{
"cells": [
{
"cell_type": "markdown",
"id": "particular-concept",
"metadata": {},
"source": [
"# Direct-Current and Induced Polarization (DC-IP) 3D Inversion\n",
"\n",
"\n",
"\n",
"This application provides an interface to the open-source [SimPEG](https://simpeg.xyz/) package for the inversion of direct-current (DC) and induced polarization (IP) data. \n",
"\n",
" - Direct-current potential data (V) inversion for the recovery of conductivity (S/m).\n",
" - Secondary potentials data (V) for the recovery of chargeability (mV/V).\n",
"\n",
"All inversion are performed on a 3D octree mesh.\n",
"\n",
"New user? Visit the [**Getting Started**](https://geoapps.readthedocs.io/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": "12545fb69e7047ea9484b45b4240723a",
"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.electricals.application import InversionApp\n",
"\n",
"app = InversionApp(geoh5=r\"../../../assets/FlinFlon_dcip.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": "7e1e894337dd4b2084379673fb98494a",
"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": "markdown",
"id": "specialized-visit",
"metadata": {},
"source": [
"## Survey Selection\n",
"\n",
"### Object\n",
"\n",
"List of `PotentialElectrode` surveys available in the target geoh5 that contains data to be inverted."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "contrary-english",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "755de22888e1480d9e53b608efc60987",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Dropdown(description='Object:', index=1, options=(['', None], ['Workspace/DC_Survey', UUID('6e14de2c-9c2f-4976…"
]
},
"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",
"- **direct current**: Invert potential (volt) data data for the recovery of conductivity (S/m)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "activated-retirement",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a22625b6565048348f9da8276c2f04e5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(Dropdown(description='inversion Type:', options=('direct current 3d', 'induced polarization 3d'…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"app.survey_type_panel"
]
},
{
"cell_type": "markdown",
"id": "angry-origin",
"metadata": {},
"source": [
"## Data Channel Options\n",
"\n",
"Define the geophysical data to be inverted."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "1de2a192",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7cd23396d3924a4599d2213fc5289725",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Dropdown(description='Component:', options=('potential',), style=DescriptionStyle(description_width='initial')…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"app.data_channel_choices"
]
},
{
"cell_type": "markdown",
"id": "final-seeker",
"metadata": {},
"source": [
"### Channel Options\n",
"\n",
"Set parameters to invividual channels."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "baking-running",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d3a11bb57ecd46949b7de3b53683d089",
"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['potential']"
]
},
{
"cell_type": "markdown",
"id": "annoying-comedy",
"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",
"Optionaly, 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": 7,
"id": "opponent-canada",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a21cb88c5c6e4158985f2216e95bc42e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(VBox(children=(FloatText(value=0.0, description='Grid Resolution (m)', disabled=True, style=Des…"
]
},
"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": 8,
"id": "robust-prior",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "288b1481add34b438959da5ed6dd68de",
"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."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "threatened-athens",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d889be3942234b14980df18dea7c30ff",
"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": "expired-lesbian",
"metadata": {},
"source": [
"#### Set Z from topo + offset\n",
"\n",
"Recommended option for most surface surveys to enforce contact of the electrodes with the active ground cells. Only consider unselecting for borehole measurements. \n",
"\n",
"#### Constant offsets (dx, dy, dz)\n",
"\n",
"Sensor position shifted by a constant offset from the vertices of the selected\n",
"object.\n",
"\n",
"- dx: East(+) or West(-) offset.\n",
"- dy: North(+) or South(-) offset\n",
"- dz: Up(+) or Down(-) offset"
]
},
{
"cell_type": "markdown",
"id": "victorian-tiffany",
"metadata": {},
"source": [
"## Inversion Options\n",
"\n",
"List of parameters controlling the inversion."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "incorrect-bidding",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5c62d901d6734d89bbfdb0f7ed4cbdcc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Dropdown(options=('starting model', 'mesh', 'reference model', 'regularization', 'upper-lower bounds', 'ignore…"
]
},
"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": 11,
"id": "attractive-canvas",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2a4f89a5cea4449abbf95284471b910c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(Label(value='Starting conductivity', style=DescriptionStyle(description_width='initial')), VBox…"
]
},
"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": 12,
"id": "committed-genealogy",
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8715db203b55421c8a416c1d53c30d5e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Dropdown(description='Object:', index=1, options=(['', None], ['Workspace/DC_mesh', UUID('da109284-aa8c-4824-a…"
]
},
"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": 13,
"id": "linear-climb",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "922ea2ee98464aa09c04f7804143fc11",
"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": 14,
"id": "2ba7940b",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4b6bf74bba104641a7e0d270b416a4b6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(Label(value='Reference conductivity', style=DescriptionStyle(description_width='initial')), VBo…"
]
},
"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",
"#### Model\n",
"\n",
"\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": 15,
"id": "metric-southwest",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b84668e06d814f308b724cfc9be9a92c",
"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": 16,
"id": "potential-fifth",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "565951057ade423190415d8410a14ecb",
"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\n"
]
},
{
"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": 17,
"id": "liable-license",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0ab0a5fa865c4d039732c9a4d8441dde",
"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": "preliminary-element",
"metadata": {},
"source": [
"### Ignore Values"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "lesbian-crawford",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a639412bbc1844319f12318c212ab5b6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Text(value='', description=\"Value (i.e. '<0' for no negatives)\", style=DescriptionStyle(description_width='ini…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"app.ignore_values"
]
},
{
"cell_type": "markdown",
"id": "b82c2cb8",
"metadata": {},
"source": [
"Flag value ignored by the inversion by assigning $\\infty$ uncertainties on the data points."
]
},
{
"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": 19,
"id": "junior-devices",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "16e8b553686c40c9be7ec3cf5a2d7a01",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(IntText(value=25, description='Max beta Iterations', style=DescriptionStyle(description_width='…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"app.optimization"
]
},
{
"cell_type": "markdown",
"id": "pleased-respondent",
"metadata": {},
"source": [
"#### 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",
"#### 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",
"#### 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.\n"
]
},
{
"cell_type": "markdown",
"id": "smart-notebook",
"metadata": {},
"source": [
"## Output panel\n",
"\n",
"Setup the inversion and launch the process."
]
},
{
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"id": "executive-revolution",
"metadata": {},
"source": [
"### Output Name"
]
},
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"cell_type": "code",
"execution_count": 20,
"id": "referenced-buffalo",
"metadata": {},
"outputs": [
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"source": [
"app.ga_group_name"
]
},
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"id": "359adb87",
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"source": [
"Name given to the inversion group added to the Geoscience ANALYST project."
]
},
{
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"id": "fbc59fa0",
"metadata": {},
"source": [
"### Working directory"
]
},
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"execution_count": 21,
"id": "3fa0113e",
"metadata": {
"scrolled": true
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},
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"id": "5d23ac07",
"metadata": {},
"source": [
"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"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "43a2716d",
"metadata": {
"scrolled": true
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"metadata": {},
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"app.write"
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"cell_type": "markdown",
"id": "341c1ed4",
"metadata": {},
"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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"execution_count": 23,
"id": "a7ae6328",
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"metadata": {},
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],
"source": [
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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."
]
},
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"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",
"\n",
"python -m geoapps.inversion.driver [Name].ui.json\n",
"```\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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"id": "db791f15",
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"### 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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]
}
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\n",
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