13a38a958da8a/01_model_1_gempy_step_by_step.py +++ b/_downloads/9a11f8f0374b9be93df13a38a958da8a/01_model_1_gempy_step_by_step.py @@ -9,7 +9,6 @@ import time import numpy as np import os -import xarray as xr from dotenv import dotenv_values from vector_geology.omf_to_gempy import process_file import gempy as gp @@ -75,8 +74,7 @@ geo_model: gp.data.GeoModel = gp.create_geomodel( project_name='Tutorial_ch1_1_Basics', extent=global_extent, - resolution=[20, 10, 20], - refinement=6, + refinement=5, structural_frame=structural_frame ) @@ -101,7 +99,8 @@ TRIGGER_OPTIMIZE_NUGGETS = False APPLY_OPTIMIZED_NUGGETS = True if TRIGGER_OPTIMIZE_NUGGETS: - + geo_model.interpolation_options.cache_mode = gp.data.InterpolationOptions.CacheMode.NO_CACHE + # Optimization process for different structural groups geo_model.interpolation_options.kernel_options.range = 0.7 geo_model.interpolation_options.kernel_options.c_o = 4 diff --git a/_downloads/a51ee1dc5ae51d9e0b87a1974006e940/01_thickness_problem_gempy.py b/_downloads/a51ee1dc5ae51d9e0b87a1974006e940/01_thickness_problem_gempy.py index 2398c43..bf22eba 100644 --- a/_downloads/a51ee1dc5ae51d9e0b87a1974006e940/01_thickness_problem_gempy.py +++ b/_downloads/a51ee1dc5ae51d9e0b87a1974006e940/01_thickness_problem_gempy.py @@ -23,6 +23,12 @@ from gempy_probability.plot_posterior import default_red, default_blue # sphinx_gallery_thumbnail_number = -1 +# %% +# Config +seed = 123456 +torch.manual_seed(seed) +pyro.set_rng_seed(seed) + # %% # Set the data path data_path = os.path.abspath('../') @@ -66,7 +72,7 @@ def plot_geo_setting_well(geo_model): project_name='Wells', extent=[0, 12000, -500, 500, 0, 4000], resolution=[100, 2, 100], - refinement=1, + # refinement=1, importer_helper=gp.data.ImporterHelper( path_to_orientations=data_path + "/data/2-layers/2-layers_orientations.csv", path_to_surface_points=data_path + "/data/2-layers/2-layers_surface_points.csv" @@ -82,7 +88,7 @@ def plot_geo_setting_well(geo_model): geo_model.interpolation_options.uni_degree = 0 geo_model.interpolation_options.mesh_extraction = False -geo_model.interpolation_options.sigmoid_slope = 1100. +geo_model.interpolation_options.sigmoid_slope = -1 # ! Temporary fix to set the hard sigmoid # %% # Setting up a Custom Grid @@ -126,7 +132,7 @@ def plot_geo_setting_well(geo_model): # By using Pyro, a probabilistic programming language, we define a model that integrates # geological data with uncertainty quantification. -sp_coords_copy = geo_model.interpolation_input.surface_points.sp_coords.copy() +sp_coords_copy = geo_model.interpolation_input_copy.surface_points.sp_coords.copy() # Change the backend to PyTorch for probabilistic modeling BackendTensor.change_backend_gempy(engine_backend=gp.data.AvailableBackends.PYTORCH) @@ -149,7 +155,7 @@ def model(y_obs_list): mu_top = pyro.sample(r'$\mu_{top}$', dist.Normal(prior_mean, torch.tensor(0.02, dtype=torch.float64))) # Update the model with the new top layer's location - interpolation_input = geo_model.interpolation_input + interpolation_input = geo_model.interpolation_input_copy interpolation_input.surface_points.sp_coords = torch.index_put( interpolation_input.surface_points.sp_coords, (torch.tensor([0]), torch.tensor([2])), diff --git a/_downloads/b6f6dddf3369cf324da24450f201df11/02_model_1_OMF_to_subsurface.py b/_downloads/b6f6dddf3369cf324da24450f201df11/02_model_1_OMF_to_subsurface.py index baadc8d..453b0f7 100644 --- a/_downloads/b6f6dddf3369cf324da24450f201df11/02_model_1_OMF_to_subsurface.py +++ b/_downloads/b6f6dddf3369cf324da24450f201df11/02_model_1_OMF_to_subsurface.py @@ -16,7 +16,7 @@ import subsurface from subsurface import TriSurf, LineSet -from subsurface.visualization import to_pyvista_mesh, to_pyvista_line, init_plotter +from subsurface.modules.visualization import to_pyvista_mesh, to_pyvista_line, init_plotter from vector_geology.utils import load_omf # %% diff --git a/_downloads/c3dcc50fab252e61ec89f67816b606a4/02_model_1_bayesian.py b/_downloads/c3dcc50fab252e61ec89f67816b606a4/02_model_1_bayesian.py index 9815ae1..1043909 100644 --- a/_downloads/c3dcc50fab252e61ec89f67816b606a4/02_model_1_bayesian.py +++ b/_downloads/c3dcc50fab252e61ec89f67816b606a4/02_model_1_bayesian.py @@ -6,28 +6,34 @@ This example demonstrates a probabilistic inversion of a geological model using Bayesian methods. """ -import time import os +import time + +import arviz as az import numpy as np -import xarray as xr -import pandas as pd -import torch import pyro import pyro.distributions as dist -import gempy as gp -import gempy_viewer as gpv -from matplotlib import pyplot as plt +import torch +import xarray as xr from dotenv import dotenv_values +from matplotlib import pyplot as plt from pyro.infer import MCMC, NUTS, Predictive -import arviz as az +import gempy as gp +import gempy_engine +import gempy_viewer as gpv +from gempy_engine.core.backend_tensor import BackendTensor from gempy_probability.plot_posterior import default_red, default_blue from vector_geology.bayesian_helpers import calculate_scale_shift, gaussian_kernel from vector_geology.model_1_builder import initialize_geo_model, setup_geophysics from vector_geology.omf_to_gempy import process_file -from vector_geology.utils import extend_box -import gempy_engine -from gempy_engine.core.backend_tensor import BackendTensor + + +# %% +# Config +seed = 123456 +torch.manual_seed(seed) +pyro.set_rng_seed(seed) # %% # Start the timer for benchmarking purposes @@ -167,13 +173,14 @@ def model(y_obs_list, interpolation_input): y_obs_list = torch.tensor(adapted_observed_grav.values).view(1, 17) interpolation_options.mesh_extraction = False interpolation_options.number_octree_levels = 1 -geo_model.grid.set_inactive("topography") -geo_model.grid.set_inactive("regular") +geo_model.grid.active_grids ^= gp.data.Grid.GridTypes.TOPOGRAPHY +geo_model.grid.active_grids ^= gp.data.Grid.GridTypes.DENSE # %% # Perform prior sampling and visualize the results +raise NotImplementedError("From this point we need to optimize the code again.") if True: - prior = Predictive(model, num_samples=50)(y_obs_list, interpolation_input=geo_model.interpolation_input) + prior = Predictive(model, num_samples=50)(y_obs_list, interpolation_input=geo_model.interpolation_input_copy) data = az.from_pyro(prior=prior) az.plot_trace(data.prior) plt.show() @@ -183,12 +190,12 @@ def model(y_obs_list, interpolation_input): pyro.primitives.enable_validation(is_validate=True) nuts_kernel = NUTS(model) mcmc = MCMC(nuts_kernel, num_samples=1000, warmup_steps=300) -mcmc.run(y_obs_list, interpolation_input=geo_model.interpolation_input) +mcmc.run(y_obs_list, interpolation_input=geo_model.interpolation_input_copy) # %% # Analyze posterior samples and predictives, and visualize the results posterior_samples = mcmc.get_samples(50) -posterior_predictive = Predictive(model, posterior_samples)(y_obs_list, interpolation_input=geo_model.interpolation_input) +posterior_predictive = Predictive(model, posterior_samples)(y_obs_list, interpolation_input=geo_model.interpolation_input_copy) data = az.from_pyro( posterior=mcmc, prior=prior, diff --git a/_downloads/c6e7636b7672db18e241e4f58389664c/01_thickness_problem_gempy.ipynb b/_downloads/c6e7636b7672db18e241e4f58389664c/01_thickness_problem_gempy.ipynb index 372c490..6e89ca0 100644 --- a/_downloads/c6e7636b7672db18e241e4f58389664c/01_thickness_problem_gempy.ipynb +++ b/_downloads/c6e7636b7672db18e241e4f58389664c/01_thickness_problem_gempy.ipynb @@ -29,6 +29,24 @@ "import os\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport torch\nimport pyro\nimport pyro.distributions as dist\nfrom pyro.infer import MCMC, NUTS, Predictive\nfrom pyro.infer.autoguide import init_to_mean\n\nimport gempy as gp\nimport gempy_engine\nimport gempy_viewer as gpv\nfrom gempy_engine.core.backend_tensor import BackendTensor\nimport arviz as az\nfrom gempy_probability.plot_posterior import default_red, default_blue\n# sphinx_gallery_thumbnail_number = -1" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Config\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "seed = 123456\ntorch.manual_seed(seed)\npyro.set_rng_seed(seed)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -80,7 +98,7 @@ }, "outputs": [], "source": [ - "geo_model = gp.create_geomodel(\n project_name='Wells',\n extent=[0, 12000, -500, 500, 0, 4000],\n resolution=[100, 2, 100],\n refinement=1,\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=data_path + \"/data/2-layers/2-layers_orientations.csv\",\n path_to_surface_points=data_path + \"/data/2-layers/2-layers_surface_points.csv\"\n )\n)" + "geo_model = gp.create_geomodel(\n project_name='Wells',\n extent=[0, 12000, -500, 500, 0, 4000],\n resolution=[100, 2, 100],\n # refinement=1,\n importer_helper=gp.data.ImporterHelper(\n path_to_orientations=data_path + \"/data/2-layers/2-layers_orientations.csv\",\n path_to_surface_points=data_path + \"/data/2-layers/2-layers_surface_points.csv\"\n )\n)" ] }, { @@ -98,7 +116,7 @@ }, "outputs": [], "source": [ - "geo_model.interpolation_options.uni_degree = 0\ngeo_model.interpolation_options.mesh_extraction = False\ngeo_model.interpolation_options.sigmoid_slope = 1100." + "geo_model.interpolation_options.uni_degree = 0\ngeo_model.interpolation_options.mesh_extraction = False\ngeo_model.interpolation_options.sigmoid_slope = -1 # ! Temporary fix to set the hard sigmoid" ] }, { @@ -170,7 +188,7 @@ }, "outputs": [], "source": [ - "sp_coords_copy = geo_model.interpolation_input.surface_points.sp_coords.copy()\n# Change the backend to PyTorch for probabilistic modeling\nBackendTensor.change_backend_gempy(engine_backend=gp.data.AvailableBackends.PYTORCH)" + "sp_coords_copy = geo_model.interpolation_input_copy.surface_points.sp_coords.copy()\n# Change the backend to PyTorch for probabilistic modeling\nBackendTensor.change_backend_gempy(engine_backend=gp.data.AvailableBackends.PYTORCH)" ] }, { @@ -188,7 +206,7 @@ }, "outputs": [], "source": [ - "def model(y_obs_list):\n \"\"\"\n This Pyro model represents the probabilistic aspects of the geological model.\n It defines a prior distribution for the top layer's location and \n computes the thickness of the geological layer as an observed variable.\n \"\"\"\n # Define prior for the top layer's location\n prior_mean = sp_coords_copy[0, 2]\n mu_top = pyro.sample(r'$\\mu_{top}$', dist.Normal(prior_mean, torch.tensor(0.02, dtype=torch.float64)))\n\n # Update the model with the new top layer's location\n interpolation_input = geo_model.interpolation_input\n interpolation_input.surface_points.sp_coords = torch.index_put(\n interpolation_input.surface_points.sp_coords,\n (torch.tensor([0]), torch.tensor([2])),\n mu_top\n )\n\n # Compute the geological model\n geo_model.solutions = gempy_engine.compute_model(\n interpolation_input=interpolation_input,\n options=geo_model.interpolation_options,\n data_descriptor=geo_model.input_data_descriptor,\n geophysics_input=geo_model.geophysics_input,\n )\n\n # Compute and observe the thickness of the geological layer\n simulated_well = geo_model.solutions.octrees_output[0].last_output_center.custom_grid_values\n thickness = simulated_well.sum()\n pyro.deterministic(r'$\\mu_{thickness}$', thickness.detach())\n y_thickness = pyro.sample(r'$y_{thickness}$', dist.Normal(thickness, 50), obs=y_obs_list)" + "def model(y_obs_list):\n \"\"\"\n This Pyro model represents the probabilistic aspects of the geological model.\n It defines a prior distribution for the top layer's location and \n computes the thickness of the geological layer as an observed variable.\n \"\"\"\n # Define prior for the top layer's location\n prior_mean = sp_coords_copy[0, 2]\n mu_top = pyro.sample(r'$\\mu_{top}$', dist.Normal(prior_mean, torch.tensor(0.02, dtype=torch.float64)))\n\n # Update the model with the new top layer's location\n interpolation_input = geo_model.interpolation_input_copy\n interpolation_input.surface_points.sp_coords = torch.index_put(\n interpolation_input.surface_points.sp_coords,\n (torch.tensor([0]), torch.tensor([2])),\n mu_top\n )\n\n # Compute the geological model\n geo_model.solutions = gempy_engine.compute_model(\n interpolation_input=interpolation_input,\n options=geo_model.interpolation_options,\n data_descriptor=geo_model.input_data_descriptor,\n geophysics_input=geo_model.geophysics_input,\n )\n\n # Compute and observe the thickness of the geological layer\n simulated_well = geo_model.solutions.octrees_output[0].last_output_center.custom_grid_values\n thickness = simulated_well.sum()\n pyro.deterministic(r'$\\mu_{thickness}$', thickness.detach())\n y_thickness = pyro.sample(r'$y_{thickness}$', dist.Normal(thickness, 50), obs=y_obs_list)" ] }, { diff --git a/_downloads/e4b9d68eae49356aa287b7703ece0488/03_model_2_OMF_to_subsurface.ipynb b/_downloads/e4b9d68eae49356aa287b7703ece0488/03_model_2_OMF_to_subsurface.ipynb index 07013ae..479ca07 100644 --- a/_downloads/e4b9d68eae49356aa287b7703ece0488/03_model_2_OMF_to_subsurface.ipynb +++ b/_downloads/e4b9d68eae49356aa287b7703ece0488/03_model_2_OMF_to_subsurface.ipynb @@ -33,7 +33,7 @@ }, "outputs": [], "source": [ - "import omfvista\nimport pandas as pd\nimport pyvista\nfrom dotenv import dotenv_values\nimport subsurface\nfrom subsurface import TriSurf, LineSet\nfrom subsurface.visualization import to_pyvista_mesh, to_pyvista_line, init_plotter" + "import omfvista\nimport pandas as pd\nimport pyvista\nfrom dotenv import dotenv_values\nimport subsurface\nfrom subsurface import TriSurf, LineSet\nfrom subsurface.modules.visualization import to_pyvista_mesh, to_pyvista_line, init_plotter" ] }, { @@ -87,7 +87,7 @@ }, "outputs": [], "source": [ - "meshes = []\nlines = []\n\nfor block_index in range(omf_project.n_blocks):\n block_name = omf_project.get_block_name(block_index)\n polydata_obj: pyvista.PolyData = omf_project[block_name]\n\n # Skip if the polydata is not a mesh\n if not isinstance(polydata_obj, pyvista.PolyData):\n continue\n\n unstruct_pyvista: pyvista.UnstructuredGrid = polydata_obj.cast_to_unstructured_grid()\n cell_data = {name: unstruct_pyvista.cell_data[name] for name in unstruct_pyvista.cell_data}\n\n # Process based on cell type\n match polydata_obj.cell_type(0):\n case pyvista.CellType.TRIANGLE:\n # Process triangle mesh\n cells_pyvista = unstruct_pyvista.cells.reshape(-1, 4)[:, 1:]\n new_cell_data = {\"Formation_Major_\": block_index, **cell_data}\n unstruct = subsurface.UnstructuredData.from_array(\n vertex=unstruct_pyvista.points,\n cells=cells_pyvista,\n cells_attr=pd.DataFrame(new_cell_data)\n )\n ts = TriSurf(mesh=unstruct)\n meshes.append(to_pyvista_mesh(ts))\n\n case pyvista.CellType.LINE:\n # Process line data\n if \"Formation_Major\" not in cell_data.keys():\n continue\n cells_pyvista = unstruct_pyvista.cells.reshape(-1, 3)[:, 1:]\n unstruct = subsurface.UnstructuredData.from_array(\n vertex=unstruct_pyvista.points,\n cells=cells_pyvista,\n cells_attr=pd.DataFrame(cell_data)\n )\n line = LineSet(data=unstruct)\n lines.append(to_pyvista_line(line, radius=100, as_tube=True, spline=False))\n\n# Export to desired format here if necessary" + "meshes = []\nlines = []\n\nfor block_index in range(omf_project.n_blocks):\n block_name = omf_project.get_block_name(block_index)\n polydata_obj: pyvista.PolyData = omf_project[block_name]\n\n # Skip if the polydata is not a mesh\n if not isinstance(polydata_obj, pyvista.PolyData):\n continue\n\n unstruct_pyvista: pyvista.UnstructuredGrid = polydata_obj.cast_to_unstructured_grid()\n cell_data = {name: unstruct_pyvista.cell_data[name] for name in unstruct_pyvista.cell_data}\n\n # Process based on cell type\n match polydata_obj.get_cell(0).type:\n case pyvista.CellType.TRIANGLE:\n # Process triangle mesh\n cells_pyvista = unstruct_pyvista.cells.reshape(-1, 4)[:, 1:]\n new_cell_data = {\"Formation_Major_\": block_index, **cell_data}\n unstruct = subsurface.UnstructuredData.from_array(\n vertex=unstruct_pyvista.points,\n cells=cells_pyvista,\n cells_attr=pd.DataFrame(new_cell_data)\n )\n ts = TriSurf(mesh=unstruct)\n meshes.append(to_pyvista_mesh(ts))\n\n case pyvista.CellType.LINE:\n # Process line data\n if \"Formation_Major\" not in cell_data.keys():\n continue\n cells_pyvista = unstruct_pyvista.cells.reshape(-1, 3)[:, 1:]\n unstruct = subsurface.UnstructuredData.from_array(\n vertex=unstruct_pyvista.points,\n cells=cells_pyvista,\n cells_attr=pd.DataFrame(cell_data)\n )\n line = LineSet(data=unstruct)\n lines.append(to_pyvista_line(line, radius=100, as_tube=True, spline=False))\n\n# Export to desired format here if necessary" ] }, { diff --git a/_downloads/e98fb3cc90cb3c5ec53603f273bc17a8/02_model_1_gempy.py b/_downloads/e98fb3cc90cb3c5ec53603f273bc17a8/02_model_1_gempy.py index 4e155fc..15410fe 100644 --- a/_downloads/e98fb3cc90cb3c5ec53603f273bc17a8/02_model_1_gempy.py +++ b/_downloads/e98fb3cc90cb3c5ec53603f273bc17a8/02_model_1_gempy.py @@ -72,7 +72,7 @@ gp.modify_surface_points( geo_model, slice=0, - X=geo_model.surface_points.data[0][0] + 130, + X=geo_model.surface_points_copy.data[0][0] + 130, ) # %% diff --git a/_images/sphx_glr_01_gravity_gradiometry_inversion_pgi_006.png b/_images/sphx_glr_01_gravity_gradiometry_inversion_pgi_006.png index 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  • GeoPhysics and Inversion
  • @@ -333,7 +330,7 @@

