From c3f497d45144d5a88d3309a712e4f5f004f05d67 Mon Sep 17 00:00:00 2001 From: Santiago Soler Date: Tue, 19 Nov 2024 11:41:09 -0800 Subject: [PATCH 1/2] Add Thibaut's notebook on weighting strategies --- _toc.yml | 1 + .../03-gravity/weighting_strategies.ipynb | 1231 +++++++++++++++++ 2 files changed, 1232 insertions(+) create mode 100644 notebooks/03-gravity/weighting_strategies.ipynb diff --git a/_toc.yml b/_toc.yml index 7135792b..398a8c57 100644 --- a/_toc.yml +++ b/_toc.yml @@ -25,6 +25,7 @@ chapters: - file: notebooks/03-gravity/fwd_gravity_anomaly_3d - file: notebooks/03-gravity/fwd_gravity_gradiometry_3d - file: notebooks/03-gravity/inv_gravity_anomaly_3d + - file: notebooks/03-gravity/weighting_strategies # MAGNETICS - file: notebooks/magnetics_index sections: diff --git a/notebooks/03-gravity/weighting_strategies.ipynb b/notebooks/03-gravity/weighting_strategies.ipynb new file mode 100644 index 00000000..6ddf2c34 --- /dev/null +++ b/notebooks/03-gravity/weighting_strategies.ipynb @@ -0,0 +1,1231 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "ec476457", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "c2deb286-8394-4483-ae62-54ba31148747", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "# Compare weighting strategy with Inversion of surface Gravity Anomaly Data" + ] + }, + { + "cell_type": "markdown", + "id": "fdfd8c76-e906-436e-b17a-a0ce8a151ed8", + "metadata": {}, + "source": [ + "```{admonition} Intermediate notebook\n", + ":class: caution\n", + "This tutorial focusses on intermediate level functionality within SimPEG. Basic functionality within SimPEG is not discussed in detail, as we assume the user is already familiar. \n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "3cd4cceb-d136-43cd-97db-9cd9fa3a526e", + "metadata": {}, + "source": [ + "```{admonition} Light-weight notebook\n", + ":class: hint\n", + "This tutorial requires minimal computational resources and can be executed quickly in the background while other computer processes are running.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "5fac71d9-0f76-44f0-9f83-7f882eb04db7", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "Here we invert gravity anomaly data to recover a density contrast model. We formulate the inverse problem as an iteratively\n", + "re-weighted least-squares (IRLS) optimization problem. For this tutorial, we\n", + "focus on the following:\n", + "\n", + "1. Setting regularization weights\n", + "2. Defining the survey from xyz formatted data\n", + "3. Generating a mesh based on survey geometry\n", + "4. Including surface topography\n", + "5. Defining the inverse problem (data misfit, regularization, optimization)\n", + "6. Specifying directives for the inversion\n", + "7. Setting sparse and blocky norms\n", + "8. Plotting the recovered model and data misfit\n", + "\n", + "Although we consider gravity anomaly data in this tutorial, the same approach\n", + "can be used to invert gradiometry and other types of geophysical data." + ] + }, + { + "cell_type": "markdown", + "id": "8405a4b2", + "metadata": { + "cell_marker": "#########################################################################" + }, + "source": [ + "Import modules\n", + "--------------\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "65cebf5c", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-19T19:21:59.611119Z", + "iopub.status.busy": "2024-11-19T19:21:59.610702Z", + "iopub.status.idle": "2024-11-19T19:21:59.615527Z", + "shell.execute_reply": "2024-11-19T19:21:59.614876Z", + "shell.execute_reply.started": "2024-11-19T19:21:59.611092Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "import tarfile\n", + "\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from discretize import TensorMesh\n", + "from discretize.utils import active_from_xyz\n", + "\n", + "from simpeg import (\n", + " data,\n", + " data_misfit,\n", + " directives,\n", + " inverse_problem,\n", + " inversion,\n", + " maps,\n", + " optimization,\n", + " regularization,\n", + " utils,\n", + ")\n", + "from simpeg.potential_fields import gravity\n", + "from simpeg.utils import model_builder, plot2Ddata" + ] + }, + { + "cell_type": "markdown", + "id": "3a797b69", + "metadata": { + "cell_marker": "#############################################" + }, + "source": [ + "Define File Names\n", + "-----------------\n", + "\n", + "File paths for assets we are loading. To set up the inversion, we require\n", + "topography and field observations. The true model defined on the whole mesh\n", + "is loaded to compare with the inversion result. These files are stored as a\n", + "tar-file on our google cloud bucket:\n", + "\"https://storage.googleapis.com/simpeg/doc-assets/gravity.tar.gz\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ada59e49", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-19T19:22:09.874564Z", + "iopub.status.busy": "2024-11-19T19:22:09.873880Z", + "iopub.status.idle": "2024-11-19T19:22:10.081568Z", + "shell.execute_reply": "2024-11-19T19:22:10.080712Z", + "shell.execute_reply.started": "2024-11-19T19:22:09.874472Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading https://storage.googleapis.com/simpeg/doc-assets/gravity.tar.gz\n", + " saved to: /home/santi/git/user-tutorials/notebooks/03-gravity/gravity.tar.gz\n", + "Download completed!