{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "# Linear Least-Squares Inversion with Tikhonov Regularization\n", "\n", "Here we demonstrate the basics of inverting data with SimPEG by considering a\n", "linear inverse problem. We formulate the inverse problem as a least-squares\n", "optimization problem. For this tutorial, we focus on the following:\n", "\n", " - Defining the forward problem\n", " - Defining the inverse problem (data misfit, regularization, optimization)\n", " - Specifying directives for the inversion\n", " - Recovering a set of model parameters which explains the observations\n" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Import Modules\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "pycharm": { "is_executing": false, "name": "#%%\n" } }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from discretize import TensorMesh\n", "\n", "from SimPEG import (\n", " simulation,\n", " maps,\n", " data_misfit,\n", " directives,\n", " optimization,\n", " regularization,\n", " inverse_problem,\n", " inversion,\n", ")\n", "\n", "# sphinx_gallery_thumbnail_number = 3" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Defining the Model and Mapping\n", "\n", "Here we generate a synthetic model and a mappig which goes from the model\n", "space to the row space of our linear operator.\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "pycharm": { "is_executing": false, "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": "(-2.0, 2.0)" }, "metadata": {}, "output_type": "execute_result", "execution_count": 2 }, { "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "nParam = 100 # Number of model paramters\n", "\n", "# A 1D mesh is used to define the row-space of the linear operator.\n", "mesh = TensorMesh([nParam])\n", "\n", "# Creating the true model\n", "true_model = np.zeros(mesh.nC)\n", "true_model[mesh.vectorCCx > 0.3] = 1.0\n", "true_model[mesh.vectorCCx > 0.45] = -0.5\n", "true_model[mesh.vectorCCx > 0.6] = 0\n", "\n", "# Mapping from the model space to the row space of the linear operator\n", "model_map = maps.IdentityMap(mesh)\n", "\n", "# Plotting the true model\n", "fig = plt.figure(figsize=(8, 5))\n", "ax = fig.add_subplot(111)\n", "ax.plot(mesh.vectorCCx, true_model, \"b-\")\n", "ax.set_ylim([-2, 2])" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Defining the Linear Operator\n", "\n", "Here we define the linear operator with dimensions (nData, nParam). In practive,\n", "you may have a problem-specific linear operator which you would like to construct\n", "or load here.\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "pycharm": { "is_executing": false, "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": "Text(0.5, 1.0, 'Columns of matrix G')" }, "metadata": {}, "output_type": "execute_result", "execution_count": 3 }, { "data": { "text/plain": "
", "image/png": 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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Number of data observations (rows)\n", "nData = 20\n", "\n", "# Create the linear operator for the tutorial. The columns of the linear operator\n", "# represents a set of decaying and oscillating functions.\n", "jk = np.linspace(1.0, 60.0, nData)\n", "p = -0.25\n", "q = 0.25\n", "\n", "\n", "def g(k):\n", " return np.exp(p * jk[k] * mesh.vectorCCx) * np.cos(\n", " np.pi * q * jk[k] * mesh.vectorCCx\n", " )\n", "\n", "\n", "G = np.empty((nData, nParam))\n", "\n", "for i in range(nData):\n", " G[i, :] = g(i)\n", "\n", "# Plot the columns of G\n", "fig = plt.figure(figsize=(8, 5))\n", "ax = fig.add_subplot(111)\n", "for i in range(G.shape[0]):\n", " ax.plot(G[i, :])\n", "\n", "ax.set_title(\"Columns of matrix G\")" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Defining the Simulation\n", "\n", "The simulation defines the relationship between the model parameters and\n", "predicted data.