    Quick search

    ©2023-2024, Vector Geology Team. | - Powered by Sphinx 7.1.1 + Powered by Sphinx 7.2.6 & Alabaster 0.7.13 | diff --git a/examples/02_structural_modeling/02_model_1_gempy.html b/examples/02_structural_modeling/02_model_1_gempy.html index 95bf216..10ab9f6 100644 --- a/examples/02_structural_modeling/02_model_1_gempy.html +++ b/examples/02_structural_modeling/02_model_1_gempy.html @@ -1,28 +1,28 @@ - + - Construct Model 1 with Helper Functions — Vector Geology 0.1.dev16+g525c9e2 documentation - + Construct Model 1 with Helper Functions — Vector Geology 0.3.dev0+gf79ba49.d20240612 documentation + - + - + - + - + @@ -46,10 +46,10 @@ to download the full example code

    -

    Construct Model 1 with Helper FunctionsΒΆ

    +

    Construct Model 1 with Helper FunctionsΒΆ

    This example demonstrates how to construct a 3D geological model of the Model 1 deposit using GemPy. It leverages custom APIs to streamline the modeling process.

    -
    import time
    +
    import time
     import os
     import xarray as xr
     from dotenv import dotenv_values
    @@ -60,30 +60,26 @@
     from vector_geology.omf_to_gempy import process_file
     
    -
    /home/leguark/subsurface/subsurface/reader/__init__.py:14: UserWarning: Welly or Striplog not installed. No well reader possible.
    -  warnings.warn("Welly or Striplog not installed. No well reader possible.")
    -
    -

    Start the timer to measure execution time

    -
    start_time = time.time()
    +
    start_time = time.time()
     

    Load environment variables and path configurations This step is crucial for setting up the directory paths for the model data.

    -
    config = dotenv_values()
    +
    config = dotenv_values()
     path = config.get("PATH_TO_MODEL_1_Subsurface")
     

    Initialize lists to store data Here we prepare the containers for the structural elements and the global extent.

    -
    structural_elements = []
    +
    structural_elements = []
     global_extent = None
     color_gen = gp.data.ColorsGenerator()
     

    Process each .nc file in the specified directory This loop reads the necessary data from each .nc file for modeling.

    -
    for filename in os.listdir(path):
    +
    for filename in os.listdir(path):
         base, ext = os.path.splitext(filename)
         if ext == '.nc':
             structural_element, global_extent = process_file(os.path.join(path, filename), global_extent, color_gen)
    @@ -92,24 +88,57 @@
     

    Initialize the GemPy model We use the data processed above to initialize the geological model with GemPy.

    -
    geo_model = initialize_geo_model(
    +
    geo_model = initialize_geo_model(
         structural_elements=structural_elements,
         extent=global_extent,
    -    topography=(xr.open_dataset(os.path.join(path, "Topography.nc"))),
    +    topography=(xr.open_dataset(os.path.join(path, "Topography.nc"))),
         load_nuggets=True
     )
     
    -
    Active grids: ['regular' 'topography']
    +
    /home/leguark/gempy/gempy/core/data/structural_frame.py:188: UserWarning: The basement color was already used in the structural elements.Changing the basement color to #015482.
    +  warnings.warn(f"The basement color was already used in the structural elements."
    +Active grids: GridTypes.NONE|TOPOGRAPHY|DENSE
     

    Display the initialized model It’s helpful to print the model to verify its initialization.

    -
    print(geo_model)
    +
    print(geo_model)
     
