{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5a91974a",
   "metadata": {},
   "source": [
    "# Gravity and Magnetic 3D Inversion\n",
    "\n",
    "<img align=\"right\" width=\"300px\" src=\"./images/inversion_grav_mag_thumbnail.png\">\n",
    "\n",
    "This application provides an interface to the open-source [SimPEG](https://simpeg.xyz/) package for the inversion of gravity and magnetic data (fields and tensor components). The application handles:\n",
    "\n",
    " - Gravity inversion for the recovery of residual density (g/cc).\n",
    " - Scalar magnetic inversion (MAG) for the recovery of susceptibility (SI).\n",
    " - Vector magnetic inversion (MVI) for the recovery of 3-component magnetization (effective susceptibility and angles).\n",
    "\n",
    "All inversions are performed on a 3D octree mesh.\n",
    "\n",
    "New user? Visit the [**Getting Started**](https://mirageoscience-geoapps.readthedocs-hosted.com/en/latest/content/installation.html) page."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unnecessary-nerve",
   "metadata": {},
   "source": [
    "## Application\n",
    "The following sections provide details on the different parameters controlling the application. Interactive widgets shown below are for demonstration purposes only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "collect-motel",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "39ad1df193734b2ba832baafbbcd883f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(VBox(children=(Label(value='Workspace', style=DescriptionStyle(description_width='initial')), H…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from geoapps.inversion.potential_fields.application import InversionApp\n",
    "\n",
    "app = InversionApp(geoh5=r\"../../../assets/FlinFlon.geoh5\")\n",
    "app()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adolescent-found",
   "metadata": {},
   "source": [
    "## Project Selection\n",
    "\n",
    "Select and connect to an existing **geoh5** project file containing data\n",
    "\n",
    "OR \n",
    "\n",
    "Select a `*.ui.json` input file to re-load parameters from. See the [Input ui.json](#Input-ui.json) section for details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "forbidden-newark",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b95d1e3f15ab43b2af6d2007329dfff4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Label(value='Workspace', style=DescriptionStyle(description_width='initial')), HBox(children=(F…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "app.project_panel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "comprehensive-revelation",
   "metadata": {},
   "source": [
    "See the [Project Panel](base_application.ipynb#Project-Panel) page for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d470b85c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "specialized-visit",
   "metadata": {},
   "source": [
    "## Survey Selection\n",
    "\n",
    "### Object\n",
    "\n",
    "List of objects available containing geophysical data to be inverted. Currently supports objects of type `Points`, `Curve`, `Surface` and `Grid2D`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "contrary-english",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5b51e988d7ff4a55a6f1a38c8e0a80ce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Object:', index=7, options=(['', None], ['Labels/Label3', UUID('39b2d390-6063-4463-a454-…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "app.objects"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ruled-characteristic",
   "metadata": {},
   "source": [
    "### Inversion Type\n",
    "\n",
    "List of available inversion types. \n",
    "\n",
    "- **gravity**: Invert bouguer corrected (mGal) or tensor (Etvos) data for the recovery of residual density (g/cc).\n",
    "- **magnetic scalar**: Invert magnetic data for the recovery of scalar susceptibility (SI).\n",
    "- **magnetic vector**: Invert magnetic data for the recovery of 3-component magnetization (effective susceptibility + angles)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "activated-retirement",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "05265d62537744149f7e6ad602e03c59",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Dropdown(description='inversion Type:', options=('magnetic vector', 'magnetic scalar', 'gravity…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "app.survey_type_panel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accomplished-voice",
   "metadata": {},
   "source": [
    "#### Inducing field parameters\n",
    "\n",
    "For magnetic data only, set the inducing field parameters:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "angry-origin",
   "metadata": {},
   "source": [
    "## Data Channel Options\n",
    "\n",
    "Define the geophysical data to be inverted."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "final-seeker",
   "metadata": {},
   "source": [
    "### Channel Options\n",
    "\n",