\n" + ] + } + ], + "source": [ + "# storage bucket where we have the data\n", + "data_source = \"https://storage.googleapis.com/simpeg/doc-assets/gravity.tar.gz\"\n", + "\n", + "# download the data\n", + "downloaded_data = utils.download(data_source, overwrite=True)\n", + "\n", + "# unzip the tarfile\n", + "tar = tarfile.open(downloaded_data, \"r\")\n", + "tar.extractall()\n", + "tar.close()\n", + "\n", + "# path to the directory containing our data\n", + "dir_path = downloaded_data.split(\".\")[0] + os.path.sep\n", + "\n", + "# files to work with\n", + "topo_filename = dir_path + \"gravity_topo.txt\"\n", + "data_filename = dir_path + \"gravity_data.obs\"" + ] + }, + { + "cell_type": "markdown", + "id": "88e7a05a", + "metadata": { + "cell_marker": "#############################################" + }, + "source": [ + "Load Data and Plot\n", + "------------------\n", + "\n", + "Here we load and plot synthetic gravity anomaly data. Topography is generally\n", + "defined as an (N, 3) array. Gravity data is generally defined with 4 columns:\n", + "x, y, z and data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "155ed12a", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-19T19:22:24.668012Z", + "iopub.status.busy": "2024-11-19T19:22:24.667553Z", + "iopub.status.idle": "2024-11-19T19:22:25.901284Z", + "shell.execute_reply": "2024-11-19T19:22:25.900522Z", + "shell.execute_reply.started": "2024-11-19T19:22:24.667972Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load topography\n", + "xyz_topo = np.loadtxt(str(topo_filename))\n", + "\n", + "# Load field data\n", + "dobs = np.loadtxt(str(data_filename))\n", + "\n", + "# Define receiver locations and observed data\n", + "receiver_locations = dobs[:, 0:3]\n", + "dobs = dobs[:, -1]\n", + "\n", + "# Plot\n", + "mpl.rcParams.update({\"font.size\": 12})\n", + "fig = plt.figure(figsize=(7, 5))\n", + "\n", + "ax1 = fig.add_axes([0.1, 0.1, 0.73, 0.85])\n", + "plot2Ddata(\n", + " receiver_locations,\n", + " dobs,\n", + " ax=ax1,\n", + " contourOpts={\"cmap\": \"bwr\"},\n", + " shade=True,\n", + " nx=20,\n", + " ny=20,\n", + " dataloc=True,\n", + ")\n", + "ax1.set_title(\"Gravity Anomaly\")\n", + "ax1.set_xlabel(\"x (m)\")\n", + "ax1.set_ylabel(\"y (m)\")\n", + "\n", + "ax2 = fig.add_axes([0.8, 0.1, 0.03, 0.85])\n", + "norm = mpl.colors.Normalize(vmin=-np.max(np.abs(dobs)), vmax=np.max(np.abs(dobs)))\n", + "cbar = mpl.colorbar.ColorbarBase(\n", + " ax2, norm=norm, orientation=\"vertical\", cmap=mpl.cm.bwr, format=\"%.1e\"\n", + ")\n", + "cbar.set_label(\"$mGal$\", rotation=270, labelpad=15, size=12)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e6724e96", + "metadata": { + "cell_marker": "#############################################" + }, + "source": [ + "Assign Uncertainties\n", + "--------------------\n", + "\n", + "Inversion with simpeg requires that we define the standard deviation of our data.\n", + "This represents our estimate of the noise in our data. For a gravity inversion,\n", + "a constant floor value is generally applied to all data. For this tutorial,\n", + "the standard deviation on each datum will be 1% of the maximum observed\n", + "gravity anomaly value.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b8033e7d", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-19T19:22:28.792833Z", + "iopub.status.busy": "2024-11-19T19:22:28.792520Z", + "iopub.status.idle": "2024-11-19T19:22:28.797038Z", + "shell.execute_reply": "2024-11-19T19:22:28.796183Z", + "shell.execute_reply.started": "2024-11-19T19:22:28.792807Z" + } + }, + "outputs": [], + "source": [ + "maximum_anomaly = np.max(np.abs(dobs))\n", + "\n", + "uncertainties = 0.01 * maximum_anomaly * np.ones(np.shape(dobs))" + ] + }, + { + "cell_type": "markdown", + "id": "9e1b7b61", + "metadata": { + "cell_marker": "#############################################" + }, + "source": [ + "Defining the Survey\n", + "-------------------\n", + "\n", + "Here, we define the survey that will be used for this tutorial. Gravity\n", + "surveys are simple to create. The user only needs an (N, 3) array to define\n", + "the xyz locations of the observation locations. From this, the user can\n", + "define the receivers and the source field.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "bfd7d653", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-19T19:22:30.220187Z", + "iopub.status.busy": "2024-11-19T19:22:30.219716Z", + "iopub.status.idle": "2024-11-19T19:22:30.227337Z", + "shell.execute_reply": "2024-11-19T19:22:30.225970Z", + "shell.execute_reply.started": "2024-11-19T19:22:30.220142Z" + } + }, + "outputs": [], + "source": [ + "# Define the receivers. The data consists of vertical gravity anomaly measurements.\n", + "# The set of receivers must be defined as a list.