    -
    {'_interpolation_options': InterpolationOptions(kernel_options={'range': 5, 'c_o': 10, 'uni_degree': 1, 'i_res': 4, 'gi_res': 2, 'number_dimensions': 3, 'kernel_function': <AvailableKernelFunctions.cubic: KernelFunction(base_function=<function cubic_function at 0x7fa8cbe9bd90>, derivative_div_r=<function cubic_function_p_div_r at 0x7fa8cbe9be20>, second_derivative=<function cubic_function_a at 0x7fa8cbe9beb0>, consume_sq_distance=False)>, 'kernel_solver': <Solvers.DEFAULT: 1>, 'compute_condition_number': False, 'optimizing_condition_number': False, 'condition_number': None}, number_octree_levels=6, current_octree_level=0, compute_scalar_gradient=False, mesh_extraction=True, mesh_extraction_masking_options=MeshExtractionMaskingOptions.INTERSECT, mesh_extraction_fancy=True, debug=True, debug_water_tight=False, sigmoid_slope=50000, _number_octree_levels_surface=4, _model_name=None),
    - 'grid': <gempy.core.data.grid.Grid object at 0x7fa8762c0eb0>,
    +
    {'_interpolation_options': InterpolationOptions(kernel_options={'range': 5, 'c_o': 10, 'uni_degree': 1, 'i_res': 4, 'gi_res': 2, 'number_dimensions': 3, 'kernel_function': <AvailableKernelFunctions.cubic: KernelFunction(base_function=<function cubic_function at 0x7fea8a6cd480>, derivative_div_r=<function cubic_function_p_div_r at 0x7fea8a6cd510>, second_derivative=<function cubic_function_a at 0x7fea8a6cd5a0>, consume_sq_distance=False)>, 'kernel_solver': <Solvers.DEFAULT: 1>, 'compute_condition_number': False, 'optimizing_condition_number': False, 'condition_number': None}, evaluation_options={'_number_octree_levels': 5, '_number_octree_levels_surface': 4, 'curvature_threshold': -1, 'error_threshold': 1.5, 'min_octree_level': 2, 'mesh_extraction': True, 'mesh_extraction_masking_options': <MeshExtractionMaskingOptions.INTERSECT: 3>, 'mesh_extraction_fancy': True, 'evaluation_chunk_size': 5000000, 'compute_scalar_gradient': False, 'verbose': False}, temp_interpolation_values=<gempy_engine.core.data.options.temp_interpolation_values.TempInterpolationValues object at 0x7fea2d6872e0>, debug=True, cache_mode=CacheMode.IN_MEMORY_CACHE, cache_model_name=, block_solutions_type=BlockSolutionType.OCTREE, sigmoid_slope=50000, debug_water_tight=False),
    + 'grid': Grid(_octree_grid=None,
    +              _dense_grid=RegularGrid(resolution=array([20, 10, 20]),
    +                                      extent=array([ 5.59902830e+05,  5.64955682e+05,  6.44278260e+05,  6.50608735e+05,
    +       -1.75340000e+03,  1.60326604e+02]),
    +                                      values=array([[ 5.60029151e+05,  6.44594784e+05, -1.70555683e+03],
    +       [ 5.60029151e+05,  6.44594784e+05, -1.60987050e+03],
    +       [ 5.60029151e+05,  6.44594784e+05, -1.51418417e+03],
    +       ...,
    +       [ 5.64829361e+05,  6.50292212e+05, -7.88892213e+01],
    +       [ 5.64829361e+05,  6.50292212e+05,  1.67971089e+01],
    +       [ 5.64829361e+05,  6.50292212e+05,  1.12483439e+02]]),
    +                                      mask_topo=array([], shape=(0, 3), dtype=bool),
    +                                      _transform=None),
    +              _custom_grid=None,
    +              _topography=<gempy.core.data.grid_modules.topography.Topography object at 0x7fea789b5600>,
    +              _sections=None,
    +              _centered_grid=None,
    +              values=array([[ 5.60029151e+05,  6.44594784e+05, -1.70555683e+03],
    +       [ 5.60029151e+05,  6.44594784e+05, -1.60987050e+03],
    +       [ 5.60029151e+05,  6.44594784e+05, -1.51418417e+03],
    +       ...,
    +       [ 5.64955682e+05,  6.49201963e+05,  1.06993262e+02],
    +       [ 5.64955682e+05,  6.49905349e+05,  8.89864414e+01],
    +       [ 5.64955682e+05,  6.50608735e+05,  7.05702308e+01]]),
    +              length=array([], dtype=float64),
    +              _transform=None,
    +              _octree_levels=-1),
    + 'input_transform': {'_cached_pivot': None,
    + '_is_default_transform': False,
    + 'position': array([-562422.65655014, -647329.31794742,     823.58712106]),
    + 'rotation': array([0., 0., 0.]),
    + 'scale': array([8.55363115e-05, 8.55363115e-05, 8.55363115e-05])},
      'meta': GeoModelMeta(name='Tutorial_ch1_1_Basics',
                           creation_date=None,
                           last_modification_date=None,
    @@ -157,16 +186,12 @@
             fault_relations=
     [[False, False],
      [False, False]],
    -,
    - 'transform': {'_is_default_transform': False,
    - 'position': array([-562422.65655014, -647329.31794742,     823.58712106]),
    - 'rotation': array([0., 0., 0.]),
    - 'scale': array([8.55363115e-05, 8.55363115e-05, 8.55363115e-05])}}
    +}
     

    Modify the interpolation options This step configures the model’s interpolation settings for better accuracy.

    -
    interpolation_options = geo_model.interpolation_options
    +
    interpolation_options = geo_model.interpolation_options
     interpolation_options.mesh_extraction = True
     interpolation_options.kernel_options.range = 0.7
     interpolation_options.kernel_options.c_o = 3
    @@ -175,10 +200,10 @@
     

    Modify surface points Adjustments to the surface points can influence the resulting geological features.

    -
    gp.modify_surface_points(
    +
    gp.modify_surface_points(
         geo_model,
         slice=0,
    -    X=geo_model.surface_points.data[0][0] + 130,
    +    X=geo_model.surface_points_copy.data[0][0] + 130,
     )
     
    @@ -240,7 +265,7 @@

    Compute the model This is where the model computation happens, utilizing the GemPy engine.

    -
    before_compute_time = time.time()
    +
    before_compute_time = time.time()
     gp.compute_model(
         geo_model,
         engine_config=gp.data.GemPyEngineConfig(
    @@ -251,27 +276,25 @@
     
    Setting Backend To: AvailableBackends.PYTORCH
    -/home/leguark/gempy/gempy/core/data/geo_model.py:183: UserWarning: You are using refinement and passing a regular grid. The resolution of the regular grid will be overwritten
    -  warnings.warn(
    -Final Iteration 230, Residual Norm: 3.6824900462306687e-08
    -Final Iteration 102, Residual Norm: 8.902938475330302e-07
    +Condition number: 146825.828125.
    +Condition number: 276737.5.
     
    -Solutions: 6 Octree Levels, 5 DualContouringMeshes +Solutions: 5 Octree Levels, 5 DualContouringMeshes


    Plot 2D model visualization Visualizing the model in 2D helps in understanding the geological layers.

    -
    gpv.plot_2d(geo_model, show_scalar=False)
    +
    gpv.plot_2d(geo_model, show_scalar=False)
     
    -Cell Number: mid Direction: y
    <gempy_viewer.modules.plot_2d.visualization_2d.Plot2D object at 0x7fa87752c970>
    +Cell Number: mid Direction: y
    <gempy_viewer.modules.plot_2d.visualization_2d.Plot2D object at 0x7fea2d483790>
     

    3D visualization with gempy_viewer A 3D view provides a comprehensive look at the model’s structure.

    -
    gempy_vista = gpv.plot_3d(
    +
    gempy_vista = gpv.plot_3d(
         model=geo_model,
         show=True,
         kwargs_plot_structured_grid={'opacity': 0.8}
    @@ -280,7 +303,7 @@
     
    02 model 1 gempy

    Calculate and print execution times It’s useful to know how long different parts of the process take.

    -
    end_time = time.time()
    +
    end_time = time.time()
     prep_time = before_compute_time - start_time
     compute_time = end_time - before_compute_time
     execution_time = end_time - start_time
    @@ -292,19 +315,19 @@
     # sphinx_gallery_thumbnail_number = -2
     
    -
    Preparation time: 1.1760990619659424 seconds.
    -Computation time: 2.8446145057678223 seconds.
    -Total execution time: 4.020713567733765 seconds.
    +
    Preparation time: 1.0133297443389893 seconds.
    +Computation time: 4.729570627212524 seconds.
    +Total execution time: 5.742900371551514 seconds.
     
    -

    Total running time of the script: ( 0 minutes 4.193 seconds)

    +

    Total running time of the script: (0 minutes 5.802 seconds)

    Gallery generated by Sphinx-Gallery

    @@ -341,6 +364,8 @@

    Navigation

  • Setting Up GemPy Model
  • Optimizing Nuggets
  • Construct Model 1 with Helper Functions
  • +
  • Construct Spremberg: Importing borehole data
  • +
  • Construct Spremberg: Building initial model
  • GeoPhysics and Inversion
  • @@ -371,7 +396,7 @@

    Quick search

    ©2023-2024, Vector Geology Team. | - Powered by Sphinx 7.1.1 + Powered by Sphinx 7.2.6 & Alabaster 0.7.13 | diff --git a/examples/02_structural_modeling/03_model_spremberg_import.html b/examples/02_structural_modeling/03_model_spremberg_import.html new file mode 100644 index 0000000..4297b44 --- /dev/null +++ b/examples/02_structural_modeling/03_model_spremberg_import.html @@ -0,0 +1,380 @@ + + + + + + + + Construct Spremberg: Importing borehole data — Vector Geology 0.3.dev0+gf79ba49.d20240612 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + + +
    +

    Construct Spremberg: Importing borehole dataΒΆ

    +

    This example demonstrates how to construct a 3D geological model of the Model 1 deposit using GemPy. +It leverages custom APIs to streamline the modeling process.

    +

    Import the necessary libraries for geological modeling and visualization. +sphinx_gallery_thumbnail_number = -1

    +
    import os
    +import pandas as pd
    +import pyvista
    +
    +import gempy as gp
    +import gempy_viewer as gpv
    +from subsurface.core.geological_formats.boreholes.boreholes import BoreholeSet, MergeOptions
    +from subsurface.core.geological_formats.boreholes.collars import Collars
    +from subsurface.core.geological_formats.boreholes.survey import Survey
    +from subsurface.core.reader_helpers.readers_data import GenericReaderFilesHelper
    +from subsurface.modules.reader.wells.read_borehole_interface import read_lith, read_survey, read_collar
    +from subsurface.modules.visualization import to_pyvista_line, to_pyvista_points, init_plotter
    +
    +
    +

    Initialize the reader for the lithological data. Specify the file path and column mappings.

    +
    import dotenv
    +dotenv.load_dotenv()
    +reader: GenericReaderFilesHelper = GenericReaderFilesHelper(
    +    file_or_buffer=os.getenv("PATH_TO_SPREMBERG_STRATIGRAPHY"),
    +    columns_map={
    +            'hole_id'   : 'id',
    +            'depth_from': 'top',
    +            'depth_to'  : 'base',
    +            'lit_code'  : 'component lith'
    +    }
    +)
    +
    +# Read the lithological data into a DataFrame.
    +lith: pd.DataFrame = read_lith(reader)
    +
    +
    +
    /home/leguark/subsurface/subsurface/modules/reader/wells/read_borehole_interface.py:116: SettingWithCopyWarning:
    +A value is trying to be set on a copy of a slice from a DataFrame.
    +Try using .loc[row_indexer,col_indexer] = value instead
    +
    +See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
    +  lith_df['top'] = -np.abs(lith_df['top'])
    +/home/leguark/subsurface/subsurface/modules/reader/wells/read_borehole_interface.py:117: SettingWithCopyWarning:
    +A value is trying to be set on a copy of a slice from a DataFrame.
    +Try using .loc[row_indexer,col_indexer] = value instead
    +
    +See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
    +  lith_df['base'] = -np.abs(lith_df['base'])
    +
    +
    +

    Initialize the reader for the survey data. Specify the file path and column mappings.

    +
    reader: GenericReaderFilesHelper = GenericReaderFilesHelper(
    +    file_or_buffer=os.getenv("PATH_TO_SPREMBERG_SURVEY"),
    +    columns_map={
    +            'depth'  : 'md',
    +            'dip'    : 'dip',
    +            'azimuth': 'azi'
    +    },
    +)
    +
    +# Read the survey data into a DataFrame.
    +df = read_survey(reader)
    +
    +
    +
    /home/leguark/subsurface/subsurface/modules/reader/wells/read_borehole_interface.py:80: UserWarning: inc and/or azi columns are not present in the file. The boreholes will be straight.
    +  warnings.warn(
    +
    +
    +

    Create a Survey object from the DataFrame and update it with lithological data.

    +
    survey: Survey = Survey.from_df(df)
    +survey.update_survey_with_lith(lith)
    +
    +
    +
    Well ID 6E_58 not found in survey trajectory. Skipping lithology assignment.
    +Well ID 6E_58 not found in survey trajectory. Skipping lithology assignment.
    +Well ID 6E_58 not found in survey trajectory. Skipping lithology assignment.
    +('cell',) are not coordinates with an index xarray dataset must include 'cell' key (KeyError) or xarray 'cell' has no index (ValueError).
    +
    +
    +

    Initialize the reader for the collar data. Specify the file path, header, and column mappings.

    +
    reader_collar: GenericReaderFilesHelper = GenericReaderFilesHelper(
    +    file_or_buffer=os.getenv("PATH_TO_SPREMBERG_COLLAR"),
    +    header=0,
    +    usecols=[0, 1, 2, 4],
    +    columns_map={
    +            "hole_id"            : "id",
    +            "X_GK5_incl_inserted": "x",
    +            "Y__incl_inserted"   : "y",
    +            "Z_GK"               : "z"
    +    }
    +)
    +
    +# Read the collar data into a DataFrame and create a Collars object.
    +df_collar = read_collar(reader_collar)
    +collar = Collars.from_df(df_collar)
    +
    +
    +

    Combine the collar and survey data into a BoreholeSet.

    +
    borehole_set = BoreholeSet(
    +    collars=collar,
    +    survey=survey,
    +    merge_option=MergeOptions.INTERSECT
    +)
    +
    +
    +

    Visualize the borehole trajectories and collars using PyVista.

