3-magnetic-inversion-raglan-lp.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "h6m9YbHtjm7lMhF3zATA",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "# 3D Magnetic Inversion of Raglan Data with Lp-norm\n",
    "\n",
    "In this notebook, we inverted the magnetic data acquired at [Raglan deposit](https://en.wikipedia.org/wiki/Raglan_Mine) located in Northern Quebec, Canada to obtain a three-dimensional susceptibility model of the subsurface. Magnetic module of an open-source geophysics software, [SimPEG ](https://www.simpeg.xyz) was used for this inversion. \n",
    "\n",
    "About 20 years ago, this data was inverted using the [MAG3D code](https://mag3d.readthedocs.io/en/latest/content/overview.html) developed by [UBC-GIF](https://gif.eos.ubc.ca/about) group, and a 3D susceptibility model was obtained. This was sort of the first time that field magnetic data was inverted in 3D, and made a significant impact on locating drilling location for a mineral exploration. \n",
    "\n",
    "In the [previous notebook](./1-magnetic-inversion-raglan-reproduce), we inverted these magnetic data using an L2-norm inversion. To promote the compactness of the target body, we used the Lp-norm inversion approach (Founier and Oldenburg, 2019). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "xSOil0mBA9bWRmvStN41",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "<center>\n",
    "<img src=\"./figures/mag3disco.gif\", class=\"center\"></img>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "rh4isubZDWAIl9CGuTHm",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "## Import modules\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "Dv1sFnCMjAak6eeNfTDV",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import tarfile\n",
    "\n",
    "from discretize import TensorMesh\n",
    "from discretize.utils import mesh_builder_xyz\n",
    "\n",
    "from SimPEG.potential_fields import magnetics\n",
    "from SimPEG import dask\n",
    "from SimPEG.utils import plot2Ddata, surface2ind_topo\n",
    "from SimPEG import (\n",
    "    maps,\n",
    "    data,\n",
    "    inverse_problem,\n",
    "    data_misfit,\n",
    "    regularization,\n",
    "    optimization,\n",
    "    directives,\n",
    "    inversion,\n",
    "    utils,\n",
    ")\n",
    "import pandas as pd\n",
    "from ipywidgets import widgets, interact"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "Yv24shLad5ZjXMm0aQlU",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "## Load Data and Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "korKsPB9v4Yugw9fFSZX",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "def read_ubc_magnetic_data(data_filename):\n",
    "    with open(data_filename, 'r') as f:\n",
    "        lines = f.readlines()\n",
    "    tmp = np.array(lines[0].split()[:3]).astype(float)\n",
    "    n_data = int(float(lines[2].split()[0]))\n",
    "    meta_data = {}\n",
    "    meta_data['inclination'] = float(tmp[0])\n",
    "    meta_data['declination'] = float(tmp[1])\n",
    "    meta_data['b0'] = float(tmp[2])\n",
    "    meta_data['n_data'] = n_data\n",
    "    data = np.zeros((n_data, 5), order='F')\n",
    "    for i_data in range(n_data):\n",
    "        data[i_data,:] = np.array(lines[3+i_data].split()).astype(float)\n",
    "    df = pd.DataFrame(data=data, columns=['x', 'y', 'z', 'data', 'data_error'])\n",
    "    return df, meta_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "rMcHA8kIQWWj9HIGWNK9",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "data_filename = \"./data/Raglan_1997/obs.mag\"\n",
    "df, meta_data = read_ubc_magnetic_data(data_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "XOiiqBjRIICg98V2ZO83",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": {
      "block": "qrnOaXVwP2Tw1rDgeVUB",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'inclination': 83.0, 'declination': -32.0, 'b0': 60000.0, 'n_data': 1638}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "6kxgIwhyE2xrH13C8k4d",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": {
      "block": "ph0hlnhwbZpEtCLXojCI",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>data</th>\n",
       "      <th>data_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>509.0</td>\n",