    "Set parameters to invividual channels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "baking-running",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4cc3c97af62248b486ece794865e5965",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Checkbox(value=True, description='Active', indent=False, style=DescriptionStyle(description_wid…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "app.data_channel_choices.data_channel_options['tmi']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5181737f",
   "metadata": {},
   "source": [
    "#### Active\n",
    "\n",
    "Checked if the channel is to be used in the inversion\n",
    "\n",
    "#### Channel:\n",
    "\n",
    "Association between the \"data channel\" and one of the expected \"system channel\".\n",
    "\n",
    "#### Uncertainty floor\n",
    "\n",
    "Estimated data uncertainty floor (constant) value associated with the data channel. \n",
    "\n",
    "#### Uncertainty channel\n",
    "\n",
    "Optionally, use a data channel to assign point-based uncertainties. Uncertainty floor value is ignored if a channel of uncertainties is selected."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "defined-princeton",
   "metadata": {},
   "source": [
    "## Window Selection\n",
    "\n",
    "Manual selection of an area of interest and data resolution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "opponent-canada",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3bfd68ba6b13452cbf32f15ca1e380ee",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(VBox(children=(FloatText(value=50.0, description='Grid Resolution (m)', style=DescriptionStyle(…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "app.window_selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "surface-rider",
   "metadata": {},
   "source": [
    "See the [Map View Selection](view_selection.ipynb#Map-View-Selection) page for more details."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "artificial-madness",
   "metadata": {},
   "source": [
    "## Spatial information"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "earlier-verse",
   "metadata": {},
   "source": [
    "### Topography\n",
    "\n",
    "Set the air/ground interface of the inversion model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "robust-prior",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0f053aba21644a5e9dce126fb42103a6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(RadioButtons(description='Define by:', index=1, options=('None', 'Object', 'Relative to Sensor'…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "app.topography_group()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "atmospheric-globe",
   "metadata": {},
   "source": [
    "#### Object\n",
    "\n",
    "Set the topography based on an object stored in the target `geoh5` project. The Z value of the cells/vertices can be assigned based on a chosen `Data` field.\n",
    "\n",
    "#### Relative to Sensor\n",
    "\n",
    "Topography is defined by a fixed vertical offset from a selected object position (vertices or centroids), also referred to as a \"draped height\".\n",
    "\n",
    "#### Constant\n",
    "\n",
    "Topography is defined as a flat surface with constant elevation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "governing-emperor",
   "metadata": {},
   "source": [
    "### Sensor Location\n",
    "\n",
    "Defines the sensors position in 3D space. The vertical position can be referenced to:\n",
    "- **sensor location**: Use the target [object](#Object) vertices or centroids z-locations\n",
    "- **topo + radar**: Interpolate the topography to set the elevation. Typically used for gridded\n",
    "    data with constant draped height or for airborne survey with inaccurate GPS\n",
    "    elevation (see [radar channel](#Radar-channel-(optional)))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "threatened-athens",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2d1804bac8df47b68873ba2c7c5b31f4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Checkbox(value=True, description='Set Z from topo + offsets', style=DescriptionStyle(descriptio…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "app.sensor()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "817f66fd",
   "metadata": {},
   "source": [
    "#### Constant offsets (dx, dy, dz)\n",
    "\n",
    "Sensor position shifted by a constant offset from the vertices of the selected\n",
    "object. Typically used for towed systems where the GPS receiver is on the\n",
    "aircraft.\n",
    "\n",
    "- dx: East(+) or West(-) offset.\n",
    "- dy: North(+) or South(-) offset\n",
    "- dz: Up(+) or Down(-) offset\n",
    "\n",
    "#### Radar channel (optional)\n",
    "\n",
    "Channel defining the ground clearance (radar) of each data point."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "victorian-tiffany",
   "metadata": {},
   "source": [
    "## Inversion Options\n",
    "\n",
    "List of parameters controlling the inversion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "incorrect-bidding",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cbd6beda1c6b4f8da911bd5bc7c3b616",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(options=('starting model', 'mesh', 'reference model', 'regularization', 'upper-lower bounds', 'detren…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "app.option_choices"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "traditional-latino",