    +
    well_mesh = to_pyvista_line(
    +    line_set=borehole_set.combined_trajectory,
    +    active_scalar="lith_ids",
    +    radius=10
    +)
    +
    +collars = to_pyvista_points(
    +    borehole_set.collars.collar_loc,
    +)
    +
    +# Initialize the PyVista plotter.
    +pyvista_plotter = init_plotter()
    +
    +# Define the units limit for thresholding the well mesh.
    +units_limit = [0, 20]
    +
    +# Add the well mesh and collars to the plotter and display.
    +pyvista_plotter.add_mesh(
    +    well_mesh.threshold(units_limit),
    +    cmap="tab20c",
    +    clim=units_limit
    +)
    +
    +pyvista_plotter.add_mesh(
    +    collars,
    +    point_size=10,
    +    render_points_as_spheres=True
    +)
    +
    +pyvista_plotter.show()
    +
    +
    +03 model spremberg import

    Create structural elements from the borehole set for different lithological units.

    +
    elements: list[gp.data.StructuralElement] = gp.structural_elements_from_borehole_set(
    +    borehole_set=borehole_set,
    +    elements_dict={
    +            "Buntsandstein"       : {
    +                    "id"   : 53_300,
    +                    "color": "#983999"
    +            },
    +            "Werra-Anhydrit"      : {
    +                    "id"   : 61_730,
    +                    "color": "#00923f"
    +            },
    +            "Kupfershiefer"       : {
    +                    "id"   : 61_760,
    +                    "color": "#da251d"
    +            },
    +            "Zechsteinkonglomerat": {
    +                    "id"   : 61_770,
    +                    "color": "#f8c300"
    +            },
    +            "Rotliegend"          : {
    +                    "id"   : 62_000,
    +                    "color": "#bb825b"
    +            }
    +    }
    +)
    +
    +
    +

    Group the structural elements into a StructuralGroup and create a StructuralFrame.

    +
    group = gp.data.StructuralGroup(
    +    name="Stratigraphic Pile",
    +    elements=elements,
    +    structural_relation=gp.data.StackRelationType.ERODE
    +)
    +structural_frame = gp.data.StructuralFrame(
    +    structural_groups=[group],
    +    color_gen=gp.data.ColorsGenerator()
    +)
    +
    +
    +

    Determine the extent of the geological model from the surface points coordinates.

    +
    all_surface_points_coords: gp.data.SurfacePointsTable = structural_frame.surface_points_copy
    +extent_from_data = all_surface_points_coords.xyz.min(axis=0), all_surface_points_coords.xyz.max(axis=0)
    +
    +
    +

    Create a GeoModel with the specified extent, grid resolution, and interpolation options.

    +
    geo_model = gp.data.GeoModel(
    +    name="Stratigraphic Pile",
    +    structural_frame=structural_frame,
    +    grid=gp.data.Grid(
    +        extent=[extent_from_data[0][0], extent_from_data[1][0], extent_from_data[0][1], extent_from_data[1][1], extent_from_data[0][2], extent_from_data[1][2]],
    +        resolution=(50, 50, 50)
    +    ),
    +    interpolation_options=gp.data.InterpolationOptions(
    +        range=5,
    +        c_o=10,
    +        mesh_extraction=True,
    +        number_octree_levels=3,
    +    ),
    +)
    +
    +
    +

    Visualize the 3D geological model using GemPy’s plot_3d function.

    +
    gempy_plot = gpv.plot_3d(
    +    model=geo_model,
    +    kwargs_pyvista_bounds={
    +            'show_xlabels': False,
    +            'show_ylabels': False,
    +    },
    +    show=True,
    +    image=True
    +)
    +
    +
    +03 model spremberg import03 model spremberg import

    Combine all visual elements and display them together.

    +
    sp_mesh: pyvista.PolyData = gempy_plot.surface_points_mesh
    +
    +pyvista_plotter = init_plotter()
    +pyvista_plotter.show_bounds(all_edges=True)
    +
    +pyvista_plotter.add_mesh(
    +    well_mesh.threshold(units_limit),
    +    cmap="tab20c",
    +    clim=units_limit
    +)
    +
    +pyvista_plotter.add_mesh(
    +    collars,
    +    point_size=10,
    +    render_points_as_spheres=True
    +)
    +
    +pyvista_plotter.add_point_labels(
    +    points=collar.collar_loc.points,
    +    labels=collar.ids,
    +    point_size=10,
    +    shape_opacity=0.5,
    +    font_size=12,
    +    bold=True
    +)
    +pyvista_plotter.add_actor(gempy_plot.surface_points_actor)
    +
    +pyvista_plotter.show()
    +
    +
    +03 model spremberg import

    Total running time of the script: (0 minutes 2.784 seconds)

    + +

    Gallery generated by Sphinx-Gallery

    +
    + + +
    + +
    +
    + +
    +
    + + + + + + + \ No newline at end of file diff --git a/examples/02_structural_modeling/04_model_spremberg_building.html b/examples/02_structural_modeling/04_model_spremberg_building.html new file mode 100644 index 0000000..af4048b --- /dev/null +++ b/examples/02_structural_modeling/04_model_spremberg_building.html @@ -0,0 +1,425 @@ + + + + + + + + Construct Spremberg: Building initial model — Vector Geology 0.3.dev0+gf79ba49.d20240612 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + + +
    +

    Construct Spremberg: Building initial modelΒΆ

    +

    This example demonstrates…

    +
    # sphinx_gallery_thumbnail_number = -2
    +import numpy as np
    +
    +import gempy as gp
    +import gempy_viewer as gpv
    +from subsurface.modules.visualization import to_pyvista_line, to_pyvista_points
    +from vector_geology.model_contructor.spremberg import generate_spremberg_model, get_spremberg_borehole_set, add_wells_plot
    +
    +
    +
    elements_to_gempy = {
    +        "Buntsandstein"       : {
    +                "id"   : 53_300,
    +                "color": "#983999"
    +        },
    +        "Werra-Anhydrit"      : {
    +                "id"   : 61_730,
    +                "color": "#00923f"
    +        },
    +        # "Kupferschiefer"      : {
    +        #         "id"   : 61_760,
    +        #         "color": "#da251d"
    +        # },
    +        "Zechsteinkonglomerat": {
    +                "id"   : 61_770,
    +                "color": "#f8c300"
    +        },
    +        "Rotliegend"          : {
    +                "id"   : 62_000,
    +                "color": "#bb825b"
    +        }
    +}
    +
    +spremberg_boreholes = get_spremberg_borehole_set()
    +geo_model: gp.data.GeoModel = generate_spremberg_model(
    +    borehole_set=spremberg_boreholes,
    +    elements_to_gempy=elements_to_gempy,
    +    plot=False
    +)
    +
    +
    +04 model spremberg building04 model spremberg building
    /home/leguark/subsurface/subsurface/modules/reader/wells/read_borehole_interface.py:116: SettingWithCopyWarning:
    +A value is trying to be set on a copy of a slice from a DataFrame.
    +Try using .loc[row_indexer,col_indexer] = value instead
    +
    +See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
    +  lith_df['top'] = -np.abs(lith_df['top'])
    +/home/leguark/subsurface/subsurface/modules/reader/wells/read_borehole_interface.py:117: SettingWithCopyWarning:
    +A value is trying to be set on a copy of a slice from a DataFrame.
    +Try using .loc[row_indexer,col_indexer] = value instead
    +
    +See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
    +  lith_df['base'] = -np.abs(lith_df['base'])
    +/home/leguark/subsurface/subsurface/modules/reader/wells/read_borehole_interface.py:80: UserWarning: inc and/or azi columns are not present in the file. The boreholes will be straight.
    +  warnings.warn(
    +Well ID 6E_58 not found in survey trajectory. Skipping lithology assignment.
    +Well ID 6E_58 not found in survey trajectory. Skipping lithology assignment.
    +Well ID 6E_58 not found in survey trajectory. Skipping lithology assignment.
    +('cell',) are not coordinates with an index xarray dataset must include 'cell' key (KeyError) or xarray 'cell' has no index (ValueError).
    +
    +
    +

    Add one orientation to the model

    +
    rotliegend: gp.data.StructuralElement = geo_model.structural_frame.get_element_by_name("Rotliegend")
    +gp.add_orientations(
    +    geo_model=geo_model,
    +    x=[5_460_077.527386775, 5_450_077.527386775],
    +    y=[5_720_030.2446156405, 5_710_030.2446156405],
    +    z=[-600, -600],
    +    elements_names=["Rotliegend", "Rotliegend"],
    +    pole_vector=[
    +            np.array([.7, 0.7, 0.2]),
    +            np.array([-1, 0, 0.2])
    +    ],
    +)
    +
    +
    +
    + + + + + +
    Structural Groups: + StructuralGroup: + + + +
    Name:Stratigraphic Pile
    Structural Relation:StackRelationType.ERODE
    Elements: + + + +
    StructuralElement:
    Name:Buntsandstein
    +
    + + + +
    StructuralElement:
    Name:Werra-Anhydrit
    +
    + + + +
    StructuralElement:
    Name:Zechsteinkonglomerat
    +
    + + + +
    StructuralElement:
    Name:Rotliegend
    +
    +
    Fault Relations:
    Stratigrap...
    Stratigraphic Pile
    + + + + + +
    True
    False
    +
    + +
    +
    +
    pivot = [5_478_256.5, 5_698_528.946534388]
    +point_2 = [5_483_077.527386775, 5_710_030.2446156405]
    +point_3 = [5_474_977.5974836275, 5_712_059.373443342]
    +section_dict = {
    +        'section1': (pivot, point_2, [100, 100]),
    +        'section2': (pivot, point_3, [100, 100]),
    +        'section3': (point_2, point_3, [100, 100])
    +}
    +
    +
    +
    gp.set_section_grid(geo_model.grid, section_dict)
    +gpv.plot_section_traces(geo_model)
    +
    +
    +Cell Number: -1 Direction: z
    Active grids: GridTypes.NONE|SECTIONS|OCTREE
    +
    +<function plot_section_traces at 0x7f14ae14f760>
    +
    +
    +
    _ = gpv.plot_3d(
    +    model=geo_model,
    +    ve=10,
    +    image=True,
    +    transformed_data=True,
    +    kwargs_pyvista_bounds={
    +            'show_xlabels': False,
    +            'show_ylabels': False,
    +    },
    +    kwargs_plot_data={
    +            'arrow_size': 0.001000
    +    }
    +)
    +
    +
    +04 model spremberg building04 model spremberg building
    # * Ignore curvature for now
    +geo_model.interpolation_options.kernel_options.range = 3
    +geo_model.interpolation_options.compute_scalar_gradient = False
    +geo_model.interpolation_options.evaluation_options.curvature_threshold = 0.4
    +geo_model.interpolation_options.evaluation_options.number_octree_levels_surface = 5
    +
    +geo_model.interpolation_options.evaluation_options.error_threshold = 1
    +geo_model.interpolation_options.evaluation_options.verbose = True
    +
    +
    +gp.compute_model(
    +    gempy_model=geo_model,
    +    engine_config=gp.data.GemPyEngineConfig(
    +        backend=gp.data.AvailableBackends.PYTORCH,
    +        dtype="float64",
    +    )
    +)
    +
    +
    +
      +
    • Voxel Scalar Values with Refinement Status
    • +
    • Voxel Scalar Values with Refinement Status
    • +
    +
    Setting Backend To: AvailableBackends.PYTORCH
    +Chunking done: 17 chunks
    +Number of voxels marked by stats: 407 of torch.Size([512]).
    + Number of voxels marked by corners : 245
    +Total voxels: 407
    +Dense Grid would be 512 voxels
    +Chunking done: 15 chunks
    +Number of voxels marked by stats: 1677 of torch.Size([3472]).
    + Number of voxels marked by corners : 1308
    +Total voxels: 1677
    +Dense Grid would be 4096 voxels
    +Chunking done: 8 chunks
    +Chunking done: 63 chunks
    +Chunking done: 26 chunks
    +
    +
    +
    +Solutions: 5 Octree Levels, 4 DualContouringMeshes +
    +
    +
    gpv.plot_2d(
    +    model=geo_model,
    +    figsize=(15, 15),
    +    ve=10,
    +    section_names=['section1', 'section2', 'section3']
    +)
    +
    +
    +section1, section2, section3
    /home/leguark/gempy_viewer/gempy_viewer/API/_plot_2d_sections_api.py:105: UserWarning: Section contacts not implemented yet. We need to pass scalar field for the sections grid
    +  warnings.warn(
    +
    +<gempy_viewer.modules.plot_2d.visualization_2d.Plot2D object at 0x7f14501aab60>
    +
    +
    +
    gempy_plot = gpv.plot_3d(
    +    model=geo_model,
    +    show_lith=False,
    +    ve=10,
    +    show=False,
    +    image=False,
    +    transformed_data=False,
    +    kwargs_pyvista_bounds={
    +            'show_xlabels': True,
    +            'show_ylabels': True,
    +            'show_zlabels': False,
    +    },
    +    kwargs_plot_data={
    +            'arrow_size': 100.001000
    +    }
    +)
    +
    +well_mesh = to_pyvista_line(
    +    line_set=spremberg_boreholes.combined_trajectory,
    +    active_scalar="lith_ids",
    +    radius=10
    +)
    +units_limit = [0, 20]
    +collars = to_pyvista_points(spremberg_boreholes.collars.collar_loc)
    +gempy_plot.p.add_mesh(
    +    well_mesh.threshold(units_limit),
    +    cmap="tab20c",
    +    clim=units_limit
    +)
    +
    +gempy_plot.p.add_mesh(
    +    collars,
    +    point_size=10,
    +    render_points_as_spheres=True
    +)
    +
    +gempy_plot.p.add_point_labels(
    +    points=spremberg_boreholes.collars.collar_loc.points,
    +    labels=spremberg_boreholes.collars.ids,
    +    point_size=3,
    +    shape_opacity=0.5,
    +    font_size=12,
    +    bold=True
    +)
    +
    +gempy_plot.p.show()
    +
    +
    +04 model spremberg building
    +

    LiquidEarth IntegrationΒΆ

    +

    Beyond the classical plotting capabilities introduced in GemPy v3, users can now also upload models to LiquidEarth. +LiquidEarth is a collaborative platform designed for 3D visualization, +developed by many of the main gempy maintainers, with a strong focus on collaboration and sharing. +This makes it an excellent tool for sharing your models with others and viewing them across different platforms. +To upload a model to LiquidEarth, you must have an account and a user token. Once your model is uploaded, +you can easily share the link with anyone.