       "      <td>42958.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>42.90</td>\n",
       "      <td>5.8580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>504.0</td>\n",
       "      <td>42858.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>88.32</td>\n",
       "      <td>6.7664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>499.3</td>\n",
       "      <td>42758.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>86.11</td>\n",
       "      <td>6.7222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       x        y     z   data  data_error\n",
       "0  509.0  42958.0  40.0  42.90      5.8580\n",
       "1  504.0  42858.0  40.0  88.32      6.7664\n",
       "2  499.3  42758.0  40.0  86.11      6.7222"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "vVCY1DmVrHu4P4nKOwF8",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": {
      "block": "g2j93s9wm5GwIO2JeCQs",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Down sample the data\n",
    "matplotlib.rcParams['font.size'] = 14\n",
    "nskip = 2\n",
    "receiver_locations = df[['x', 'y', 'z']].values[::nskip,:]\n",
    "xyz_topo = np.c_[receiver_locations[:,:2], np.zeros(receiver_locations.shape[0])]\n",
    "dobs = df['data'].values[::nskip]\n",
    "# Plot\n",
    "fig = plt.figure(figsize=(12, 10))\n",
    "vmin, vmax = np.percentile(dobs, 0.5), np.percentile(dobs, 99.5)\n",
    "tmp = np.clip(dobs, vmin, vmax)\n",
    "ax1 = fig.add_axes([0.1, 0.1, 0.75, 0.85])\n",
    "plot2Ddata(\n",
    "    receiver_locations,\n",
    "    tmp,\n",
    "    ax=ax1,\n",
    "    ncontour=30,\n",
    "    clim=(vmin-5, vmax+5),\n",
    "    contourOpts={\"cmap\": \"Spectral_r\"},\n",
    ")\n",
    "ax1.set_title(\"TMI Anomaly\")\n",
    "ax1.set_xlabel(\"x (m)\")\n",
    "ax1.set_ylabel(\"y (m)\")\n",
    "\n",
    "ax2 = fig.add_axes([0.9, 0.25, 0.05, 0.5])\n",
    "\n",
    "norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)\n",
    "cbar = mpl.colorbar.ColorbarBase(\n",
    "    ax2, norm=norm, orientation=\"vertical\", cmap=mpl.cm.Spectral_r\n",
    ")\n",
    "cbar.set_label(\"$nT$\", rotation=270, labelpad=15, size=12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "u7sQlIJtgfFAj8oXcClo",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "## Assign Uncertainty\n",
    "\n",
    "Inversion with SimPEG requires that we define data error i.e., standard deviation on the observed data.\n",
    "This represents our estimate of the noise in our data. \n",
    "For this magnetic inversion, 2% relative error and a noise floor of 2 nT are assigned. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "nj91uOIFTa8McscCJuNI",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "standard_deviation = 0.02 * abs(dobs) + 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "Bfu9nCkVK0R3GFTBfD5H",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "## Defining the Survey\n",
    "\n",
    "Here, we define a survey object that will be used for the simulation. \n",
    "The user needs an (N, 3) array to define\n",
    "the xyz locations of the observation locations and the list of field components\n",
    "which are to be modeled and the properties of the Earth's field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "zP3YD0OkTc9ENhPjbTWz",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "# Define the component(s) of the field we are inverting as a list. Here we will\n",
    "# Invert total magnetic intensity data.\n",
    "components = [\"tmi\"]\n",
    "\n",
    "# Use the observation locations and components to define the receivers. To\n",
    "# simulate data, the receivers must be defined as a list.\n",
    "receiver_list = magnetics.receivers.Point(receiver_locations, components=components)\n",
    "\n",
    "receiver_list = [receiver_list]\n",
    "\n",
    "# Define the inducing field H0 = (intensity [nT], inclination [deg], declination [deg])\n",
    "inclination = meta_data['inclination']\n",
    "declination = meta_data['declination']\n",
    "strength = meta_data['b0']\n",
    "inducing_field = (strength, inclination, declination)\n",
    "\n",
    "source_field = magnetics.sources.SourceField(\n",
    "    receiver_list=receiver_list, parameters=inducing_field\n",
    ")\n",