\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "pycharm": { "is_executing": false, "name": "#%%\n" } }, "outputs": [ { "name": "stderr", "text": [ "c:\\users\\u0102388\\pyprojects\\wavelet-based-inversion\\venv\\lib\\site-packages\\SimPEG\\simulation.py:547: UserWarning: G has not been implemented for the simulation\n warnings.warn(\"G has not been implemented for the simulation\")\n" ], "output_type": "stream" } ], "source": [ "sim = simulation.LinearSimulation(mesh, G=G, model_map=model_map)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Predict Synthetic Data\n", "\n", "Here, we use the true model to create synthetic data which we will subsequently\n", "invert.\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "pycharm": { "is_executing": false, "name": "#%%\n" } }, "outputs": [], "source": [ "# Standard deviation of Gaussian noise being added\n", "std = 0.01\n", "np.random.seed(1)\n", "\n", "# Create a SimPEG data object\n", "data_obj = sim.make_synthetic_data(true_model, relative_error=std, add_noise=True)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Define the Inverse Problem\n", "\n", "The inverse problem is defined by 3 things:\n", "\n", " 1) Data Misfit: a measure of how well our recovered model explains the field data\n", " 2) Regularization: constraints placed on the recovered model and a priori information\n", " 3) Optimization: the numerical approach used to solve the inverse problem\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "pycharm": { "is_executing": false, "name": "#%%\n" } }, "outputs": [], "source": [ "# Define the data misfit. Here the data misfit is the L2 norm of the weighted\n", "# residual between the observed data and the data predicted for a given model.\n", "# Within the data misfit, the residual between predicted and observed data are\n", "# normalized by the data's standard deviation.\n", "dmis = data_misfit.L2DataMisfit(simulation=sim, data=data_obj)\n", "\n", "# Define the regularization (model objective function).\n", "reg = regularization.Tikhonov(mesh, alpha_s=1.0, alpha_x=1.0)\n", "\n", "# Define how the optimization problem is solved.\n", "opt = optimization.InexactGaussNewton(maxIter=50)\n", "\n", "# Here we define the inverse problem that is to be solved\n", "inv_prob = inverse_problem.BaseInvProblem(dmis, reg, opt)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Define Inversion Directives\n", "\n", "Here we define any directiveas that are carried out during the inversion. This\n", "includes the cooling schedule for the trade-off parameter (beta), stopping\n", "criteria for the inversion and saving inversion results at each iteration.\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "pycharm": { "is_executing": false, "name": "#%%\n" } }, "outputs": [], "source": [ "# Defining a starting value for the trade-off parameter (beta) between the data\n", "# misfit and the regularization.\n", "starting_beta = directives.BetaEstimate_ByEig(beta0_ratio=1e-4)\n", "\n", "# Setting a stopping criteria for the inversion.\n", "target_misfit = directives.TargetMisfit()\n", "\n", "# The directives are defined as a list.\n", "directives_list = [starting_beta, target_misfit]" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Setting a Starting Model and Running the Inversion\n", "\n", "To define the inversion object, we need to define the inversion problem and\n", "the set of directives. We can then run the inversion.\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "pycharm": { "is_executing": false, "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "text": [ "SimPEG.InvProblem will set Regularization.mref to m0.\n\n SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n ***Done using same Solver and solverOpts as the problem***\nmodel has any nan: 0\n============================ Inexact Gauss Newton ============================\n # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n-----------------------------------------------------------------------------\nx0 has any nan: 0\n 0 1.86e+02 1.00e+05 0.00e+00 1.00e+05 1.26e+06 0 \n", " 1 1.86e+02 4.68e+04 3.50e-01 4.68e+04 8.00e+04 0 \n 2 1.86e+02 3.18e+04 1.35e+00 3.21e+04 5.90e+04 0 \n 3 1.86e+02 1.75e+04 5.21e+00 1.85e+04 5.36e+04 0 Skip BFGS \n", " 4 1.86e+02 1.16e+04 5.18e+00 1.25e+04 8.12e+04 0 \n 5 1.86e+02 8.05e+03 8.09e+00 9.55e+03 4.44e+04 0 \n 6 1.86e+02 4.39e+03 1.19e+01 6.59e+03 5.39e+04 0 \n", " 7 1.86e+02 3.33e+03 1.24e+01 5.65e+03 2.83e+04 0 \n 8 1.86e+02 2.87e+03 1.31e+01 