    +
    # # %%
    +if True:
    +    link = gpv.plot_to_liquid_earth(
    +        geo_model=geo_model,
    +        space_name="Spremberg",
    +        file_name="gempy_model",
    +        user_token=None,  # If None, it will try to grab it from the environment
    +        grab_link=True,
    +    )
    +
    +    print(f"Generated Link: {link}")
    +
    +
    +
    Generated Link: https://liquidearth.app.link/4a7b5726-6ccd-4453-94d7-a9bbe72152a8
    +
    +
    +

    Total running time of the script: (0 minutes 52.865 seconds)

    + +

    Gallery generated by Sphinx-Gallery

    +
    +
    + + +
    + +
    +
    + +
    +
    + + + + + + + \ No newline at end of file diff --git a/examples/02_structural_modeling/index.html b/examples/02_structural_modeling/index.html index 4d9c573..9e18998 100644 --- a/examples/02_structural_modeling/index.html +++ b/examples/02_structural_modeling/index.html @@ -1,23 +1,23 @@ - + - Structural Modeling — Vector Geology 0.1.dev16+g525c9e2 documentation - + Structural Modeling — Vector Geology 0.3.dev0+gf79ba49.d20240612 documentation + - + - + - + @@ -41,7 +41,7 @@
    -

    Structural ModelingΒΆ

    +

    Structural ModelingΒΆ

    Delve into the world of 3D subsurface modeling with our Structural Modeling tutorials. In this section, we focus on harnessing the capabilities of gempy, a powerful tool developed by a partner within the Vector consortium. Here’s what these tutorials offer:

    • 3D Subsurface Modeling: Learn the intricacies of constructing detailed 3D geological models. We guide you through the process of transforming raw geophysical data into a comprehensive three-dimensional representation of the subsurface.

    • @@ -55,6 +55,12 @@

    Construct Model 1 with Helper Functions

    Construct Model 1 with Helper Functions
    +
    +

    Construct Spremberg: Importing borehole data

    +
    Construct Spremberg: Importing borehole data
    +
    +

    Construct Spremberg: Building initial model

    +
    Construct Spremberg: Building initial model
    @@ -91,6 +97,8 @@

    Navigation

  • Setting Up GemPy Model
  • Optimizing Nuggets
  • Construct Model 1 with Helper Functions
  • +
  • Construct Spremberg: Importing borehole data
  • +
  • Construct Spremberg: Building initial model
  • GeoPhysics and Inversion
  • @@ -121,7 +129,7 @@

    Quick search

    ©2023-2024, Vector Geology Team. | - Powered by Sphinx 7.1.1 + Powered by Sphinx 7.2.6 & Alabaster 0.7.13 | diff --git a/examples/02_structural_modeling/sg_execution_times.html b/examples/02_structural_modeling/sg_execution_times.html index 2c1ddc7..69ee75a 100644 --- a/examples/02_structural_modeling/sg_execution_times.html +++ b/examples/02_structural_modeling/sg_execution_times.html @@ -1,23 +1,23 @@ - + - Computation times — Vector Geology 0.1.dev16+g525c9e2 documentation - + Computation times — Vector Geology 0.3.dev0+gf79ba49.d20240612 documentation + - + - + - + @@ -39,20 +39,47 @@
    -

    Computation timesΒΆ

    -

    00:03.922 total execution time for examples_02_structural_modeling files:

    - +

    Computation timesΒΆ

    +

    00:52.865 total execution time for 4 files from examples/02_structural_modeling:

    +
    + + + + +
    + + + + + + + + + + - - + + - + + + + +

    Example

    Time

    Mem (MB)

    Construct Spremberg: Building initial model (04_model_spremberg_building.py)

    00:52.865

    0.0

    Constructing Structural Geological Model (01_model_1_gempy_step_by_step.py)

    00:03.922

    0.0 MB

    00:00.000

    0.0

    Construct Model 1 with Helper Functions (02_model_1_gempy.py)

    00:00.000

    0.0 MB

    0.0

    Construct Spremberg: Importing borehole data (03_model_spremberg_import.py)

    00:00.000

    0.0

    +
    @@ -111,7 +138,7 @@

    Quick search

    ©2023-2024, Vector Geology Team. | - Powered by Sphinx 7.1.1 + Powered by Sphinx 7.2.6 & Alabaster 0.7.13 | diff --git a/examples/03_forward_engines/01_gravity_gradiometry_inversion_pgi.html b/examples/03_forward_engines/01_gravity_gradiometry_inversion_pgi.html index e5a5ef5..040396e 100644 --- a/examples/03_forward_engines/01_gravity_gradiometry_inversion_pgi.html +++ b/examples/03_forward_engines/01_gravity_gradiometry_inversion_pgi.html @@ -1,23 +1,23 @@ - + - Inversion of Full Tensor Gravity Gradiometry Data — Vector Geology 0.1.dev16+g525c9e2 documentation - + Inversion of Full Tensor Gravity Gradiometry Data — Vector Geology 0.3.dev0+gf79ba49.d20240612 documentation + - + - + - + @@ -47,7 +47,7 @@ to download the full example code

    -

    Inversion of Full Tensor Gravity Gradiometry DataΒΆ

    +

    Inversion of Full Tensor Gravity Gradiometry DataΒΆ

    This example illustrates the inversion of sub-surface density models from Full-Tensor Gravity Gradiometry (FTG-G) data, measured over the Irish Midlands, using Petrophysically and Geologically guided Inversion (PGI). The key steps include:

      @@ -82,17 +82,17 @@ from vector_geology import SimpegHelper as SH # Plot beautification -formatter = mpl.ticker.ScalarFormatter(useMathText=True) -formatter.set_scientific(True) -formatter.set_powerlimits((0, 0)) +formatter = mpl.ticker.ScalarFormatter(useMathText=True) +formatter.set_scientific(True) +formatter.set_powerlimits((0, 0)) -mpl.rc("axes", titlesize=14, labelsize=12) -mpl.rc("xtick", labelsize=12) -mpl.rc("ytick", labelsize=12) +mpl.rc("axes", titlesize=14, labelsize=12) +mpl.rc("xtick", labelsize=12) +mpl.rc("ytick", labelsize=12)
      -

      Step 1: Create Directories for Storing Model Iterations and OutputsΒΆ

      +

      Step 1: Create Directories for Storing Model Iterations and OutputsΒΆ

      PGI is an iterative process, and it’s useful to save the model at every iteration. This helps in preventing overfitting and in adjusting the stopping criteria based on the model iterations.

      name = "Ireland_FTG"
      @@ -110,7 +110,7 @@ 

      Step 1: Create Directories for Storing Model Iterations and Outputs

      -

      Step 2: Load the FTG-G DataΒΆ

      +

      Step 2: Load the FTG-G DataΒΆ

      Full Tensor Gradiometry data, a 2-Tensor, contains five independent components due to its symmetric and traceless nature. These components are used for the inversion.

      \[\begin{split}G = \left[\begin{array}{cc} @@ -120,34 +120,34 @@

      Step 2: Load the FTG-G Data
      config = dotenv_values()
       file_path = config.get("PATH_TO_GRADIOMETRY")
      -FTG_Data = pd.read_csv(file_path, delimiter=",").to_numpy()
      +FTG_Data = pd.read_csv(file_path, delimiter=",").to_numpy()
       

      -

      Step 3: Resample Data onto a Regular GridΒΆ

      +

      Step 3: Resample Data onto a Regular GridΒΆ

      Resampling the data onto a regular grid aids in mesh generation and visualization. The SimpegHelper.pf_rs() function resamples potential field data onto a new regular grid.

      inc = 150  # New sampling interval (ground units)
      -grav_new, nx_new, ny_new = SH.pf_rs(
      -    FTG_Data,
      +grav_new, nx_new, ny_new = SH.pf_rs(
      +    FTG_Data,
           inc,
           bounds=[156000, 168000, 143000, 148500]
       )
       
       # NOTE: This step is necessary for real data since the convention followed by
       # the SimPEG forward operator is the opposite of the general convention
      -grav_vec = grav_new[:, 3:] * np.array([-1.0, -1.0, 1.0, 1.0, -1.0])[None, :]  # Adjusting sign convention
      -inv_topo = grav_new[:, [0, 1, 2]]  # Extracting topography
      +grav_vec = grav_new[:, 3:] * np.array([-1.0, -1.0, 1.0, 1.0, -1.0])[None, :]  # Adjusting sign convention
      +inv_topo = grav_new[:, [0, 1, 2]]  # Extracting topography
       
      -

      Step 4: Visualize the DataΒΆ

      +

      Step 4: Visualize the DataΒΆ

      The SimpegHelper.plot_2D_data() function is used to visualize the datasets with matplotlib.

      SH.plot_2D_data(
      -    data=np.c_[grav_new[:, [0, 1, 2]], grav_vec[:, 0]],
      -    clim=[np.nanmin(grav_vec[:, 0]), np.nanmax(grav_vec[:, 0])],
      +    data=np.c_[grav_new[:, [0, 1, 2]], grav_vec[:, 0]],
      +    clim=[np.nanmin(grav_vec[:, 0]), np.nanmax(grav_vec[:, 0])],
           cmap="jet",
           which_data="FTG",
           comp="xx",
      @@ -155,8 +155,8 @@ 

      Step 4: Visualize the Dataname=name ) SH.plot_2D_data( - data=np.c_[grav_new[:, [0, 1, 2]], inv_topo[:, -1]], - clim=[np.nanmin(inv_topo[:, -1]), np.nanmax(inv_topo[:, -1])], + data=np.c_[grav_new[:, [0, 1, 2]], inv_topo[:, -1]], + clim=[np.nanmin(inv_topo[:, -1]), np.nanmax(inv_topo[:, -1])], cmap="terrain", which_data="Topo", comp="xx", @@ -171,7 +171,7 @@

      Step 4: Visualize the Data -

      Step 5: Create a TensorMesh Object for InversionΒΆ

      +

      Step 5: Create a TensorMesh Object for InversionΒΆ

      A 3D TensorMesh is created using the discretize.TensorMesh utility to invert the gravity gradiometry data. The mesh is designed to have a constant cell size in the core region and expanding/contracting cells in the padding region.

      @@ -183,20 +183,20 @@

      Step 5: Create a TensorMesh Object for Inversionnz_core, nz_pad = 10, 18 # Number of cells in core and padding fact = 1.1 # Factor for expanding/contracting cells -inv_hx = dx * np.ones(nx_new) -inv_hy = dy * np.ones(ny_new) +inv_hx = dx * np.ones(nx_new) +inv_hy = dy * np.ones(ny_new) inv_hz = [(dz, nz_pad, -fact), (dz, nz_core), (dz, nz_pad, fact)] # Create the inverse tensor mesh inv_mesh = ds.TensorMesh( - h=[inv_hx, inv_hy, inv_hz], - x0=[np.min(inv_topo[:, 0]), np.min(inv_topo[:, 1]), "C"]) + h=[inv_hx, inv_hy, inv_hz], + x0=[np.min(inv_topo[:, 0]), np.min(inv_topo[:, 1]), "C"]) # Drape the topography over the mesh -actv = active_from_xyz(inv_mesh, inv_topo) -ndv = np.nan -actvMap = maps.InjectActiveCells(inv_mesh, actv, ndv) -nactv = int(actv.sum()) +actv = active_from_xyz(inv_mesh, inv_topo) +ndv = np.nan +actvMap = maps.InjectActiveCells(inv_mesh, actv, ndv) +nactv = int(actv.sum()) # Print mesh details print(inv_mesh) @@ -214,33 +214,33 @@

      Step 5: Create a TensorMesh Object for Inversion -

      Step 6: Visualize the MeshΒΆ

      +

      Step 6: Visualize the MeshΒΆ

      A slice of the Tensor Mesh object is visualized using the discretize.TensorMesh.plot_slice() utility.