    "\n",
    "# Define the survey\n",
    "survey = magnetics.survey.Survey(source_field)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "NFI3ARAHrmk0TLnNxdgp",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "## Defining the Data\n",
    "\n",
    "Here is where we define the data that is inverted. The data is defined by\n",
    "the survey, the observation values and the standard deviations.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "17kdgUcgQfIkaHteoGly",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "data_object = data.Data(survey, dobs=dobs, standard_deviation=standard_deviation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "uGWOcn1YVf3AmFR4Ny87",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "## Defining a Tensor Mesh\n",
    "\n",
    "Here, we create the tensor mesh that will be used to invert TMI data.\n",
    "If desired, we could define an OcTree mesh."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "Emx4JRxt5izoIIwU8ina",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "dx = 100 \n",
    "dy = 100\n",
    "dz = 100\n",
    "depth_core = 1000\n",
    "padding_distance_x_left = 1000\n",
    "padding_distance_x_right = 1000\n",
    "padding_distance_y_left = 1000\n",
    "padding_distance_y_right= 1000\n",
    "padding_distance_z_lower = 1000\n",
    "padding_distance_z_upper = 0\n",
    "mesh = mesh_builder_xyz(\n",
    "    xyz=xyz_topo, \n",
    "    h=[dx, dy, dz], \n",
    "    depth_core=depth_core, \n",
    "    padding_distance=[\n",
    "        [padding_distance_x_left, padding_distance_x_right], \n",
    "        [padding_distance_y_left, padding_distance_y_right], \n",
    "        [padding_distance_z_lower, padding_distance_z_upper]\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "3N6xgKtbGfC20FDVYFsp",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": {
      "block": "lDXlJFe7PROuBdgMqRww",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<string>:6: UserWarning: Warning: converting a masked element to nan.\n",
      "C:\\Users\\sgkan\\AppData\\Roaming\\Python\\Python37\\site-packages\\numpy\\ma\\core.py:717: UserWarning: Warning: converting a masked element to nan.\n",
      "  data = np.array(a, copy=False, subok=subok)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
    "mesh.plotSlice(np.ones(mesh.nC)*np.nan, ax=ax, grid=True)\n",
    "ax.plot(receiver_locations[:,0], receiver_locations[:,1], 'r.')\n",
    "ax.set_xlabel(\"Easting (m)\")\n",
    "ax.set_ylabel(\"Northing (m)\")\n",
    "ax.set_aspect(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "PketRpNq9eDeCz2Hg8Sd",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": {
      "block": "eIu7BaHX4EFP1qGDV5Fu",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
    "mesh.plotSlice(np.ones(mesh.nC)*np.nan, ax=ax, grid=True, normal='Y')\n",
    "ax.set_xlabel(\"Easting (m)\")\n",
    "ax.set_ylabel(\"Depth (m)\")\n",
    "ax.set_aspect(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "pJTzXhrGHqthw9ntNRv2",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "## Starting/Reference Model and Mapping on Tensor Mesh\n",
    "\n",
    "Here, we would create starting and/or reference models for the inversion as\n",
    "well as the mapping from the model space to the active cells. Starting and\n",
    "reference models can be a constant background value or contain a-priori\n",
    "structures. Here, the background is 1e-4 SI.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "M57lXY0THBaap8W2wzSw",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "# Define background susceptibility model in SI. Don't make this 0!\n",
    "# Otherwise the gradient for the 1st iteration is zero and the inversion will\n",
    "# not converge.\n",
    "background_susceptibility = 1e-4\n",
    "\n",
    "# Find the indecies of the active cells in forward model (ones below surface)\n",
    "ind_active = surface2ind_topo(mesh, np.c_[receiver_locations[:,:2], np.zeros(survey.nD)])\n",
    "\n",
    "# Define mapping from model to active cells\n",
    "nC = int(ind_active.sum())\n",
    "model_map = maps.IdentityMap(nP=nC)  # model consists of a value for each cell\n",
    "\n",
    "# Define starting model\n",
    "starting_model = background_susceptibility * np.ones(nC)\n",