\n", + "receiver_list = gravity.receivers.Point(receiver_locations, components=\"gz\")\n", + "\n", + "receiver_list = [receiver_list]\n", + "\n", + "# Define the source field\n", + "source_field = gravity.sources.SourceField(receiver_list=receiver_list)\n", + "\n", + "# Define the survey\n", + "survey = gravity.survey.Survey(source_field)" + ] + }, + { + "cell_type": "markdown", + "id": "ac514c26", + "metadata": { + "cell_marker": "#############################################" + }, + "source": [ + "Defining the Data\n", + "-----------------\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 deviation.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d64e3ec9", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-19T19:22:31.735358Z", + "iopub.status.busy": "2024-11-19T19:22:31.735024Z", + "iopub.status.idle": "2024-11-19T19:22:31.739003Z", + "shell.execute_reply": "2024-11-19T19:22:31.738263Z", + "shell.execute_reply.started": "2024-11-19T19:22:31.735321Z" + } + }, + "outputs": [], + "source": [ + "data_object = data.Data(survey, dobs=dobs, standard_deviation=uncertainties)\n" + ] + }, + { + "cell_type": "markdown", + "id": "cbb7ff9e", + "metadata": { + "cell_marker": "#############################################" + }, + "source": [ + "Defining a Tensor Mesh\n", + "----------------------\n", + "\n", + "Here, we create the tensor mesh that will be used to invert gravity anomaly\n", + "data. If desired, we could define an OcTree mesh.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "38f77c49", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-19T19:22:32.977725Z", + "iopub.status.busy": "2024-11-19T19:22:32.977137Z", + "iopub.status.idle": "2024-11-19T19:22:32.988191Z", + "shell.execute_reply": "2024-11-19T19:22:32.986251Z", + "shell.execute_reply.started": "2024-11-19T19:22:32.977699Z" + } + }, + "outputs": [], + "source": [ + "dh = 5.0\n", + "hx = [(dh, 5, -1.3), (dh, 40), (dh, 5, 1.3)]\n", + "hy = [(dh, 5, -1.3), (dh, 40), (dh, 5, 1.3)]\n", + "hz = [(dh, 5, -1.3), (dh, 15)]\n", + "mesh = TensorMesh([hx, hy, hz], \"CCN\")" + ] + }, + { + "cell_type": "markdown", + "id": "205ac551", + "metadata": { + "cell_marker": "########################################################" + }, + "source": [ + "Starting/Reference Model and Mapping on Tensor Mesh\n", + "---------------------------------------------------\n", + "\n", + "Here, we 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.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "bb2d47a0", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-19T19:22:35.511425Z", + "iopub.status.busy": "2024-11-19T19:22:35.511094Z", + "iopub.status.idle": "2024-11-19T19:22:35.578547Z", + "shell.execute_reply": "2024-11-19T19:22:35.577831Z", + "shell.execute_reply.started": "2024-11-19T19:22:35.511399Z" + } + }, + "outputs": [], + "source": [ + "# Find the indices of the active cells in forward model (ones below surface)\n", + "ind_active = active_from_xyz(mesh, xyz_topo)\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 active cell\n", + "\n", + "# Define and plot starting model\n", + "starting_model = np.zeros(nC)\n" + ] + }, + { + "cell_type": "markdown", + "id": "0afc47f8", + "metadata": { + "cell_marker": "##############################################" + }, + "source": [ + "Define the Physics and data misfit\n", + "----------------------------------\n", + "\n", + "Here, we define the physics of the gravity problem by using the simulation\n", + "class.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "13180cd6", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-19T19:22:43.034191Z", + "iopub.status.busy": "2024-11-19T19:22:43.033753Z", + "iopub.status.idle": "2024-11-19T19:22:43.085638Z", + "shell.execute_reply": "2024-11-19T19:22:43.084818Z", + "shell.execute_reply.started": "2024-11-19T19:22:43.034153Z" + } + }, + "outputs": [], + "source": [ + "simulation = gravity.simulation.Simulation3DIntegral(\n", + " survey=survey, mesh=mesh, rhoMap=model_map, active_cells=ind_active\n", + ")\n", + "\n", + "# 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" + ] + }, + { + "cell_type": "markdown", + "id": "9c1b0031", + "metadata": { + "cell_marker": "#######################################################################" + }, + "source": [ + "Running the Depth Weighted inversion\n", + "------------------------------------\n", + "\n", + "Here we define the directives, weights, regularization, and optimization\n", + "for a depth-weighted inversion\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "23edf0b5", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-19T19:25:38.112034Z", + "iopub.status.busy": "2024-11-19T19:25:38.111233Z", + "iopub.status.idle": "2024-11-19T19:25:58.600063Z", + "shell.execute_reply": "2024-11-19T19:25:58.599249Z", + "shell.execute_reply.started": "2024-11-19T19:25:38.111960Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Running inversion with SimPEG v0.23.0\n", + "simpeg.InvProblem will set Regularization.reference_model to m0.\n", + "simpeg.InvProblem will set Regularization.reference_model to m0.\n", + "simpeg.InvProblem will set Regularization.reference_model to m0.\n", + "simpeg.InvProblem will set Regularization.reference_model to m0.\n", + "\n", + " simpeg.