   "metadata": {},
   "source": [
    "### Starting Model\n",
    "\n",
    "Initial model used to begin the inversion.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "attractive-canvas",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4a34e96070d34371815a0c288062636a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Label(value='Starting effective susceptibility', style=DescriptionStyle(description_width='init…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "app._starting_model_group()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "authentic-payroll",
   "metadata": {},
   "source": [
    "#### Model\n",
    "\n",
    "Model object and values selected from any `Points`, `Curve`, `Surface`, `BlockModel` or `Octree` object.\n",
    "Values are interpolated onto the inversion mesh using a nearest neighbor approach.\n",
    "\n",
    "#### Constant\n",
    "\n",
    "Constant background half-space value."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "spiritual-coating",
   "metadata": {},
   "source": [
    "## Mesh\n",
    "\n",
    "Select an existing `Octree` mesh object from the target geoh5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "committed-genealogy",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "db2151d0c1f3408899559a06d2aa794d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Object:', index=1, options=(['', None], ['Workspace/Octree Mesh', UUID('a8f3b369-10bd-4c…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "app._mesh_octree.objects"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "handed-journalism",
   "metadata": {},
   "source": [
    "## Regularization Panel\n",
    "\n",
    "Parameters controlling the regularization function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "linear-climb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "22753e23ff754145ac23866661829d8d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(VBox(children=(Label(value='Scaling (alphas)'), FloatText(value=1.0, description='Reference Mod…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "app.inversion_options['regularization']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "russian-evidence",
   "metadata": {},
   "source": [
    "### Reference model\n",
    "\n",
    "Reference model values used to constrain the inversion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2ba7940b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9dc06ef88d14476097a8362ad1852697",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Label(value='Reference effective susceptibility', style=DescriptionStyle(description_width='ini…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "app._reference_model_group()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "obvious-advertiser",
   "metadata": {},
   "source": [
    "#### None\n",
    "\n",
    "No reference model used. This is equivalent as setting the [Alphas](#Scaling-(alpha)-parameters) `s` parameter to zero.\n",
    "\n",
    "\n",
    "#### Model\n",
    "\n",
    "Reference model selected from any `Points`, `Curve`, `Surface`, `BlockModel` or `Octree` object.\n",
    "Values are interpolated onto the inversion mesh using a nearest neighbor approach.\n",
    "\n",
    "#### Constant\n",
    "\n",
    "Constant reference half-space value."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "differential-fifth",
   "metadata": {},
   "source": [
    "### Scaling (alpha) parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "metric-southwest",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e0a823d792254b428df583487c03810c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Label(value='Scaling (alphas)'), FloatText(value=1.0, description='Reference Model (s)', style=…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "app._alphas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "empty-meeting",
   "metadata": {},
   "source": [
    "Scaling between the components of the regularization function.\n",
    "\n",
    "See the [SimPEG.API](https://docs.simpeg.xyz/content/api_core/api_Regularization.html#SimPEG.regularization.Simple) for technical details."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "intimate-fisher",
   "metadata": {},
   "source": [
    "### $l_p$-norms\n",
    "\n",
    "Approximated norms applied to the components of the regularization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "potential-fifth",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a0ef0355fa4549b0bf7a862607fa9589",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Label(value='Lp-norms'), FloatText(value=0.0, style=DescriptionStyle(description_width='initial…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "app._norms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "middle-sweet",
   "metadata": {},
   "source": [
    "See notes on [Sparse and Blocky Norms](https://giftoolscookbook.readthedocs.io/en/latest/content/fundamentals/Norms.html#sparse-and-blocky-norms) for technical details."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pacific-realtor",
   "metadata": {},
   "source": [