    "reference_model = np.zeros(nC) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "jvQIJqPdFKO9TlRzPJsE",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "## Define the Physics\n",
    "\n",
    "Here, we define the physics of the magnetics problem by using the simulation\n",
    "class.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "b8u2dHOg51oNDo5o4xvF",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "# Define the problem. Define the cells below topography and the mapping\n",
    "simulation = magnetics.simulation.Simulation3DIntegral(\n",
    "    survey=survey,\n",
    "    mesh=mesh,\n",
    "    modelType=\"susceptibility\",\n",
    "    chiMap=model_map,\n",
    "    actInd=ind_active,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "k7pl5S6JfwtM3TGGA87G",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "## Define Inverse Problem\n",
    "\n",
    "The inverse problem is defined by 3 things:\n",
    "\n",
    "    1) Data Misfit: a measure of how well our recovered model explains the field data\n",
    "    2) Regularization: constraints placed on the recovered model and a priori information\n",
    "    3) Optimization: the numerical approach used to solve the inverse problem\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "xYq92mR9VSr3IkNnB0B1",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "# Define the data misfit. Here the data misfit is the L2 norm of the weighted\n",
    "# residual between the observed data and the data predicted for a given model.\n",
    "# Within the data misfit, the residual between predicted and observed data are\n",
    "# normalized by the data's standard deviation.\n",
    "dmis = data_misfit.L2DataMisfit(data=data_object, simulation=simulation)\n",
    "\n",
    "# Define the regularization (model objective function)\n",
    "reg = regularization.Sparse(\n",
    "    mesh,\n",
    "    indActive=ind_active,\n",
    "    mapping=model_map,\n",
    "    mref=reference_model,\n",
    "    gradientType=\"total\",\n",
    "    alpha_s=1,\n",
    "    alpha_x=1,\n",
    "    alpha_y=1,\n",
    "    alpha_z=1,\n",
    ")\n",
    "\n",
    "# Define sparse and blocky norms ps, px, py, pz\n",
    "ps = 0\n",
    "px = 2\n",
    "py = 2\n",
    "pz = 2\n",
    "reg.norms = np.c_[ps, px, py, pz]\n",
    "\n",
    "# Define how the optimization problem is solved. Here we will use a projected\n",
    "# Gauss-Newton approach that employs the conjugate gradient solver.\n",
    "opt = optimization.ProjectedGNCG(\n",
    "    maxIter=100, lower=0.0, upper=np.Inf, maxIterLS=20, maxIterCG=30, tolCG=1e-3\n",
    ")\n",
    "\n",
    "# Here we define the inverse problem that is to be solved\n",
    "inv_prob = inverse_problem.BaseInvProblem(dmis, reg, opt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "9dnfccgTdxMgTgEUhWyX",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "## Define Inversion Directives\n",
    "\n",
    "Here we define any directiveas that are carried out during the inversion. This\n",
    "includes the cooling schedule for the trade-off parameter (beta), stopping\n",
    "criteria for the inversion and saving inversion results at each iteration.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "xoattZV3IOqjWDTFQ5vA",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "# Defining a starting value for the trade-off parameter (beta) between the data\n",
    "# misfit and the regularization.\n",
    "starting_beta = directives.BetaEstimate_ByEig(beta0_ratio=1)\n",
    "beta_schedule = directives.BetaSchedule(coolingFactor=2, coolingRate=1)\n",
    "\n",
    "# Options for outputting recovered models and predicted data as a dictionary\n",
    "save_dictionary = directives.SaveOutputDictEveryIteration()\n",
    "\n",
    "# Defines the directives for the IRLS regularization. This includes setting\n",
    "# the cooling schedule for the trade-off parameter.\n",
    "update_IRLS = directives.Update_IRLS(\n",
    "    f_min_change=1e-3, max_irls_iterations=20, coolEpsFact=1.5, beta_tol=1e-2,\n",
    "    chifact_target=1, chifact_start=1\n",
    ")\n",
    "\n",
    "# Updating the preconditionner if it is model dependent.\n",
    "update_jacobi = directives.UpdatePreconditioner()\n",
    "\n",