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n", + " ***Done using the default solver Pardiso and no solver_opts.***\n", + " \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 9.79e+02 2.66e+05 0.00e+00 2.66e+05 2.19e+02 0 \n", + " 1 4.90e+02 3.20e+03 1.71e+01 1.16e+04 2.10e+02 0 \n", + " 2 2.45e+02 1.21e+03 1.99e+01 6.08e+03 2.05e+02 0 Skip BFGS \n", + " 3 1.22e+02 4.62e+02 2.20e+01 3.15e+03 2.00e+02 0 Skip BFGS \n", + "Reached starting chifact with l2-norm regularization: Start IRLS steps...\n", + "irls_threshold 0.059269951164308395\n", + " 4 1.22e+02 1.84e+02 4.61e+01 5.83e+03 2.07e+02 0 Skip BFGS \n", + " 5 1.50e+02 2.54e+02 5.91e+01 9.13e+03 2.14e+02 0 \n", + " 6 8.28e+01 4.71e+02 6.48e+01 5.84e+03 2.03e+02 0 \n", + " 7 1.06e+02 2.34e+02 6.70e+01 7.34e+03 2.15e+02 0 \n", + " 8 6.26e+01 4.02e+02 5.96e+01 4.13e+03 2.02e+02 0 \n", + " 9 8.14e+01 2.27e+02 5.22e+01 4.48e+03 2.14e+02 0 \n", + " 10 9.83e+01 2.62e+02 4.39e+01 4.57e+03 2.15e+02 0 \n", + " 11 6.42e+01 3.06e+02 3.79e+01 2.74e+03 1.79e+02 0 \n", + " 12 9.09e+01 1.92e+02 3.32e+01 3.21e+03 2.17e+02 0 Skip BFGS \n", + " 13 1.11e+02 2.58e+02 2.73e+01 3.28e+03 2.17e+02 0 \n", + " 14 7.22e+01 3.07e+02 2.30e+01 1.97e+03 2.04e+02 0 \n", + " 15 9.82e+01 2.08e+02 2.14e+01 2.31e+03 2.18e+02 0 Skip BFGS \n", + " 16 1.20e+02 2.57e+02 1.85e+01 2.48e+03 2.18e+02 0 \n", + " 17 7.96e+01 2.93e+02 1.64e+01 1.60e+03 2.03e+02 0 \n", + " 18 1.06e+02 2.19e+02 1.62e+01 1.93e+03 2.18e+02 0 Skip BFGS \n", + " 19 1.28e+02 2.60e+02 1.48e+01 2.16e+03 2.18e+02 0 \n", + " 20 1.49e+02 2.80e+02 1.36e+01 2.31e+03 2.18e+02 0 \n", + " 21 1.71e+02 2.86e+02 1.28e+01 2.48e+03 2.18e+02 0 Skip BFGS \n", + " 22 1.97e+02 2.90e+02 1.22e+01 2.70e+03 2.18e+02 0 \n", + " 23 1.30e+02 2.99e+02 1.18e+01 1.83e+03 1.86e+02 0 \n", + " 24 1.70e+02 2.25e+02 1.19e+01 2.25e+03 2.18e+02 0 Skip BFGS \n", + " 25 2.11e+02 2.49e+02 1.15e+01 2.67e+03 2.18e+02 0 \n", + " 26 2.52e+02 2.66e+02 1.11e+01 3.07e+03 2.18e+02 0 \n", + " 27 2.99e+02 2.72e+02 1.07e+01 3.48e+03 2.18e+02 0 \n", + " 28 3.45e+02 2.84e+02 1.05e+01 3.90e+03 2.18e+02 0 Skip BFGS \n", + " 29 3.98e+02 2.90e+02 1.02e+01 4.34e+03 2.18e+02 0 \n", + " 30 2.61e+02 3.04e+02 9.93e+00 2.89e+03 2.08e+02 0 \n", + " 31 3.35e+02 2.33e+02 1.01e+01 3.60e+03 2.18e+02 0 \n", + " 32 4.06e+02 2.60e+02 9.87e+00 4.27e+03 2.18e+02 0 \n", + " 33 4.73e+02 2.80e+02 9.67e+00 4.85e+03 2.18e+02 0 \n", + "Reach maximum number of IRLS cycles: 30\n", + "------------------------- STOP! -------------------------\n", + "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 2.6622e+04\n", + "1 : |xc-x_last| = 3.2206e-02 <= tolX*(1+|x0|) = 1.0000e-01\n", + "0 : |proj(x-g)-x| = 2.1836e+02 <= tolG = 1.0000e-01\n", + "0 : |proj(x-g)-x| = 2.1836e+02 <= 1e3*eps = 1.0000e-02\n", + "0 : maxIter = 100 <= iter = 34\n", + "------------------------- DONE! -------------------------\n" + ] + } + ], + "source": [ + "# inversion directives\n", + "# 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=1e0)\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.UpdateIRLS(\n", + " f_min_change=1e-4,\n", + " max_irls_iterations=30,\n", + " irls_cooling_factor=1.5,\n", + " misfit_tolerance=1e-2,\n", + ")\n", + "\n", + "# Options for outputting recovered models and predicted data for each beta.\n", + "save_iteration = directives.SaveOutputEveryIteration(save_txt=False)\n", + "\n", + "# Updating the preconditionner if it is model dependent.\n", + "update_jacobi = directives.UpdatePreconditioner()\n", + "\n", + "# The directives are defined as a list\n", + "directives_list = [\n", + " update_IRLS,\n", + " starting_beta,\n", + " save_iteration,\n", + " update_jacobi,\n", + "]\n", + "\n", + "# Define the regularization (model objective function) with depth weighting.\n", + "reg_dpth = regularization.Sparse(mesh, active_cells=ind_active, mapping=model_map)\n", + "reg_dpth.norms = [0, 2, 2, 2]\n", + "depth_weights = utils.depth_weighting(\n", + " mesh, receiver_locations, active_cells=ind_active, exponent=2\n", + ")\n", + "reg_dpth.set_weights(depth_weights=depth_weights)\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=-1.0, upper=1.0, maxIterLS=20, maxIterCG=10, 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_dpth, opt)\n", + "\n", + "# Here we combine the inverse problem and the set of directives\n", + "inv = inversion.BaseInversion(inv_prob, directives_list)\n", + "\n", + "# Run inversion\n", + "recovered_model_dpth = inv.run(starting_model)" + ] + }, + { + "cell_type": "markdown", + "id": "622ee100", + "metadata": { + "cell_marker": "#######################################################################" + }, + "source": [ + "Running the Distance Weighted inversion\n", + "---------------------------------------\n", + "\n", + "Here we define the directives, weights, regularization, and optimization\n", + "for a distance-weighted inversion\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "ed7d37fb", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-19T19:26:03.669388Z", + "iopub.status.busy": "2024-11-19T19:26:03.669028Z", + "iopub.status.idle": "2024-11-19T19:26:28.193798Z", + "shell.execute_reply": "2024-11-19T19:26:28.192945Z", + "shell.execute_reply.started": "2024-11-19T19:26:03.669360Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Running inversion with SimPEG v0.23.0\n", + "simpeg.InvProblem will set Regularization.reference_model to m0.