      Create a background model.

      -
      bg = np.ones(nactv)
      -mod = actvMap * bg
      +
      bg = np.ones(nactv)
      +mod = actvMap * bg
       
       # Plot the mesh slice.
      -fig = plt.figure(figsize=(6, 6))
      -ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
      -cplot = inv_mesh.plot_slice(mod, grid=True, ax=ax, normal="Y", ind=22)
      -ax.set_title('Tensor Mesh Slice')
      -ax.set_xlabel('x (m)')
      -ax.set_ylabel('z (m)')
      -ax.set_xlim([158000, 159000])
      -ax.set_ylim([-500, 100])
      -ax.ticklabel_format(axis="both")
      -ax.set_aspect('equal')
      -plt.savefig(
      +fig = plt.figure(figsize=(6, 6))
      +ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
      +cplot = inv_mesh.plot_slice(mod, grid=True, ax=ax, normal="Y", ind=22)
      +ax.set_title('Tensor Mesh Slice')
      +ax.set_xlabel('x (m)')
      +ax.set_ylabel('z (m)')
      +ax.set_xlim([158000, 159000])
      +ax.set_ylim([-500, 100])
      +ax.ticklabel_format(axis="both")
      +ax.set_aspect('equal')
      +plt.savefig(
           path_to_output + "/" + name + "_TreeMeshSlice.pdf",
           bbox_inches="tight"
       )
      -plt.show()
      +plt.show()
       
      Tensor Mesh Slice
      -

      Step 7: Set up the Gravity Inverse ProblemΒΆ

      +

      Step 7: Set up the Gravity Inverse ProblemΒΆ

      The gravity inverse problem is set up using SimPEG’s pf.gravity module to generate a forward model based on the mesh and receiver locations.

      Create wires for the density model

      @@ -256,11 +256,11 @@

      Step 7: Set up the Gravity Inverse Problemgravity_components = ["gxx", "gyy", "gxz", "gyz", "gxy"] # Receiver locations (25 meters above the topography) -gravity_receiver_locations = inv_topo + 120. +gravity_receiver_locations = inv_topo + 120. # Create a gravity receiver list gravity_receivers = pf.gravity.receivers.Point( - locations=gravity_receiver_locations, + locations=gravity_receiver_locations, components=gravity_components ) gravity_receiver_list = [gravity_receivers] @@ -278,13 +278,13 @@

      Step 7: Set up the Gravity Inverse Probleminv_mesh, survey=gravity_survey, rhoMap=wires.density, - ind_active=actv + ind_active=actv ) # Create a gravity data object, with the relative errors and a standard noise floor gravity_data = data.Data( survey=gravity_survey, - dobs=grav_vec.flatten(), + dobs=grav_vec.flatten(), noise_floor=5, relative_error=0.1 ) @@ -298,7 +298,7 @@

      Step 7: Set up the Gravity Inverse Problem -

      Step 8: Setting up the Gaussian Mixture Model (GMM) PriorΒΆ

      +

      Step 8: Setting up the Gaussian Mixture Model (GMM) PriorΒΆ

      In PGI, the petrophysical data is used as a constraint in the form of a Gaussian Mixture Model (GMM). A GMM is a multimodal probabilistic distribution which is just a weighted sum of multiple gaussian distributions. Given the number of rock units (\(n\)), the petrophysical distribution can be displayed as an $n$-modal GMM. If you have no petrophysical information available, you can initialize the GMM as below. @@ -311,7 +311,7 @@

      Step 8: Setting up the Gaussian Mixture Model (GMM) Priorgmmref = utils.WeightedGaussianMixture( n_components=num_rock_units, mesh=inv_mesh, - actv=actv, + actv=actv, covariance_type="full", ) @@ -320,11 +320,11 @@

      Step 8: Setting up the Gaussian Mixture Model (GMM) Prior# Initialize the GMM fit with random samples, mesh size, # and number of physical properties -gmmref.fit(np.random.randn(nactv, num_physical_props)) +gmmref.fit(np.random.randn(nactv, num_physical_props)) # Set the mean values of physical property contrasts for each rock unit # One value (density) for each rock unit -gmmref.means_ = np.c_[ +gmmref.means_ = np.c_[ [-0.4], [-0.2], [0.0], @@ -339,7 +339,7 @@

      Step 8: Setting up the Gaussian Mixture Model (GMM) Prior# NOTE: Since we don't have petrophysical information for this example, # we keep the covariances # same for every unit. The GMM will update # itself during the inversion. -gmmref.covariances_ = np.array( +gmmref.covariances_ = np.array( [ [[density_variance]], [[density_variance]], @@ -354,26 +354,26 @@

      Step 8: Setting up the Gaussian Mixture Model (GMM) Prior# Set the weights for each rock unit # NOTE: This determines the size of each peak of the n-modal GMM -gmmref.weights_ = np.ones((nactv, 1)) * np.c_[0.125, 0.125, 0.5, 0.125, 0.125] +gmmref.weights_ = np.ones((nactv, 1)) * np.c_[0.125, 0.125, 0.5, 0.125, 0.125]

      -

      Plot the 1D-GMMΒΆ

      +

      Plot the 1D-GMMΒΆ

      The initial density distribution is visualized.

      -
      fig = plt.figure(figsize=(6, 6))
      -ax = gmmref.plot_pdf(flag2d=False, plotting_precision=1000, padding=0.2)
      -ax[0].set_xlabel(r"Density Contrast (g/cc)")
      -ax[0].set_ylabel(r"Probability Density Values")
      -ax[0].get_legend().remove()
      -ax[0].set_title(r"Initial DelRho Distribution")
      -ax[0].ticklabel_format(axis="both", style="scientific", scilimits=(0, 0))
      -ax[0].set_aspect(1 / 20)
      -plt.savefig(
      +
      fig = plt.figure(figsize=(6, 6))
      +ax = gmmref.plot_pdf(flag2d=False, plotting_precision=1000, padding=0.2)
      +ax[0].set_xlabel(r"Density Contrast (g/cc)")
      +ax[0].set_ylabel(r"Probability Density Values")
      +ax[0].get_legend().remove()
      +ax[0].set_title(r"Initial DelRho Distribution")
      +ax[0].ticklabel_format(axis="both", style="scientific", scilimits=(0, 0))
      +ax[0].set_aspect(1 / 20)
      +plt.savefig(
           path_to_output + "/" + name + "_Init_GMM.pdf",
           bbox_inches="tight"
       )
      -plt.show()
      +plt.show()
       
        @@ -382,7 +382,7 @@

        Plot the 1D-GMM -

        Step 9: Setting the Hyper-parameters and the Sensitivity WeightsΒΆ

        +

        Step 9: Setting the Hyper-parameters and the Sensitivity WeightsΒΆ

        Every PGI is a set of three Maximum-A-Posteriori (MAP) problems, being solved iteratively. The solver tries to minimize the L2 error of an objective function containing both the FTG Data and the petrophysical GMM. In this section we tune the necessary hyper-parameters, as well as initialise @@ -392,15 +392,15 @@

        Step 9: Setting the Hyper-parameters and the Sensitivity Weights
        initial_model = np.r_[background_density * np.ones(actvMap.nP)]
        +
        initial_model = np.r_[background_density * np.ones(actvMap.nP)]
         

        Sensitivity weighting Compute the sensitivity weights for each cell based on the gravity problem’s sensitivity matrix (G) This is done by computing the square root of the sum of the squared elements of G for each cell, and then normalizing by the cell volumes and the maximum weight value.

        -
        sensitivity_weights_gravity = np.sum(gravity_problem.G ** 2.0, axis=0) ** 0.5 / (inv_mesh.cell_volumes[actv])
        -sensitivity_weights_gravity = sensitivity_weights_gravity / np.nanmax(sensitivity_weights_gravity)
        +
        sensitivity_weights_gravity = np.sum(gravity_problem.G ** 2.0, axis=0) ** 0.5 / (inv_mesh.cell_volumes[actv])
        +sensitivity_weights_gravity = sensitivity_weights_gravity / np.nanmax(sensitivity_weights_gravity)
         
         # Regularization multipliers
         smallness_multiplier = 1e-4
        @@ -414,7 +414,7 @@ 

        Step 9: Setting the Hyper-parameters and the Sensitivity Weightsmesh=inv_mesh, wiresmap=wires, maplist=[identity_map], - active_cells=actv, + active_cells=actv, alpha_pgi=smallness_multiplier, alpha_x=smoothness_x_multiplier, alpha_y=smoothness_y_multiplier, @@ -424,13 +424,13 @@

        Step 9: Setting the Hyper-parameters and the Sensitivity Weightsalpha_xx=0.0, alpha_yy=0.0, alpha_zz=0.0, - weights_list=[sensitivity_weights_gravity] + weights_list=[sensitivity_weights_gravity] )

      -

      Step 10: Initialize the DirectivesΒΆ

      +

      Step 10: Initialize the DirectivesΒΆ

      Directives are set, including instructions on the bounds, update factors, and other hyperparameters for the inversion solver.

      Estimate smoothness multipliers

      alphas_directive = directives.AlphasSmoothEstimate_ByEig(verbose=True)
      @@ -477,12 +477,12 @@ 

      Step 10: Initialize the Directives) # Optimization options for the inversion -lower_bound = np.r_[-1.0 * np.ones(actvMap.nP)] -upper_bound = np.r_[1.0 * np.ones(actvMap.nP)] +lower_bound = np.r_[-1.0 * np.ones(actvMap.nP)] +upper_bound = np.r_[1.0 * np.ones(actvMap.nP)] optimizer = optimization.ProjectedGNCG( maxIter=20, - lower=lower_bound, - upper=upper_bound, + lower=lower_bound, + upper=upper_bound, maxIterLS=20, maxIterCG=100, tolCG=1e-4, @@ -508,9 +508,9 @@

      Step 10: Initialize the Directives -

      Step 11: Run the Inversion!ΒΆ

      +

      Step 11: Run the Inversion!ΒΆ

      The inversion is executed using the initialized settings.

      -
      inverted_model = inversion_algo.run(initial_model)
      +
      inverted_model = inversion_algo.run(initial_model)
       
      SimPEG.InvProblem will set Regularization.reference_model to m0.
      @@ -519,93 +519,93 @@ 

      Step 11: Run the Inversion! -

      Step 12: Visualize the 3D ModelΒΆ

      +

      Step 12: Visualize the 3D ModelΒΆ

      The inverted 3D model is visualized using slices in different orientations.

      set = 1
       save_plots = True
      @@ -618,16 +618,16 @@ 

      Step 12: Visualize the 3D Modelind_plot = [ind_plot_x, ind_plot_y, ind_plot_z] # Extract the results -inverted_density_model = density_map * inverted_model -quasi_geology_model = actvMap * regularization_term.objfcts[0].compute_quasi_geology_model() +inverted_density_model = density_map * inverted_model +quasi_geology_model = actvMap * regularization_term.objfcts[0].compute_quasi_geology_model() # Plot Density Contrast Model (Z) normal = "Z" -model_to_plot = inverted_density_model +model_to_plot = inverted_density_model SH.plot_model_slice( mesh=inv_mesh, - ind_active=actv, - model=model_to_plot, + ind_active=actv, + model=model_to_plot, normal= normal, ind_plot_arr=ind_plot, clim=[-1.0, 1.0], @@ -642,13 +642,13 @@

      Step 12: Visualize the 3D Model# Plot Inverted Model Slices (Y) normal = "X" -model_to_plot = inverted_density_model -SH.plot_model_slice(inv_mesh, actv, model_to_plot, normal, ind_plot, [-1.0, 1.0], set, sec_loc=True, gdlines=True, which_prop="Den", cmap="Spectral", save_plt=save_plots, path_to_output=path_to_output, name=name) +model_to_plot = inverted_density_model +SH.plot_model_slice(inv_mesh, actv, model_to_plot, normal, ind_plot, [-1.0, 1.0], set, sec_loc=True, gdlines=True, which_prop="Den", cmap="Spectral", save_plt=save_plots, path_to_output=path_to_output, name=name) # Plot Inverted Model Slices (X) normal = "Y" -model_to_plot = inverted_density_model -SH.plot_model_slice(inv_mesh, actv, model_to_plot, normal, ind_plot, [-1.0, 1.0], set, sec_loc=True, gdlines=True, which_prop="Den", cmap="Spectral", save_plt=save_plots, path_to_output=path_to_output, name=name) +model_to_plot = inverted_density_model +SH.plot_model_slice(inv_mesh, actv, model_to_plot, normal, ind_plot, [-1.0, 1.0], set, sec_loc=True, gdlines=True, which_prop="Den", cmap="Spectral", save_plt=save_plots, path_to_output=path_to_output, name=name)

        @@ -658,22 +658,22 @@

        Step 12: Visualize the 3D Model -

        Step 13: Visualize the Quasi-Geological ModelΒΆ

        +

        Step 13: Visualize the Quasi-Geological ModelΒΆ

        The quasi-geological model (as given by Li et al., 2019) is visualized, showing different rock units classified by the GMM.