    "### Upper-Lower Bounds\n",
    "\n",
    "Upper and lower bound constraints applied on physical property model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "liable-license",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0583bf4807b9424583df8af9f94ae19c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(VBox(children=(Label(value='Lower Bounds'), VBox(children=(Label(value='', style=DescriptionSty…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "app.bound_panel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "amber-migration",
   "metadata": {},
   "source": [
    "Bounds can be defined as either:\n",
    "- Model: Values defined on a cell-by-cell basis. The values are projected onto the inversion mesh using a nearest neighour interpolation. \n",
    "- Constant: Constant value defining the bounds.\n",
    "- None: The inversion uses [$-\\infty$, $\\infty$] as physical property bounds."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73052aee",
   "metadata": {},
   "source": [
    "### Detrend (optional)\n",
    "\n",
    "Remove a polynomial trend from the selected data channels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ab495d1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<geoh5py.workspace.workspace.Workspace at 0x19ce3413df0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app.workspace\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3932cae2",
   "metadata": {
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "app.detrend_type.value = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "256b9331",
   "metadata": {},
   "outputs": [
    {
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    "#### Order\n",
    "\n",
    "Order [0, 1 or 2] of the polynomial defining the trend. The 0th order trend corresponds to removing the mean value.\n",
    "\n",
    "#### Method\n",
    "\n",
    "- all: Use all data points to compute the trend\n",
    "- perimeter: Use only the edge points to compute the trend."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "preliminary-element",
   "metadata": {},
   "source": [
    "### Ignore Values"
   ]
  },
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   "cell_type": "code",
   "execution_count": 20,
   "id": "lesbian-crawford",
   "metadata": {},
   "outputs": [
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   "id": "b82c2cb8",
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    "Flag value ignored by the inversion by assigning $\\infty$ uncertainties on the data points."
   ]
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   "cell_type": "markdown",
   "id": "flying-style",
   "metadata": {},
   "source": [
    "### Optimization\n",
    "\n",
    "Parameters controlling various aspects of the projected Gauss-Newton inversion algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "junior-devices",
   "metadata": {},
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   "id": "pleased-respondent",
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    "#### Max beta Iterations\n",
    "\n",
    "Maximum number of $\\beta$-iterations allowed.\n",
    "Note that when applying sparse norms, the inversion may require $>20$ iterations to converge.\n",
    "\n",
    "\n",
    "#### Target misfit\n",
    "\n",
    "Target data misfit where $\\chi=1$ corresponds to $\\phi_d=N$ (number of data). (See documentation on [Data Misfit and Uncertainties](https://giftoolscookbook.readthedocs.io/en/latest/content/fundamentals/Uncertainties.html#data-misfit-and-uncertainties) for mathematical details)\n",
    "\n",
    "\n",
    "#### Starting trade-off ($\\beta$)\n",
    "\n",
    "**ratio**: Factor multiplying the initial $\\beta$ defined by the ratio between the initial misfit and regularization:\n",
    "\n",
    "\\begin{equation}\n",
    "\\beta_0 = \\gamma * \\frac{\\phi_d}{ \\phi_m}\n",
    "\\end{equation}\n",
    "\n",
    "**value**: Fixed $\\beta$ value specified by the user.\n",
    "\n",
    "#### Max CG Iterations\n",
    "\n",
    "Maximum number of Conjugate Gradient (CG) iterations per Gauss-Newton solve.\n",
    "\n",
    "\n",
    "#### CG Tolerance\n",
    "\n",
    "Threshold on the minimum Conjugate Gradient (CG) step to end the Gauss-Newton solve.\n",
    "\n",
    "\n",
    "#### Max CPUs\n",
    "\n",
    "Maximum number of threads used for the parallelization. Defaults to half the system thread count.\n",
    "\n",
    "\n",
    "#### Storage device\n",
    "\n",
    "Option to store the sensitivities in memory (RAM) or in chunks on a solid-state drive (disk). The **RAM** execution will be faster but limits the problem size to the available memory. The **disk** option will be slower as it depends on the read-write speed of the SSD, but the memory requirement is reduced (2-3 times the chunk size times the number of threads). This option permits much larger inversions than the amount RAM available on a machine.\n",
    "\n",
    "\n",
    "#### Number of tiles\n",
    "\n",
    "Number of data tiles used by the mesh decoupling algorithm. Nested sensitivities for each data blocks are interpolated to the global inversion mesh with a volumetric weighted averaging scheme."