\n", + "simpeg.InvProblem will set Regularization.reference_model to m0.\n", + "simpeg.InvProblem will set Regularization.reference_model to m0.\n", + "simpeg.InvProblem will set Regularization.reference_model to m0.\n", + "\n", + " simpeg.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n", + " ***Done using the default solver Pardiso and no solver_opts.***\n", + " \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 2.84e+03 2.66e+05 0.00e+00 2.66e+05 2.19e+02 0 \n", + " 1 1.42e+03 2.33e+04 1.42e+01 4.35e+04 2.17e+02 0 \n", + " 2 7.10e+02 1.04e+04 2.05e+01 2.50e+04 2.15e+02 0 Skip BFGS \n", + " 3 3.55e+02 4.20e+03 2.65e+01 1.36e+04 2.11e+02 0 Skip BFGS \n", + " 4 1.77e+02 1.61e+03 3.15e+01 7.20e+03 2.05e+02 0 Skip BFGS \n", + " 5 8.87e+01 6.11e+02 3.54e+01 3.75e+03 2.00e+02 0 Skip BFGS \n", + "Reached starting chifact with l2-norm regularization: Start IRLS steps...\n", + "irls_threshold 0.05591404683481955\n", + " 6 8.87e+01 2.41e+02 7.46e+01 6.86e+03 2.08e+02 0 Skip BFGS \n", + " 7 5.54e+01 3.48e+02 9.55e+01 5.64e+03 2.05e+02 0 \n", + " 8 7.35e+01 2.19e+02 1.13e+02 8.56e+03 2.15e+02 0 \n", + " 9 4.08e+01 4.64e+02 1.08e+02 4.86e+03 2.14e+02 0 \n", + " 10 5.17e+01 2.39e+02 9.73e+01 5.27e+03 2.12e+02 0 \n", + " 11 3.39e+01 3.05e+02 8.16e+01 3.07e+03 1.98e+02 0 \n", + " 12 4.89e+01 1.84e+02 6.99e+01 3.60e+03 2.15e+02 0 Skip BFGS \n", + " 13 6.39e+01 2.25e+02 5.94e+01 4.02e+03 2.16e+02 0 \n", + " 14 7.36e+01 2.85e+02 4.94e+01 3.92e+03 2.17e+02 0 \n", + " 15 4.79e+01 3.09e+02 4.14e+01 2.29e+03 1.94e+02 0 \n", + " 16 6.65e+01 2.00e+02 3.78e+01 2.71e+03 2.17e+02 0 Skip BFGS \n", + " 17 8.33e+01 2.45e+02 3.28e+01 2.97e+03 2.18e+02 0 \n", + " 18 1.04e+02 2.91e+02 2.90e+01 3.31e+03 2.18e+02 0 \n", + " 19 6.56e+01 3.41e+02 2.61e+01 2.05e+03 2.14e+02 0 \n", + " 20 8.36e+01 2.37e+02 2.56e+01 2.38e+03 2.18e+02 0 Skip BFGS \n", + " 21 1.00e+02 2.66e+02 2.36e+01 2.63e+03 2.18e+02 0 \n", + " 22 6.65e+01 2.92e+02 2.23e+01 1.77e+03 2.02e+02 0 \n", + " 23 8.96e+01 2.12e+02 2.22e+01 2.20e+03 2.18e+02 0 Skip BFGS \n", + " 24 1.14e+02 2.39e+02 2.09e+01 2.62e+03 2.18e+02 0 \n", + " 25 1.38e+02 2.60e+02 2.00e+01 3.01e+03 2.18e+02 0 \n", + " 26 1.62e+02 2.73e+02 1.91e+01 3.38e+03 2.18e+02 0 \n", + " 27 1.89e+02 2.81e+02 1.86e+01 3.78e+03 2.18e+02 0 Skip BFGS \n", + " 28 2.19e+02 2.92e+02 1.81e+01 4.26e+03 2.18e+02 0 \n", + " 29 1.43e+02 3.07e+02 1.77e+01 2.84e+03 2.04e+02 0 \n", + " 30 1.86e+02 2.28e+02 1.79e+01 3.55e+03 2.18e+02 0 Skip BFGS \n", + " 31 2.29e+02 2.53e+02 1.74e+01 4.24e+03 2.18e+02 0 \n", + " 32 2.69e+02 2.76e+02 1.70e+01 4.85e+03 2.18e+02 0 \n", + " 33 1.78e+02 2.92e+02 1.66e+01 3.25e+03 1.49e+02 0 \n", + " 34 2.38e+02 2.16e+02 1.67e+01 4.20e+03 2.18e+02 0 Skip BFGS \n", + " 35 2.92e+02 2.54e+02 1.64e+01 5.04e+03 2.18e+02 0 \n", + "Reach maximum number of IRLS cycles: 30\n", + "------------------------- STOP! -------------------------\n", + "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 2.6622e+04\n", + "1 : |xc-x_last| = 3.8510e-02 <= tolX*(1+|x0|) = 1.0000e-01\n", + "0 : |proj(x-g)-x| = 2.1822e+02 <= tolG = 1.0000e-01\n", + "0 : |proj(x-g)-x| = 2.1822e+02 <= 1e3*eps = 1.0000e-02\n", + "0 : maxIter = 100 <= iter = 36\n", + "------------------------- DONE! -------------------------\n" + ] + } + ], + "source": [ + "# inversion directives\n", + "# 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=1e0)\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.UpdateIRLS(\n", + " f_min_change=1e-4,\n", + " max_irls_iterations=30,\n", + " irls_cooling_factor=1.5,\n", + " misfit_tolerance=1e-2,\n", + ")\n", + "\n", + "# Options for outputting recovered models and predicted data for each beta.\n", + "save_iteration = directives.SaveOutputEveryIteration(save_txt=False)\n", + "\n", + "# Updating the preconditionner if it is model dependent.\n", + "update_jacobi = directives.UpdatePreconditioner()\n", + "\n", + "# The directives are defined as a list\n", + "directives_list = [\n", + " update_IRLS,\n", + " starting_beta,\n", + " save_iteration,\n", + " update_jacobi,\n", + "]\n", + "\n", + "# Define the regularization (model objective function) with distance weighting.\n", + "reg_dist = regularization.Sparse(mesh, active_cells=ind_active, mapping=model_map)\n", + "reg_dist.norms = [0, 2, 2, 2]\n", + "distance_weights = utils.distance_weighting(\n", + " mesh, receiver_locations, active_cells=ind_active, exponent=2\n", + ")\n", + "reg_dist.set_weights(distance_weights=distance_weights)\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=-1.0, upper=1.0, maxIterLS=20, maxIterCG=10, 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_dist, opt)\n", + "\n", + "# Here we combine the inverse problem and the set of directives\n", + "inv = inversion.BaseInversion(inv_prob, directives_list)\n", + "\n", + "# Run inversion\n", + "recovered_model_dist = inv.run(starting_model)" + ] + }, + { + "cell_type": "markdown", + "id": "4a503f48", + "metadata": { + "cell_marker": "#######################################################################" + }, + "source": [ + "Running the Distance Weighted inversion\n", + "---------------------------------------\n", + "\n", + "Here we define the directives, weights, regularization, and optimization\n", + "for a sensitivity weighted inversion\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "737052ab", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-19T19:26:30.057875Z", + "iopub.status.busy": "2024-11-19T19:26:30.057515Z", + "iopub.status.idle": "2024-11-19T19:26:52.382609Z", + "shell.execute_reply": "2024-11-19T19:26:52.381782Z", + "shell.execute_reply.started": "2024-11-19T19:26:30.057848Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_18221/553847346.py:8: DeprecationWarning: Update_IRLS has been deprecated, please use InversionDirective. It will be removed in version 0.24.0 of SimPEG.\n", + " update_IRLS = directives.Update_IRLS(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Running inversion with SimPEG v0.23.0\n", + "simpeg.InvProblem will set Regularization.reference_model to m0.\n", + "simpeg.InvProblem will set Regularization.reference_model to m0.\n", + "simpeg.InvProblem will set Regularization.reference_model to m0.\n", + "simpeg.InvProblem will set Regularization.reference_model to m0.\n", + "\n", + " simpeg.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n", + " ***Done using the default solver Pardiso and no solver_opts.***\n", + " \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 3.22e+03 2.66e+05 0.00e+00 2.66e+05 2.19e+02 0 \n", + " 1 1.61e+03 1.89e+04 1.14e+01 3.73e+04 2.16e+02 0 \n", + " 2 8.04e+02 8.24e+03 1.60e+01 2.11e+04 2.14e+02 0 Skip BFGS \n", + " 3 4.02e+02 3.28e+03 2.03e+01 1.14e+04 2.09e+02 0 Skip BFGS \n", + " 4 2.01e+02 1.25e+03 2.37e+01 6.02e+03 2.00e+02 0 Skip BFGS \n", + " 5 1.00e+02 4.74e+02 2.64e+01 3.12e+03 1.90e+02 0 Skip BFGS \n", + "Reached starting chifact with l2-norm regularization: Start IRLS steps...\n", + "irls_threshold 0.05478647261353992\n", + " 6 5.02e+01 1.90e+02 5.43e+01 2.92e+03 1.89e+02 0 Skip BFGS \n", + " 7 1.22e+02 1.01e+02 7.45e+01 9.19e+03 2.14e+02 0 \n", + " 8 8.21e+01 4.85e+02 7.44e+01 6.60e+03 2.17e+02 0 \n", + " 9 6.79e+01 3.20e+02 7.55e+01 5.45e+03 2.10e+02 0 \n", + " 10 1.03e+02 2.80e+02 6.81e+01 7.28e+03 2.16e+02 0 \n", + " 11 7.12e+01 4.55e+02 5.86e+01 4.63e+03 2.05e+02 0 \n", + " 12 1.08e+02 2.81e+02 5.23e+01 5.92e+03 2.16e+02 0 Skip BFGS \n", + " 13 7.94e+01 4.00e+02 4.31e+01 3.83e+03 2.13e+02 0 \n", + " 14 1.22e+02 2.70e+02 3.77e+01 4.87e+03 2.17e+02 0 Skip BFGS \n", + " 15 9.76e+01 3.40e+02 3.19e+01 3.45e+03 2.11e+02 0 \n", + " 16 1.52e+02 2.58e+02 2.88e+01 4.64e+03 2.18e+02 0 Skip BFGS \n", + " 17 1.19e+02 3.58e+02 2.52e+01 3.34e+03 2.13e+02 0 \n", + " 18 1.19e+02 2.89e+02 2.38e+01 3.11e+03 2.17e+02 0 Skip BFGS \n", + " 19 1.80e+02 2.79e+02 2.23e+01 4.29e+03 2.18e+02 0 Skip BFGS \n", + " 20 1.36e+02 3.81e+02 2.05e+01 3.16e+03 2.13e+02 0 \n", + " 21 1.16e+02 3.01e+02 2.01e+01 2.64e+03 2.17e+02 0 Skip BFGS \n", + " 22 1.82e+02 2.56e+02 1.95e+01 3.81e+03 2.18e+02 0 Skip BFGS \n", + " 23 1.47e+02 3.32e+02 1.82e+01 3.01e+03 2.16e+02 0 \n", + " 24 2.27e+02 2.66e+02 1.79e+01 4.33e+03 2.18e+02 0 Skip BFGS \n", + " 25 1.82e+02 3.39e+02 1.70e+01 3.42e+03 2.16e+02 0 \n", + " 26 2.79e+02 2.72e+02 1.68e+01 4.95e+03 2.18e+02 0 Skip BFGS \n", + " 27 2.18e+02 3.54e+02 1.61e+01 3.87e+03 2.15e+02 0 \n", + " 28 3.31e+02 2.80e+02 1.61e+01 5.59e+03 2.18e+02 0 \n", + " 29 2.55e+02 3.65e+02 1.54e+01 4.29e+03 2.15e+02 0 \n", + " 30 3.86e+02 2.82e+02 1.54e+01 6.23e+03 2.18e+02 0 Skip BFGS \n", + " 31 2.91e+02 3.80e+02 1.49e+01 4.71e+03 2.14e+02 0 \n", + " 32 2.91e+02 2.89e+02 1.49e+01 4.63e+03 2.15e+02 0 \n", + " 33 4.41e+02 2.82e+02 1.48e+01 6.80e+03 2.18e+02 0 Skip BFGS \n", + " 34 3.25e+02 3.99e+02 1.44e+01 5.06e+03 2.13e+02 0 \n", + " 35 3.25e+02 2.90e+02 1.44e+01 4.97e+03 2.17e+02 0 \n", + "Reach maximum number of IRLS cycles: 30\n", + "------------------------- STOP! -------------------------\n", + "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 2.6622e+04\n", + "1 : |xc-x_last| = 3.5632e-02 <= tolX*(1+|x0|) = 1.0000e-01\n", + "0 : |proj(x-g)-x| = 2.1655e+02 <= tolG = 1.0000e-01\n", + "0 : |proj(x-g)-x| = 2.1655e+02 <= 1e3*eps = 1.0000e-02\n", + "0 : maxIter = 100 <= iter = 36\n", + "------------------------- DONE! -------------------------\n" + ] + } + ], + "source": [ + "# inversion directives\n", + "# 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=1e0)\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-4,\n", + " max_irls_iterations=30,\n", + " coolEpsFact=1.5,\n", + " beta_tol=1e-2,\n", + ")\n", + "\n", + "# Options for outputting recovered models and predicted data for each beta.\n", + "save_iteration = directives.SaveOutputEveryIteration(save_txt=False)\n", + "\n", + "# Updating the preconditionner if it is model dependent.