    "# Setting a stopping criteria for the inversion.\n",
    "target_misfit = directives.TargetMisfit(chifact=1)\n",
    "\n",
    "# Add sensitivity weights\n",
    "sensitivity_weights = directives.UpdateSensitivityWeights(everyIter=False)\n",
    "opt.remember('xc')\n",
    "# The directives are defined as a list.\n",
    "directives_list = [\n",
    "    sensitivity_weights,\n",
    "    starting_beta,\n",
    "    save_dictionary,\n",
    "    update_IRLS,\n",
    "    update_jacobi,\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "QAEzeEMBS4UYigFzuPac",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "## Running the Inversion\n",
    "\n",
    "To define the inversion object, we need to define the inversion problem and\n",
    "the set of directives. We can then run the inversion.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "gQ5328059eGkAsDbWips",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": {
      "block": "f1fmFdNpovSwRy4Sve6F",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "        SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
      "        ***Done using same Solver and solverOpts as the problem***\n",
      "((819,), (18375, 18375))\n",
      "Computing sensitivities to local ram\n",
      "[########################################] | 100% Completed | 18.1s\n",
      "model has any nan: 0\n",
      "=============================== Projected GNCG ===============================\n",
      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
      "-----------------------------------------------------------------------------\n",
      "x0 has any nan: 0\n",
      "   0  5.22e+06  3.24e+05  4.09e-06  3.24e+05    7.59e+05      0              \n",
      "   1  2.61e+06  9.13e+04  5.12e-03  1.05e+05    4.15e+05      0              \n",
      "   2  1.30e+06  4.13e+04  1.23e-02  5.74e+04    1.37e+05      0   Skip BFGS  \n",
      "   3  6.52e+05  2.85e+04  1.87e-02  4.08e+04    1.76e+05      0   Skip BFGS  \n",
      "   4  3.26e+05  1.78e+04  2.85e-02  2.71e+04    1.25e+05      0   Skip BFGS  \n",
      "   5  1.63e+05  1.48e+04  3.44e-02  2.04e+04    1.16e+05      1   Skip BFGS  \n",
      "   6  8.15e+04  8.19e+03  6.04e-02  1.31e+04    7.66e+04      0              \n",
      "   7  4.08e+04  3.84e+03  8.30e-02  7.22e+03    8.45e+04      0              \n",
      "   8  2.04e+04  2.60e+03  1.08e-01  4.80e+03    1.05e+05      0   Skip BFGS  \n",
      "   9  1.02e+04  1.73e+03  1.18e-01  2.93e+03    6.45e+04      1              \n",
      "  10  5.10e+03  8.16e+02  1.44e-01  1.55e+03    3.28e+04      0   Skip BFGS  \n",
      "  11  2.55e+03  4.37e+02  1.55e-01  8.31e+02    2.34e+04      0              \n",
      "Reached starting chifact with l2-norm regularization: Start IRLS steps...\n",
      "eps_p: 0.3939384326453484 eps_q: 0.3939384326453484\n",
      "  12  1.27e+03  2.00e+02  2.42e-01  5.09e+02    2.24e+04      0              \n",
      "  13  2.68e+03  1.86e+02  2.90e-01  9.64e+02    1.94e+04      0              \n",
      "  14  5.70e+03  1.82e+02  3.37e-01  2.10e+03    1.70e+04      3   Skip BFGS  \n",
      "  15  1.23e+04  1.77e+02  3.81e-01  4.87e+03    2.04e+04      3              \n",
      "  16  2.00e+04  3.27e+02  4.03e-01  8.38e+03    3.32e+04      2              \n",
      "  17  1.10e+04  1.17e+03  3.68e-01  5.22e+03    9.69e+04      1              \n",
      "  18  6.45e+03  9.65e+02  3.86e-01  3.45e+03    9.24e+04      1              \n",
      "  19  3.94e+03  8.71e+02  4.04e-01  2.46e+03    5.83e+04      1   Skip BFGS  \n",
      "  20  2.64e+03  6.96e+02  4.12e-01  1.78e+03    3.83e+04      1              \n",
      "  21  1.80e+03  6.68e+02  4.08e-01  1.40e+03    6.62e+04      1   Skip BFGS  \n",
      "  22  1.25e+03  6.39e+02  4.09e-01  1.15e+03    1.02e+05      0              \n",
      "  23  2.47e+03  2.10e+02  3.86e-01  1.16e+03    2.11e+04      0              \n",
      "  24  4.87e+03  2.10e+02  3.36e-01  1.85e+03    1.50e+04      1              \n",
      "  25  3.87e+03  4.88e+02  2.49e-01  1.45e+03    5.68e+04      0              \n",
      "  26  9.44e+03  1.42e+02  2.26e-01  2.28e+03    1.86e+04      0              \n",
      "  27  1.60e+04  2.95e+02  1.70e-01  3.01e+03    6.22e+03      0              \n",