   ]
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   "source": [
    "## Output panel\n",
    "\n",
    "Setup the inversion and launch the process."
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  },
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   "id": "executive-revolution",
   "metadata": {},
   "source": [
    "### Output Name"
   ]
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   "id": "359adb87",
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    "Name given to the inversion group added to the Geoscience ANALYST project."
   ]
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  {
   "cell_type": "markdown",
   "id": "fbc59fa0",
   "metadata": {},
   "source": [
    "### Working directory"
   ]
  },
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   "execution_count": 23,
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    "Specify the working directory where the inversion occurs. \n",
    "\n",
    "**It is highly recommended to chose a location on a solid-state drive (SSD) to fully take advantage of the `Dask` parallelization.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af703f0a",
   "metadata": {},
   "source": [
    "### Write input"
   ]
  },
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   "execution_count": 24,
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   "metadata": {
    "scrolled": true
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   "source": [
    "\n",
    "Click to write the input parameters to a `*.json` file and a workspace (.geoh5) with all required entities. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff5ce976",
   "metadata": {},
   "source": [
    "### Run Inversion: "
   ]
  },
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   "cell_type": "markdown",
   "id": "08216fba",
   "metadata": {},
   "source": [
    "Launch the inversion routine. Results will be written directly to the target `geoh5` project."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cfe2258",
   "metadata": {},
   "source": [
    "## Input ui.json \n",
    "\n",
    "This application relies on a [structured json](https://github.com/MiraGeoscience/geoapps/tree/main/assets/uijson) file to store the parameters and run the program.\n",
    "The `ui.json` can be used to re-load the parameters from a previous run by selecting the `ui.json` file from the [Project Selection](#Project-Selection) widget, instead of a `geoh5` file. \n",
    "\n",
    "\n",
    "### Command line execution\n",
    "\n",
    "The same input `ui.json` file can be used to run the program from command line:\n",
    "\n",
    "```\n",
    "activate geoapps\n",
    "python -m geoapps.inversion.driver [Name].ui.json\n",
    "```\n",
    "\n",
    "This assumes that the geoapps have been installed and that the reference `*.ui.json` and `*.geoh5` project are accessible in the current directory."
   ]
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   "cell_type": "markdown",
   "id": "db791f15",
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   "source": [
    "### Geoscience ANALYST Pro v3.4\n",
    "\n",
    "Geoscience ANALYST Pro users (v3.4) can execute this application directly from an active session with a drag & drop of a `*.ui.json` file to the `Viewport`. \n",
    "\n",
    "![](images/GA_pro_grav_mag_inversion.gif \"ga_pro\")"
   ]
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   "execution_count": 26,
   "id": "hairy-seeking",
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    "import matplotlib.pyplot as plt\n",
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     "4b3ad266172d41f29e0b9d82eb360ec2": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "ToggleButtonModel",
      "state": {
       "layout": "IPY_MODEL_b2da7a5a10bb48608066341460983225",
       "style": "IPY_MODEL_cfde9a3eb89246b99411eff48970465b"
      }
     },
     "4b3e37898fc642b5a4d08fcce6041f30": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "ButtonStyleModel",
      "state": {}
     },
     "4b459532c99a44e3812b1bccc4de3f44": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": "initial"
      }
     },
     "4b498abfe0b24c0ba5a50bbcc8c68d33": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "4b4aa6bb222f4cf7be9d940602fad701": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "4b54ac8d3cb1402b8137611f679e8660": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_f49e07111c6840178cb917d9f4be0078",
       "outputs": [
        {
         "data": {
          "image/png": 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          "text/plain": "<Figure size 720x720 with 1 Axes>"
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      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_aaf055900bfb43f1a957048d573bc52c",
       "outputs": [
        {
         "data": {
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