\n", + "update_jacobi = directives.UpdatePreconditioner()\n", + "\n", + "# Add sensitivity weights\n", + "sensitivity_weights = directives.UpdateSensitivityWeights(every_iteration=False)\n", + "\n", + "# The directives are defined as a list\n", + "directives_list = [\n", + " update_IRLS,\n", + " sensitivity_weights,\n", + " starting_beta,\n", + " save_iteration,\n", + " update_jacobi,\n", + "]\n", + "\n", + "# Define the regularization (model objective function) for sensitivity weighting.\n", + "reg_sensw = regularization.Sparse(mesh, active_cells=ind_active, mapping=model_map)\n", + "reg_sensw.norms = [0, 2, 2, 2]\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=-1.0, upper=1.0, maxIterLS=20, maxIterCG=10, 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_sensw, opt)\n", + "\n", + "# Here we combine the inverse problem and the set of directives\n", + "inv = inversion.BaseInversion(inv_prob, directives_list)\n", + "\n", + "# Run inversion\n", + "recovered_model_sensw = inv.run(starting_model)" + ] + }, + { + "cell_type": "markdown", + "id": "815da5a8", + "metadata": { + "cell_marker": "############################################################" + }, + "source": [ + "Recreate True Model\n", + "-------------------\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "785249b0", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-19T19:30:56.829122Z", + "iopub.status.busy": "2024-11-19T19:30:56.828635Z", + "iopub.status.idle": "2024-11-19T19:30:56.841409Z", + "shell.execute_reply": "2024-11-19T19:30:56.840487Z", + "shell.execute_reply.started": "2024-11-19T19:30:56.829079Z" + } + }, + "outputs": [], + "source": [ + "# Define density contrast values for each unit in g/cc\n", + "background_density = 0.0\n", + "block_density = -0.2\n", + "sphere_density = 0.2\n", + "\n", + "# Define model. Models in simpeg are vector arrays.\n", + "true_model = background_density * np.ones(nC)\n", + "\n", + "# You could find the indicies of specific cells within the model and change their\n", + "# value to add structures.\n", + "ind_block = (\n", + " (mesh.gridCC[ind_active, 0] > -50.0)\n", + " & (mesh.gridCC[ind_active, 0] < -20.0)\n", + " & (mesh.gridCC[ind_active, 1] > -15.0)\n", + " & (mesh.gridCC[ind_active, 1] < 15.0)\n", + " & (mesh.gridCC[ind_active, 2] > -50.0)\n", + " & (mesh.gridCC[ind_active, 2] < -30.0)\n", + ")\n", + "true_model[ind_block] = block_density\n", + "\n", + "# You can also use simpeg utilities to add structures to the model more concisely\n", + "ind_sphere = model_builder.get_indices_sphere(\n", + " np.r_[35.0, 0.0, -40.0], 15.0, mesh.gridCC\n", + ")\n", + "ind_sphere = ind_sphere[ind_active]\n", + "true_model[ind_sphere] = sphere_density\n" + ] + }, + { + "cell_type": "markdown", + "id": "47d992c8", + "metadata": { + "cell_marker": "############################################################" + }, + "source": [ + "Plotting True Model and Recovered Models\n", + "----------------------------------------\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "cbfa14f1", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-19T19:31:00.045716Z", + "iopub.status.busy": "2024-11-19T19:31:00.044710Z", + "iopub.status.idle": "2024-11-19T19:31:01.485919Z", + "shell.execute_reply": "2024-11-19T19:31:01.485293Z", + "shell.execute_reply.started": "2024-11-19T19:31:00.045644Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot Models\n", + "fig, ax = plt.subplots(2, 2, figsize=(20, 10), sharex=True, sharey=True)\n", + "ax = ax.flatten()\n", + "plotting_map = maps.InjectActiveCells(mesh, ind_active, np.nan)\n", + "cmap = \"coolwarm\"\n", + "slice_y_loc = 0.0\n", + "\n", + "mm = mesh.plot_slice(\n", + " plotting_map * true_model,\n", + " normal=\"Y\",\n", + " ax=ax[0],\n", + " grid=False,\n", + " slice_loc=slice_y_loc,\n", + " pcolor_opts={\"cmap\": cmap, \"norm\": norm},\n", + ")\n", + "ax[0].set_title(f\"True model slice at y = {slice_y_loc} m\")\n", + "plt.colorbar(mm[0], label=\"$g/cm^3$\", ax=ax[0])\n", + "\n", + "# plot depth weighting result\n", + "vmax = np.abs(recovered_model_dpth).max()\n", + "norm = mpl.colors.TwoSlopeNorm(vcenter=0, vmin=-vmax, vmax=vmax)\n", + "mm = mesh.plot_slice(\n", + " plotting_map * recovered_model_dpth,\n", + " normal=\"Y\",\n", + " ax=ax[1],\n", + " grid=False,\n", + " slice_loc=slice_y_loc,\n", + " pcolor_opts={\"cmap\": cmap, \"norm\": norm},\n", + ")\n", + "ax[1].set_title(f\"Depth weighting Model slice at y = {slice_y_loc} m\")\n", + "plt.colorbar(mm[0], label=\"$g/cm^3$\", ax=ax[1])\n", + "\n", + "# plot distance weighting result\n", + "vmax = np.abs(recovered_model_dist).max()\n", + "norm = mpl.colors.TwoSlopeNorm(vcenter=0, vmin=-vmax, vmax=vmax)\n", + "mm = mesh.plot_slice(\n", + " plotting_map * recovered_model_dist,\n", + " normal=\"Y\",\n", + " ax=ax[2],\n", + " grid=False,\n", + " slice_loc=slice_y_loc,\n", + " pcolor_opts={\"cmap\": cmap, \"norm\": norm},\n", + ")\n", + "ax[2].set_title(f\"Distance weighting Model slice at y = {slice_y_loc} m\")\n", + "plt.colorbar(mm[0], label=\"$g/cm^3$\", ax=ax[2])\n", + "\n", + "# plot sensitivity weighting result\n", + "vmax = np.abs(recovered_model_sensw).max()\n", + "norm = mpl.colors.TwoSlopeNorm(vcenter=0, vmin=-vmax, vmax=vmax)\n", + "mm = mesh.plot_slice(\n", + " plotting_map * recovered_model_sensw,\n", + " normal=\"Y\",\n", + " ax=ax[3],\n", + " grid=False,\n", + " slice_loc=slice_y_loc,\n", + " pcolor_opts={\"cmap\": cmap, \"norm\": norm},\n", + ")\n", + "ax[3].set_title(f\"Sensitivity weighting Model slice at y = {slice_y_loc} m\")\n", + "plt.colorbar(mm[0], label=\"$g/cm^3$\", ax=ax[3])\n", + "\n", + "# shared plotting\n", + "plotting_map = maps.InjectActiveCells(mesh, ind_active, 0.0)\n", + "slice_y_ind = (\n", + " mesh.cell_centers[:, 1] == np.abs(mesh.cell_centers[:, 1] - slice_y_loc).min()\n", + ")\n", + "for axx in ax:\n", + " utils.plot2Ddata(\n", + " mesh.cell_centers[slice_y_ind][:, [0, 2]],\n", + " (plotting_map * true_model)[slice_y_ind],\n", + " contourOpts={\"alpha\": 0},\n", + " level=True,\n", + " ncontour=2,\n", + " levelOpts={\"colors\": \"grey\", \"linewidths\": 2, \"linestyles\": \"--\"},\n", + " method=\"nearest\",\n", + " ax=axx,\n", + " )\n", + " axx.set_aspect(1)\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "3686ba28", + "metadata": { + "cell_marker": "############################################################", + "lines_to_next_cell": 0 + }, + "source": [ + "Visualize weights\n", + "-----------------\n", + "\n", + "Plot Weights" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "d812af42", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-19T19:31:13.195723Z", + "iopub.status.busy": "2024-11-19T19:31:13.195287Z", + "iopub.status.idle": "2024-11-19T19:31:13.962268Z", + "shell.execute_reply": "2024-11-19T19:31:13.961628Z", + "shell.execute_reply.started": "2024-11-19T19:31:13.195682Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 3, figsize=(20, 4), sharex=True, sharey=True)\n", + "plotting_map = maps.InjectActiveCells(mesh, ind_active, np.nan)\n", + "cmap = \"magma\"\n", + "slice_y_loc = 0.0\n", + "\n", + "# plot depth weights\n", + "mm = mesh.plot_slice(\n", + " plotting_map * np.log10(depth_weights),\n", + " normal=\"Y\",\n", + " ax=ax[0],\n", + " grid=False,\n", + " slice_loc=slice_y_loc,\n", + " pcolor_opts={\"cmap\": cmap},\n", + ")\n", + "ax[0].set_title(f\"log10(depth weights) slice at y = {slice_y_loc} m\")\n", + "plt.colorbar(mm[0], label=\"log10(depth weights)\", ax=ax[0])\n", + "\n", + "# plot distance weights\n", + "mm = mesh.plot_slice(\n", + " plotting_map * np.log10(distance_weights),\n", + " normal=\"Y\",\n", + " ax=ax[1],\n", + " grid=False,\n", + " slice_loc=slice_y_loc,\n", + " pcolor_opts={\"cmap\": cmap},\n", + ")\n", + "ax[1].set_title(f\"log10(distance weights) slice at y = {slice_y_loc} m\")\n", + "plt.colorbar(mm[0], label=\"log10(distance weights)\", ax=ax[1])\n", + "\n", + "# plot sensitivity weights\n", + "mm = mesh.plot_slice(\n", + " plotting_map * np.log10(reg_sensw.objfcts[0].get_weights(key=\"sensitivity\")),\n", + " normal=\"Y\",\n", + " ax=ax[2],\n", + " grid=False,\n", + " slice_loc=slice_y_loc,\n", + " pcolor_opts={\"cmap\": cmap},\n", + ")\n", + "ax[2].set_title(f\"log10(sensitivity weights) slice at y = {slice_y_loc} m\")\n", + "plt.colorbar(mm[0], label=\"log10(sensitivity weights)\", ax=ax[2])\n", + "\n", + "# shared plotting\n", + "for axx in ax:\n", + " axx.set_aspect(1)\n", + "\n", + "plt.tight_layout()" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "formats": "auto:light,ipynb", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From b05e30042f00c1a7f8c6345273cf28635fc18bf4 Mon Sep 17 00:00:00 2001 From: Santiago Soler Date: Tue, 19 Nov 2024 16:09:23 -0800 Subject: [PATCH 2/2] Add Thibaut to authors list --- myst.yml | 7 +++++ .../03-gravity/weighting_strategies.ipynb | 27 +++++-------------- 2 files changed, 14 insertions(+), 20 deletions(-) diff --git a/myst.yml b/myst.yml index a5759cf2..a74d168b 100644 --- a/myst.yml +++ b/myst.yml @@ -36,6 +36,11 @@ project: roles: - editing - Software + - name: Thibaut Astic + id: thibautastic + affiliations: kobold + roles: + - writing affiliations: - id: ubc institution: University of British Columbia @@ -48,6 +53,8 @@ project: country: Canada postal_code: V6T 1Z4 phone: 604 822 2449 + - id: kobold + institution: Kobold Metals # bibliography: [] references: python: https://docs.python.org/3/ diff --git a/notebooks/03-gravity/weighting_strategies.ipynb b/notebooks/03-gravity/weighting_strategies.ipynb index 6ddf2c34..4d02ed64 100644 --- a/notebooks/03-gravity/weighting_strategies.ipynb +++ b/notebooks/03-gravity/weighting_strategies.ipynb @@ -1,23 +1,15 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "ec476457", - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, { "cell_type": "markdown", - "id": "c2deb286-8394-4483-ae62-54ba31148747", - "metadata": { - "cell_marker": "\"\"\"" - }, + "id": "d32aad5a-f2ad-4dbf-8d01-709c1fe1c7b6", + "metadata": {}, "source": [ - "# Compare weighting strategy with Inversion of surface Gravity Anomaly Data" + "---\n", + "title: \"Compare weighting strategy with Inversion of surface Gravity Anomaly Data\"\n", + "authors:\n", + " - id: thibautastic\n", + "---" ] }, { @@ -1203,11 +1195,6 @@ } ], "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "formats": "auto:light,ipynb", - "notebook_metadata_filter": "-all" - }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python",