        Plot Density Contrast Model (Z)

        normal = "Z"
        -model_to_plot = quasi_geology_model
        -SH.plot_model_slice(inv_mesh, actv, model_to_plot, normal, ind_plot, [0, 4], set, sec_loc=True, gdlines=True, which_prop="QGM", cmap="jet", save_plt=save_plots, path_to_output=path_to_output, name=name)
        +model_to_plot = quasi_geology_model
        +SH.plot_model_slice(inv_mesh, actv, model_to_plot, normal, ind_plot, [0, 4], set, sec_loc=True, gdlines=True, which_prop="QGM", cmap="jet", save_plt=save_plots, path_to_output=path_to_output, name=name)
         
         # Plot Inverted Model Slices (Y)
         normal = "X"
        -model_to_plot = quasi_geology_model
        -SH.plot_model_slice(inv_mesh, actv, model_to_plot, normal, ind_plot, [0, 4], set, sec_loc=True, gdlines=True, which_prop="QGM", cmap="jet", save_plt=save_plots, path_to_output=path_to_output, name=name)
        +model_to_plot = quasi_geology_model
        +SH.plot_model_slice(inv_mesh, actv, model_to_plot, normal, ind_plot, [0, 4], set, sec_loc=True, gdlines=True, which_prop="QGM", cmap="jet", save_plt=save_plots, path_to_output=path_to_output, name=name)
         
         # Plot Inverted Model Slices (X)
         normal = "Y"
        -model_to_plot = quasi_geology_model
        -SH.plot_model_slice(inv_mesh, actv, model_to_plot, normal, ind_plot, [0, 4], set, sec_loc=True, gdlines=True, which_prop="QGM", cmap="jet", save_plt=save_plots, path_to_output=path_to_output, name=name)
        +model_to_plot = quasi_geology_model
        +SH.plot_model_slice(inv_mesh, actv, model_to_plot, normal, ind_plot, [0, 4], set, sec_loc=True, gdlines=True, which_prop="QGM", cmap="jet", save_plt=save_plots, path_to_output=path_to_output, name=name)
         
          @@ -683,26 +683,26 @@

          Step 13: Visualize the Quasi-Geological Model -

          Step 14: Plot the Updated 1D-GMMΒΆ

          +

          Step 14: Plot the Updated 1D-GMMΒΆ

          The updated GMM, learned through the inversion, is visualized.

          # Plot the learned GMM
          -fig = plt.figure(figsize=(6, 6))
          -ax = regularization_term.objfcts[0].gmm.plot_pdf(flag2d=False, plotting_precision=500, padding=0.4)
          -ax[0].hist(inverted_density_model[actv], density=True, bins=1000)
          -ax[0].set_xlabel(r"Density contrast (g/cc)")
          -ax[0].set_ylabel(r"Probability Density Values")
          -ax[0].get_legend().remove()
          -ax[0].set_title(r"Learned DelRho Distribution")
          -ax[0].ticklabel_format(axis="both", style="scientific", scilimits=(0, 0))
          -plt.savefig(path_to_output + "/" + name + "_Learned_GMM.pdf", bbox_inches="tight")
          -plt.show()
          +fig = plt.figure(figsize=(6, 6))
          +ax = regularization_term.objfcts[0].gmm.plot_pdf(flag2d=False, plotting_precision=500, padding=0.4)
          +ax[0].hist(inverted_density_model[actv], density=True, bins=1000)
          +ax[0].set_xlabel(r"Density contrast (g/cc)")
          +ax[0].set_ylabel(r"Probability Density Values")
          +ax[0].get_legend().remove()
          +ax[0].set_title(r"Learned DelRho Distribution")
          +ax[0].ticklabel_format(axis="both", style="scientific", scilimits=(0, 0))
          +plt.savefig(path_to_output + "/" + name + "_Learned_GMM.pdf", bbox_inches="tight")
          +plt.show()
           
          • 01 gravity gradiometry inversion pgi
          • Learned DelRho Distribution
          -

          Total running time of the script: ( 10 minutes 14.392 seconds)

          +

          Total running time of the script: ( 10 minutes 3.217 seconds)

          -

          Predicting P-Wave Velocity from Hyperspectral DataΒΆ

          +

          Predicting P-Wave Velocity from Hyperspectral DataΒΆ

          This example demonstrates the prediction of P-Wave velocities from hyperspectral data using machine learning techniques. Hyperspectral data provides detailed information on rock mineralogy, which influences its petrophysical properties. This script aims to explore and quantify the relationship between spectral features and various petrophysical properties, specifically focusing on P-Wave velocity.

          -

          Importing Necessary LibrariesΒΆ

          +

          Importing Necessary LibrariesΒΆ

          First, we import the required Python packages. We use dotenv to manage environment variables, numpy and sklearn for numerical operations and machine learning, and custom packages hklearn and hycore for handling hyperspectral data and machine learning workflows.

          from dotenv import dotenv_values
           import numpy as np
          @@ -66,7 +66,7 @@ 

          Importing Necessary Libraries -

          Loading Data with hycore.ShedΒΆ

          +

          Loading Data with hycore.ShedΒΆ

          The hycore package, which organizes hyperspectral data in a structure called Shed, is used here. A Shed makes it convenient to categorize and store hyperspectral data. We use HyLibraries (a data structure within hylite for storing spectral libraries) within a Shed to store the hyperspectral data. The corresponding P-Wave velocity measurements are stored as numpy arrays.

          # Load the train and test sheds
           config = dotenv_values()
          @@ -80,17 +80,17 @@ 

          Loading Data with hycore.Shedtrain_fx50 = train_shed.results.FX50Lib train_fenix = train_shed.results.FENIXLib train_lwir = train_shed.results.LWIRLib -train_vp = train_shed.results.Vp +train_vp = train_shed.results.Vp test_fx50 = test_shed.results.FX50Lib test_fenix = test_shed.results.FENIXLib test_lwir = test_shed.results.LWIRLib -test_vp = test_shed.results.Vp +test_vp = test_shed.results.Vp

          -

          Creating a hklearn.Stack ObjectΒΆ

          +

          Creating a hklearn.Stack ObjectΒΆ

          The hklearn package, designed to handle hyperspectral data, is used to organize spectral libraries into a structure called Stack. A Stack can manage multiple spectral libraries and integrate data from multiple sensors. We perform hull corrections on the spectra to enhance features of interest.

          # Create Stack objects and apply hull correction to the spectra
           hsi_train = hklearn.Stack(
          @@ -109,7 +109,7 @@ 

          Creating a hklearn.Stack Object
          Removing hull:   0%|          | 0/3771 [00:00<?, ?it/s]
          -Removing hull:   0%|          | 1/3771 [00:01<1:12:24,  1.15s/it]
          +Removing hull:   0%|          | 1/3771 [00:01<1:12:51,  1.16s/it]
           
           
           Removing hull:   0%|          | 0/3771 [00:00<?, ?it/s]
          @@ -129,29 +129,29 @@ 

          Creating a hklearn.Stack Object -

          Scaling the Dependent Variable (Y)ΒΆ

          +

          Scaling the Dependent Variable (Y)ΒΆ

          The P-Wave velocities are scaled to facilitate the convergence of the loss function during model training. We use the StandardScaler from scikit-learn for this purpose.

          # Scale Y Variable (P-Wave velocity)
           y_scaler = sklearn.preprocessing.StandardScaler()
          -_ = y_scaler.fit(train_vp[:, None])
          +_ = y_scaler.fit(train_vp[:, None])
           

          -

          Transforming the Independent Variable (X)ΒΆ

          +

          Transforming the Independent Variable (X)ΒΆ

          Hyperspectral data often contains a large number of bands, many of which might be redundant. We apply Principal Component Analysis (PCA) to extract the most significant components. A two-step PCA is performed to ensure that there isn’t any inter-sensor correlation. The Stack object stores the PCA, which is also applied to the test set.

          # Apply PCA to the X variable (spectral data)
           PCA_X = hsi_train.fit_pca(n_components=10, normalise=True)
           hsi_test.set_transform(PCA_X)
           
           # Set the Y-Variable (P-Wave velocity)
          -hsi_train.set_y(train_vp[:, None, None])
          -hsi_test.set_y(test_vp[:, None, None])
          +hsi_train.set_y(train_vp[:, None, None])
          +hsi_test.set_y(test_vp[:, None, None])
           
          -

          Initializing and Training ModelsΒΆ

          +

          Initializing and Training ModelsΒΆ

          Different machine learning models are initialized and trained. In this example, we use a simple linear regression (LinearRegression) and a Multilayer Perceptron (MLPRegressor) from scikit-learn. A dictionary with the parameters for optimization is also initialized.

          -

          Scoring and Evaluating ModelsΒΆ

          +

          Scoring and Evaluating ModelsΒΆ

          Finally, the trained models are scored and evaluated. The Stack.get_score_table() method is used to display a table with the training, cross-validation, and test scores of the best-performing model.


          -

          Total running time of the script: ( 0 minutes 34.561 seconds)

          +

          Total running time of the script: ( 0 minutes 35.813 seconds)

          -

          Structural ModelingΒΆ

          +

          Structural ModelingΒΆ

          Delve into the world of 3D subsurface modeling with our Structural Modeling tutorials. In this section, we focus on harnessing the capabilities of gempy, a powerful tool developed by a partner within the Vector consortium. Here’s what these tutorials offer:

          • 3D Subsurface Modeling: Learn the intricacies of constructing detailed 3D geological models. We guide you through the process of transforming raw geophysical data into a comprehensive three-dimensional representation of the subsurface.

          • @@ -93,9 +93,15 @@

            Structural Modeling

            Construct Model 1 with Helper Functions

            Construct Model 1 with Helper Functions
            +

      +

      Construct Spremberg: Importing borehole data

      +
      Construct Spremberg: Importing borehole data
      +
      +

      Construct Spremberg: Building initial model

      +
      Construct Spremberg: Building initial model
      -

      GeoPhysics and InversionΒΆ

      +

      GeoPhysics and InversionΒΆ

      Our tutorials are crafted to guide you through the process of applying geophysical forward modeling and inversion techniques, using advanced tools like gempy and simpeg. Here’s what you can expect:

      • Geophysical Forward Modeling: Learn how to transform structural models into geophysical models. These tutorials will show you how to use gempy to simulate geophysical responses based on your structural models. Whether you are a beginner or looking to refine your modeling skills, these guides offer comprehensive insights.

      • @@ -118,7 +124,7 @@

        GeoPhysics and InversionPredicting P-Wave Velocity from Hyperspectral Data

      -

      Probabilistic ModelingΒΆ

      +

      Probabilistic ModelingΒΆ

      In the dynamic and often uncertain realm of subsurface exploration, probabilistic modeling emerges as a key tool. This section delves into the application of Bayesian Statistics and probabilistic approaches to address the inherent uncertainties in subsurface data. Here’s what you’ll learn:

      • Embracing Uncertainty: Gain an understanding of how to quantify and incorporate uncertainty in geological models. We focus on aleatoric uncertainty, which represents the inherent variability in subsurface data.

      • @@ -200,7 +206,7 @@

        Quick search

        ©2023-2024, Vector Geology Team. | - Powered by Sphinx 7.1.1 + Powered by Sphinx 7.2.6 & Alabaster 0.7.13 | diff --git a/examples/sg_execution_times.html b/examples/sg_execution_times.html new file mode 100644 index 0000000..23e8d32 --- /dev/null +++ b/examples/sg_execution_times.html @@ -0,0 +1,141 @@ + + + + + + + + Computation times — Vector Geology 0.3.dev0+gf79ba49.d20240612 documentation + + + + + + + + + + + + + + + + + + + + + + + + + +
        +
        +
        + + +
        + +
        +

        Computation timesΒΆ

        +

        00:00.000 total execution time for 0 files from examples:

        +
        + + + + + + + + + + + + + + + + + +

        Example

        Time

        Mem (MB)

        N/A

        N/A

        N/A

        +
        +
        + + +
        + +
        +
        + +
        +
        + + + + + + + \ No newline at end of file diff --git a/external/external_examples.html b/external/external_examples.html index d5bcd2a..693fc48 100644 --- a/external/external_examples.html +++ b/external/external_examples.html @@ -1,23 +1,23 @@ - + - Bayesian Inference Theory — Vector Geology 0.1.dev16+g525c9e2 documentation - + Bayesian Inference Theory — Vector Geology 0.3.dev0+gf79ba49.d20240612 documentation + - + - + - + @@ -41,7 +41,7 @@
        -

        Bayesian Inference TheoryΒΆ

        +

        Bayesian Inference TheoryΒΆ

        Here you will find a collection of examples that demonstrate the basics of Bayesian inference theory and how to apply it to geology using Pyro.