      "  28  1.28e+04  4.82e+02  1.38e-01  2.25e+03    9.84e+03      0              \n",
      "  29  2.09e+04  3.23e+02  1.38e-01  3.20e+03    3.71e+03      0              \n",
      "  30  1.57e+04  5.43e+02  1.18e-01  2.40e+03    6.39e+03      0              \n",
      "  31  2.44e+04  3.72e+02  1.23e-01  3.36e+03    3.49e+03      0              \n",
      "Reach maximum number of IRLS cycles: 20\n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 3.2360e+04\n",
      "0 : |xc-x_last| = 2.3015e-01 <= tolX*(1+|x0|) = 1.0192e-01\n",
      "0 : |proj(x-g)-x|    = 3.4870e+03 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 3.4870e+03 <= 1e3*eps       = 1.0000e-02\n",
      "0 : maxIter   =     100    <= iter          =     32\n",
      "------------------------- DONE! -------------------------\n"
     ]
    }
   ],
   "source": [
    "# Here we combine the inverse problem and the set of directives\n",
    "inv = inversion.BaseInversion(inv_prob, directives_list)\n",
    "\n",
    "# Print target misfit to compare with convergence\n",
    "# print(\"Target misfit is \" + str(target_misfit.target))\n",
    "\n",
    "# Run the inversion\n",
    "recovered_model = inv.run(starting_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "s1NBa8bHKt5ID5psRCtC",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "def plot_tikhonov_curve(iteration, scale):\n",
    "    phi_d = []\n",
    "    phi_m = []\n",
    "    beta = []\n",
    "    iterations = np.arange(len(save_dictionary.outDict)) + 1\n",
    "    for kk in iterations:\n",
    "        phi_d.append(save_dictionary.outDict[kk]['phi_d'])\n",
    "        phi_m.append(save_dictionary.outDict[kk]['phi_m'])\n",
    "        beta.append(save_dictionary.outDict[kk]['beta'])\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(12,5))\n",
    "    axs[0].plot(phi_m ,phi_d, 'k.-')\n",
    "    axs[0].plot(phi_m[iteration-1] ,phi_d[iteration-1], 'go', ms=10)\n",
    "    axs[0].set_xlabel(\"$\\phi_m$\")\n",
    "    axs[0].set_ylabel(\"$\\phi_d$\")\n",
    "    axs[0].grid(True)\n",
    "\n",
    "    axs[1].plot(iterations, phi_d, 'k.-')\n",
    "    axs[1].plot(iterations[iteration-1], phi_d[iteration-1], 'go', ms=10)\n",
    "    ax_1 = axs[1].twinx()\n",
    "    ax_1.plot(iterations, phi_m, 'r.-')\n",
    "    ax_1.plot(iterations[iteration-1], phi_m[iteration-1], 'go', ms=10)\n",
    "    axs[1].set_ylabel(\"$\\phi_d$\")\n",
    "    ax_1.set_ylabel(\"$\\phi_m$\")\n",
    "    axs[1].set_xlabel(\"Iterations\")\n",
    "    axs[1].grid(True)\n",
    "    axs[0].set_title(\n",
    "        \"$\\phi_d$={:.1e}, $\\phi_m$={:.1e}, $\\\\beta$={:.1e}\".format(phi_d[iteration-1], phi_m[iteration-1], beta[iteration-1]),\n",
    "        fontsize = 14\n",
    "    )\n",
    "    axs[1].set_title(\"Target misfit={:.0f}\".format(survey.nD/2))\n",
    "    for ii, ax in enumerate(axs):        \n",
    "        if ii == 0:\n",
    "            ax.set_xscale(scale)        \n",
    "        ax.set_yscale(scale)\n",
    "        xlim = ax.get_xlim()\n",
    "        ax.hlines(survey.nD/2, xlim[0], xlim[1], linestyle='--', label='$\\phi_d^{*}$')\n",
    "        ax.set_xlim(xlim)\n",
    "    axs[0].legend()\n",
    "    plt.tight_layout()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "qVnPWo578COas4ItZ6jR",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": {
      "block": "JsoHKBWN62IFY8vMpwF2",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5f10999d96de4e89abf872b688e6bc53",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=1, continuous_update=False, description='iteration', max=32, min=1), Rad…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_tikhonov_curve(iteration, scale)>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact(\n",
    "    plot_tikhonov_curve, \n",
    "    iteration=widgets.IntSlider(min=1, max=len(save_dictionary.outDict), step=1, continuous_update=False),\n",
    "    scale=widgets.RadioButtons(options=[\"linear\", \"log\"])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "1uBaWvM9cFr371dHqT8o",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "susceptibility_model = save_dictionary.outDict[31]['m']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "am3p4LsPAGjCvlEssNOk",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "def plot_model_histogram(iteration, yscale):\n",