5.30e+03 8.05e+04 0 \n 9 1.86e+02 2.38e+03 1.38e+01 4.95e+03 3.99e+04 0 \n", " 10 1.86e+02 1.60e+03 1.55e+01 4.48e+03 6.49e+04 0 \n 11 1.86e+02 1.32e+03 1.64e+01 4.37e+03 6.36e+04 0 \n 12 1.86e+02 1.19e+03 1.66e+01 4.28e+03 4.79e+04 0 \n", " 13 1.86e+02 1.15e+03 1.65e+01 4.22e+03 6.65e+04 0 \n 14 1.86e+02 7.66e+02 1.72e+01 3.97e+03 8.01e+03 0 Skip BFGS \n 15 1.86e+02 6.29e+02 1.76e+01 3.91e+03 2.86e+04 0 \n", " 16 1.86e+02 5.57e+02 1.76e+01 3.83e+03 2.20e+04 0 Skip BFGS \n 17 1.86e+02 5.61e+02 1.74e+01 3.81e+03 1.74e+04 0 \n 18 1.86e+02 6.09e+02 1.71e+01 3.79e+03 1.76e+04 0 Skip BFGS \n", " 19 1.86e+02 6.08e+02 1.71e+01 3.79e+03 1.38e+04 0 \n 20 1.86e+02 6.44e+02 1.68e+01 3.77e+03 1.66e+04 0 Skip BFGS \n 21 1.86e+02 6.14e+02 1.69e+01 3.77e+03 1.38e+04 0 \n", " 22 1.86e+02 6.11e+02 1.69e+01 3.76e+03 1.32e+04 0 Skip BFGS \n 23 1.86e+02 5.91e+02 1.70e+01 3.76e+03 1.97e+04 0 \n 24 1.86e+02 5.43e+02 1.72e+01 3.75e+03 2.16e+04 0 Skip BFGS \n", " 25 1.86e+02 5.54e+02 1.72e+01 3.75e+03 1.51e+04 0 \n 26 1.86e+02 5.71e+02 1.71e+01 3.75e+03 1.48e+04 0 Skip BFGS \n 27 1.86e+02 5.61e+02 1.71e+01 3.75e+03 1.47e+04 0 \n", " 28 1.86e+02 5.60e+02 1.71e+01 3.75e+03 1.48e+04 0 Skip BFGS \n 29 1.86e+02 5.61e+02 1.71e+01 3.75e+03 1.49e+04 0 \n 30 1.86e+02 5.55e+02 1.71e+01 3.74e+03 1.40e+04 0 Skip BFGS ", "\n 31 1.86e+02 5.55e+02 1.71e+01 3.74e+03 1.41e+04 0 \n 32 1.86e+02 5.42e+02 1.71e+01 3.72e+03 1.03e+04 0 Skip BFGS \n", " 33 1.86e+02 5.42e+02 1.71e+01 3.72e+03 1.03e+04 0 \n 34 1.86e+02 5.30e+02 1.71e+01 3.72e+03 8.23e+03 0 \n 35 1.86e+02 5.01e+02 1.73e+01 3.71e+03 4.98e+03 0 Skip BFGS \n", " 36 1.86e+02 5.11e+02 1.72e+01 3.71e+03 4.00e+03 0 \n 37 1.86e+02 5.07e+02 1.72e+01 3.71e+03 2.92e+03 0 \n 38 1.86e+02 5.13e+02 1.72e+01 3.71e+03 5.65e+03 0 Skip BFGS \n", " 39 1.86e+02 5.10e+02 1.72e+01 3.71e+03 5.97e+02 0 \n 40 1.86e+02 5.12e+02 1.72e+01 3.71e+03 1.73e+03 0 Skip BFGS \n", " 41 1.86e+02 5.14e+02 1.72e+01 3.71e+03 1.68e+03 0 \n 42 1.86e+02 5.13e+02 1.72e+01 3.71e+03 1.37e+03 0 \n 43 1.86e+02 5.12e+02 1.72e+01 3.71e+03 2.83e+03 0 \n", " 44 1.86e+02 5.14e+02 1.72e+01 3.71e+03 1.50e+03 0 Skip BFGS \n 45 1.86e+02 5.16e+02 1.72e+01 3.71e+03 1.71e+03 0 \n 46 1.86e+02 5.16e+02 1.72e+01 3.71e+03 1.83e+03 0 Skip BFGS \n", " 47 1.86e+02 5.16e+02 1.72e+01 3.71e+03 1.71e+03 0 \n 48 1.86e+02 5.13e+02 1.72e+01 3.71e+03 1.78e+03 0 Skip BFGS \n 49 1.86e+02 5.13e+02 1.72e+01 3.71e+03 1.83e+03 0 \n", " 50 1.86e+02 5.13e+02 1.72e+01 3.71e+03 1.67e+03 0 \n------------------------- STOP! -------------------------\n1 : |fc-fOld| = 4.0445e-04 <= tolF*(1+|f0|) = 1.0000e+04\n1 : |xc-x_last| = 5.0070e-04 <= tolX*(1+|x0|) = 1.0000e-01\n0 : |proj(x-g)-x| = 1.6708e+03 <= tolG = 1.0000e-01\n0 : |proj(x-g)-x| = 1.6708e+03 <= 1e3*eps = 1.0000e-02\n1 : maxIter = 50 <= iter = 50\n------------------------- DONE! -------------------------\n" ], "output_type": "stream" } ], "source": [ "# Here we combine the inverse problem and the set of directives\n", "inv = inversion.BaseInversion(inv_prob, directives_list)\n", "\n", "# Starting model\n", "starting_model = np.zeros(nParam)\n", "\n", "# Run inversion\n", "recovered_model = inv.run(starting_model)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Plotting Results\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "pycharm": { "is_executing": false, "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": "(-2.0, 2.0)" }, "metadata": {}, "output_type": "execute_result", "execution_count": 9 }, { "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Observed versus predicted data\n", "fig, ax = plt.subplots(1, 2, figsize=(12 * 1.2, 4 * 1.2))\n", "ax[0].plot(data_obj.dobs, \"b-\")\n", "ax[0].plot(inv_prob.dpred, \"r-\")\n", "ax[0].legend((\"Observed Data\", \"Predicted Data\"))\n", "\n", "# True versus recovered model\n", "ax[1].plot(mesh.vectorCCx, true_model, \"b-\")\n", "ax[1].plot(mesh.vectorCCx, recovered_model, \"r-\")\n", "ax[1].legend((\"True Model\", \"Recovered Model\"))\n", "ax[1].set_ylim([-2, 2])" ] } ], "metadata": { "kernelspec": { "name": "python3", "language": "python", "display_name": "Python 3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.5" } }, "nbformat": 4, "nbformat_minor": 1 }