        @@ -133,7 +133,7 @@

        Quick search

        ©2023-2024, Vector Geology Team. | - Powered by Sphinx 7.1.1 + Powered by Sphinx 7.2.6 & Alabaster 0.7.13 | diff --git a/genindex.html b/genindex.html index 30f3adf..50bb4e9 100644 --- a/genindex.html +++ b/genindex.html @@ -1,22 +1,22 @@ - + - Index — Vector Geology 0.1.dev16+g525c9e2 documentation - + Index — Vector Geology 0.3.dev0+gf79ba49.d20240612 documentation + - + - + - + @@ -100,7 +100,7 @@

        Quick search

        ©2023-2024, Vector Geology Team. | - Powered by Sphinx 7.1.1 + Powered by Sphinx 7.2.6 & Alabaster 0.7.13
        diff --git a/index.html b/index.html index d6c7a35..1b84701 100644 --- a/index.html +++ b/index.html @@ -1,23 +1,23 @@ - + - vector-geology Documentation — Vector Geology 0.1.dev16+g525c9e2 documentation - + vector-geology Documentation — Vector Geology 0.3.dev0+gf79ba49.d20240612 documentation + - + - + - + @@ -40,13 +40,13 @@
        -

        vector-geology DocumentationΒΆ

        +

        vector-geology DocumentationΒΆ

        Release:
        -

        0.1.dev16+g525c9e2

        +

        0.3.dev0+gf79ba49.d20240612

        Date:
        -

        26 January 2024

        +

        13 June 2024

        Source:

        github.com/vector-raw-materials/vector-geology

        @@ -54,13 +54,13 @@

        -

        Vector Geology RepositoryΒΆ

        +

        Vector Geology RepositoryΒΆ

        -

        OverviewΒΆ

        +

        OverviewΒΆ

        Welcome to the Vector Geology repository, a collaborative platform integrating a suite of geophysical and remote sensing data processing tools developed by the Vector consortium . This repository serves as the central hub for accessing, understanding, and utilizing a range of software packages designed for geological exploration and research.

        -

        Key FeaturesΒΆ

        +

        Key FeaturesΒΆ

        • Integrated Toolsets: Collection of diverse code packages developed and maintained by Vector consortium members, offering a wide range of functionalities for geophysical data processing.

        • Comprehensive Tutorials: Step-by-step guides demonstrating typical workflows, from data input to advanced inversion techniques.

        • @@ -69,9 +69,9 @@

          Key Features -

          Getting StartedΒΆ

          +

          Getting StartedΒΆ

          -

          PrerequisitesΒΆ

          +

          PrerequisitesΒΆ

          • Basic understanding of geophysical exploration techniques and remote sensing data.

          • Familiarity with programming languages and tools used in the repository (e.g., Python).

          • @@ -82,7 +82,7 @@

            Prerequisites -

            Explore Our GuidesΒΆ

            +

            Explore Our GuidesΒΆ

            Galleries

              @@ -102,7 +102,7 @@

              Explore Our Guides -

              Vector Geology ExamplesΒΆ

              +

              Vector Geology ExamplesΒΆ

              Welcome to the Vector Geology Examples section. This space is dedicated to showcasing the diverse capabilities of the Vector platform. Here, we illustrate not only what you can achieve with Vector but also guide you on how to implement these techniques effectively. Our examples are categorized into distinct areas:

              @@ -121,7 +121,7 @@

              Vector Geology Examples

        -

        Readers and ParsersΒΆ

        +

        Readers and ParsersΒΆ

        Effective data analysis in geophysics begins with efficient data handling. This section is dedicated to demonstrating the use of specialized libraries to read and parse various data formats commonly encountered in the Vector Geology project. Here’s what you can expect:

        • Format Recognition: Learn how to identify and handle different data formats used in geophysical and remote sensing research.

        • @@ -142,7 +142,7 @@

          Readers and ParsersGravity Data Visualization

      -

      Structural ModelingΒΆ

      +

      Structural ModelingΒΆ

      Delve into the world of 3D subsurface modeling with our Structural Modeling tutorials. In this section, we focus on harnessing the capabilities of gempy, a powerful tool developed by a partner within the Vector consortium. Here’s what these tutorials offer:

      -

      GeoPhysics and InversionΒΆ

      +

      GeoPhysics and InversionΒΆ

      Our tutorials are crafted to guide you through the process of applying geophysical forward modeling and inversion techniques, using advanced tools like gempy and simpeg. Here’s what you can expect:

      • Geophysical Forward Modeling: Learn how to transform structural models into geophysical models. These tutorials will show you how to use gempy to simulate geophysical responses based on your structural models. Whether you are a beginner or looking to refine your modeling skills, these guides offer comprehensive insights.

      • @@ -181,7 +187,7 @@

        GeoPhysics and InversionPredicting P-Wave Velocity from Hyperspectral Data

      -

      Probabilistic ModelingΒΆ

      +

      Probabilistic ModelingΒΆ

      In the dynamic and often uncertain realm of subsurface exploration, probabilistic modeling emerges as a key tool. This section delves into the application of Bayesian Statistics and probabilistic approaches to address the inherent uncertainties in subsurface data. Here’s what you’ll learn:

      • Embracing Uncertainty: Gain an understanding of how to quantify and incorporate uncertainty in geological models. We focus on aleatoric uncertainty, which represents the inherent variability in subsurface data.

      • @@ -200,7 +206,7 @@

        Probabilistic ModelingGallery generated by Sphinx-Gallery

      -

      Bayesian Inference TheoryΒΆ

      +

      Bayesian Inference TheoryΒΆ

      Here you will find a collection of examples that demonstrate the basics of Bayesian inference theory and how to apply it to geology using Pyro.

      @@ -235,7 +241,7 @@

      Probabilistic Modeling

      -

      RequirementsΒΆ

      +

      RequirementsΒΆ

      The requirements for the core functionality of the package are:

      .
       numpy
      @@ -245,7 +251,7 @@ 

      Requirements -

      Optional requirementsΒΆ

      +

      Optional requirementsΒΆ

      There are many optional requirements, depending on the data format you want to read/write. Currently, the requirements_opt.txt reads like:

      git+https://github.com/terranigma-solutions/subsurface.git#egg=subsurface
      @@ -261,7 +267,7 @@ 

      Optional requirements

      -

      AcknowledgementsΒΆ

      +

      AcknowledgementsΒΆ

      This project has received funding from the European Unionβ€˜s Horizon Europe research and innovation programme under grant agreement No 101058483

      Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or Horizon Europe research and innovation programme

      EU logo @@ -326,7 +332,7 @@

      Quick search

      ©2023-2024, Vector Geology Team. | - Powered by Sphinx 7.1.1 + Powered by Sphinx 7.2.6 & Alabaster 0.7.13 | diff --git a/maintenance.html b/maintenance.html index 94459de..84ab245 100644 --- a/maintenance.html +++ b/maintenance.html @@ -1,23 +1,23 @@ - + - Maintenance — Vector Geology 0.1.dev16+g525c9e2 documentation - + Maintenance — Vector Geology 0.3.dev0+gf79ba49.d20240612 documentation + - + - + - + @@ -40,9 +40,9 @@
      -

      MaintenanceΒΆ

      +

      MaintenanceΒΆ

      -

      Making a releaseΒΆ

      +

      Making a releaseΒΆ

      1. Add release notes to CHANGELOG.rst.

      2. Create a release on GitHub, add the above release notes to it.

      3. @@ -59,7 +59,7 @@

        Making a release -

        Type of commitsΒΆ

        +

        Type of commitsΒΆ

        • ENH: Enhancement, new functionality

        • BUG: Bug fix

        • @@ -132,7 +132,7 @@

          Quick search

          ©2023-2024, Vector Geology Team. | - Powered by Sphinx 7.1.1 + Powered by Sphinx 7.2.6 & Alabaster 0.7.13 | diff --git a/manual.html b/manual.html index a1e441a..07ad121 100644 --- a/manual.html +++ b/manual.html @@ -1,23 +1,23 @@ - + - Manual — Vector Geology 0.1.dev16+g525c9e2 documentation - + Manual — Vector Geology 0.3.dev0+gf79ba49.d20240612 documentation + - + - + - + @@ -39,7 +39,7 @@
          -

          ManualΒΆ

          +

          ManualΒΆ

          Todo

          Add a basic user manual

          @@ -102,7 +102,7 @@

          Quick search

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          Navigation

          ©2023-2024, Vector Geology Team. | - Powered by Sphinx 7.1.1 + Powered by Sphinx 7.2.6 & Alabaster 0.7.13
          diff --git a/searchindex.js b/searchindex.js index f10ed48..33fbad2 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["contributing", "examples/01_readers/01_read_OMF_sites", "examples/01_readers/02_model_1_OMF_to_subsurface", "examples/01_readers/03_model_2_OMF_to_subsurface", "examples/01_readers/04_model_1_read_gravity", "examples/01_readers/index", "examples/01_readers/sg_execution_times", "examples/02_structural_modeling/01_model_1_gempy_step_by_step", "examples/02_structural_modeling/02_model_1_gempy", "examples/02_structural_modeling/index", "examples/02_structural_modeling/sg_execution_times", "examples/03_forward_engines/01_gravity_gradiometry_inversion_pgi", "examples/03_forward_engines/02_model_1_gempy_fw_gravity", "examples/03_forward_engines/03_HSI_To_Petrophysics", "examples/03_forward_engines/04_FTG_PGI", "examples/03_forward_engines/index", "examples/03_forward_engines/sg_execution_times", 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"Todo": [[27, "id1"]]}, "indexentries": {}}) \ No newline at end of file diff --git a/sg_execution_times.html b/sg_execution_times.html new file mode 100644 index 0000000..e654f7a --- /dev/null +++ b/sg_execution_times.html @@ -0,0 +1,189 @@ + + + + + + + + Computation times — Vector Geology 0.3.dev0+gf79ba49.d20240612 documentation + + + + + + + + + + + + + + + + + + + + + + + + + +
          +
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          + +
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          Computation timesΒΆ

          +

          01:09.159 total execution time for 13 files from all galleries:

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          Example

          Time

          Mem (MB)

          Construct Spremberg: Building initial model (../../examples/02_structural_modeling/04_model_spremberg_building.py)

          00:52.865

          0.0

          Probabilistic Inversion Example: Geological Model (../../examples/04_probabilistic_modeling/02_model_1_bayesian.py)

          00:16.294

          0.0

          Reading OMF Project Into Python (../../examples/01_readers/01_read_OMF_sites.py)

          00:00.000

          0.0

          Reading Model 1 OMF Project into Subsurface (../../examples/01_readers/02_model_1_OMF_to_subsurface.py)

          00:00.000

          0.0

          Reading OMF project and exporting it to Subsurface: Example 1 (../../examples/01_readers/03_model_2_OMF_to_subsurface.py)

          00:00.000

          0.0

          Gravity Data Visualization (../../examples/01_readers/04_model_1_read_gravity.py)

          00:00.000

          0.0

          Constructing Structural Geological Model (../../examples/02_structural_modeling/01_model_1_gempy_step_by_step.py)

          00:00.000

          0.0

          Construct Model 1 with Helper Functions (../../examples/02_structural_modeling/02_model_1_gempy.py)

          00:00.000

          0.0

          Construct Spremberg: Importing borehole data (../../examples/02_structural_modeling/03_model_spremberg_import.py)

          00:00.000

          0.0

          Inversion of Full Tensor Gravity Gradiometry Data (../../examples/03_forward_engines/01_gravity_gradiometry_inversion_pgi.py)

          00:00.000

          0.0

          Model 1 Forward Gravity (../../examples/03_forward_engines/02_model_1_gempy_fw_gravity.py)

          00:00.000

          0.0

          Predicting P-Wave Velocity from Hyperspectral Data (../../examples/03_forward_engines/03_HSI_To_Petrophysics.py)

          00:00.000

          0.0

          2.2 - Including GemPy (../../examples/04_probabilistic_modeling/01_thickness_problem_gempy.py)

          00:00.000

          0.0

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          + + + + + + + \ No newline at end of file

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