    "    out = plt.hist(save_dictionary.outDict[iteration]['m'], bins=np.linspace(0, 0.1))\n",
    "    plt.xlabel('Susceptibility (SI)')\n",
    "    plt.yscale(yscale)\n",
    "    plt.ylabel('Counts')\n",
    "#     plt.ylim(10, 1e5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "GiHN32hUsqD73U53As9y",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": {
      "block": "yCQZOejMhi7rP8n6SKSC",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "addf0c0e6eac4093857051a8dd570dbc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=1, description='iteration', max=32, min=1), RadioButtons(description='ys…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_model_histogram(iteration, yscale)>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact(\n",
    "    plot_model_histogram, \n",
    "    iteration=widgets.IntSlider(min=1, max=len(save_dictionary.outDict), step=1),\n",
    "    yscale=widgets.RadioButtons(options=[\"linear\", \"log\"])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "wfKAKue4ZpRyEaI4pDOM",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "def plot_dobs_vs_dpred(iteration):\n",
    "    # Predicted data with final recovered model\n",
    "    dpred = save_dictionary.outDict[iteration]['dpred']\n",
    "\n",
    "    # Observed data | Predicted data | Normalized data misfit\n",
    "    data_array = np.c_[dobs, dpred, (dobs - dpred) / standard_deviation]\n",
    "    vmin, vmax = dobs.min(), dobs.max()\n",
    "    fig = plt.figure(figsize=(17, 4))\n",
    "    plot_title = [\"Observed\", \"Predicted\", \"Normalized Misfit\"]\n",
    "    plot_units = [\"nT\", \"nT\", \"\"]\n",
    "\n",
    "    ax1 = 3 * [None]\n",
    "    ax2 = 3 * [None]\n",
    "    norm = 3 * [None]\n",
    "    cbar = 3 * [None]\n",
    "    cplot = 3 * [None]\n",
    "    v_lim = [(vmin, vmax), (vmin, vmax),(-3,3)]\n",
    "\n",
    "    for ii in range(0, 3):\n",
    "\n",
    "        ax1[ii] = fig.add_axes([0.33 * ii + 0.03, 0.11, 0.25, 0.84])\n",
    "        cplot[ii] = plot2Ddata(\n",
    "            receiver_list[0].locations,\n",
    "            data_array[:, ii],\n",
    "            ax=ax1[ii],\n",
    "            ncontour=30,\n",
    "            clim=v_lim[ii],\n",
    "            contourOpts={\"cmap\": \"Spectral_r\"},\n",
    "        )\n",
    "        ax1[ii].set_title(plot_title[ii])\n",
    "        ax1[ii].set_xlabel(\"x (m)\")\n",
    "        ax1[ii].set_ylabel(\"y (m)\")\n",
    "\n",
    "        ax2[ii] = fig.add_axes([0.33 * ii + 0.27, 0.11, 0.01, 0.84])\n",
    "        norm[ii] = mpl.colors.Normalize(vmin=v_lim[ii][0], vmax=v_lim[ii][1])\n",
    "        cbar[ii] = mpl.colorbar.ColorbarBase(\n",
    "            ax2[ii], norm=norm[ii], orientation=\"vertical\", cmap=mpl.cm.Spectral_r\n",
    "        )\n",
    "        cbar[ii].set_label(plot_units[ii], rotation=270, labelpad=15, size=12)\n",
    "    for ax in ax1[1:]:\n",
    "        ax.set_ylabel(\"\")\n",
    "        ax.set_yticklabels([])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "e9SIBXX14ow1fa2Ug2S8",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": {
      "block": "28DF3QQTrsk5RNq7Xnv8",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "200c6fe0be5940a49a92a528328aadaa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=1, description='iteration', max=32, min=1), Output()), _dom_classes=('wi…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_dobs_vs_dpred(iteration)>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact(plot_dobs_vs_dpred, iteration=widgets.IntSlider(min=1, max=len(save_dictionary.outDict), step=1, value=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "rFCb20u7KpIf4VSmnEBa",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": {
      "block": "RUiWSHv62CDX0G4XFeRH",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "daca122bda694a62a995441635b95784",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=1, description='iteration', max=32, min=1), FloatText(value=2000.0, desc…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_recovered_model(iteration, xslice, yslice, zslice, vmax)>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_recovered_model(iteration, xslice, yslice, zslice, vmax):\n",
    "    fig = plt.figure(figsize=(10, 10))\n",
    "    mesh.plot_3d_slicer(\n",
    "        save_dictionary.outDict[iteration]['m'], clim=(0, vmax),\n",
    "        xslice=xslice,\n",
    "        yslice=yslice,\n",
    "        zslice=zslice,\n",
    "        fig=fig,\n",
    "        pcolor_opts={'cmap':'Spectral_r'}\n",
    "    )\n",
    "interact(\n",
    "    plot_recovered_model, \n",
    "    iteration=widgets.IntSlider(min=1, max=len(save_dictionary.outDict), value=0),\n",
    "    xslice=widgets.FloatText(value=2000, step=100),\n",
    "    yslice=widgets.FloatText(value=41000, step=100),\n",
    "    zslice=widgets.FloatText(value=-800, step=100),\n",
    "    vmax=widgets.FloatText(value=0.07),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "mYiSLbeGieb8NTsKbWqW",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "## Comparing the historic model with the recovered model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "ALFEYmYgU5pSpAq3bjoT",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "from discretize.utils import ExtractCoreMesh\n",
    "zmin, zmax = -1500, 0\n",
    "ymin, ymax = receiver_locations[:,1].min(), receiver_locations[:,1].max()\n",
    "xmin, xmax = receiver_locations[:,0].min(), receiver_locations[:,0].max()\n",
    "xyzlim = np.array([[xmin, xmax],[ymin, ymax], [zmin, zmax]])\n",
    "inds_core, mesh_core = ExtractCoreMesh(xyzlim, mesh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "KURVN5vpHplvEsqxTZz8",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "import pyvista as pv\n",
    "def plot_3d_with_pyvista(model, notebook=True, threshold=0.04):\n",
    "    pv.set_plot_theme(\"document\")\n",
    "    # Get the PyVista dataset of the inverted model\n",
    "    dataset = mesh_core.to_vtk({'susceptibility':model})\n",
    "    # Create the rendering scene\n",
    "    p = pv.Plotter(notebook=notebook)\n",
    "    # add a grid axes\n",
    "    p.show_grid()\n",
    "    # Extract volumetric threshold\n",
    "    threshed = dataset.threshold(threshold, invert=False)\n",
    "    # Add spatially referenced data to the scene\n",
    "    dparams = dict(\n",
    "        show_edges=False,\n",
    "        cmap=\"Spectral_r\",\n",
    "        clim=[0, 0.07],\n",
    "        stitle='Susceptibility (SI)', \n",
    "    )\n",
    "    p.add_mesh(threshed, **dparams)\n",
    "    p.set_scale(1,1,1)\n",
    "    cpos = [(-5248.506818695238, 35263.832232792156, 4945.734122744097),\n",
    "     (2140.1554568144284, 40814.32410594353, -1198.9698078219635),\n",
    "     (0.4274014723619113, 0.35262874486945933, 0.8324547733749025)]\n",
    "    p.camera_position = cpos\n",
    "    p.show(window_size=[1024, 768])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "C4qI6WHmxq7cc5LAbOmz",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "### Recovered susceptiblity model from L2-norm inversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "m5WoFEunUeIefQ3nxsp4",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": {
      "block": "5AgnYefnFLeN9mUHP3kV",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1024x768 at 0x152E4E10AC8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_3d_with_pyvista(inv_prob.l2model[inds_core], notebook=True, threshold=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "Qz9yky7qUFvKfbVwyWLb",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "source": [
    "### Recovered susceptiblity model from Lp-norm inversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "ZTgDJB0thZrQDM2jidBS",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     },
     "outputId": {
      "block": "q1OOmsjAY081euRb95nt",
      "project": "FesuZy9nERkRH9r67vBe",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1024x768 at 0x152B4C44BA8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_3d_with_pyvista(susceptibility_model[inds_core], notebook=True, threshold=0.07)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "iooxa": null
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "iooxa": {
   "id": {
    "block": "cjnIdgfF82YJ74eqHMm7",
    "project": "FesuZy9nERkRH9r67vBe",
    "version": 4
   }
  },
  "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}