{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"\n",
"\n",
"Credits: This is a modified example from SimPEG.xyz
\n",
"\n",
"# Linear Least-Squares Inversion\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": 2,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from SimPEG import (\n",
" simulation,\n",
" maps,\n",
" data_misfit,\n",
" directives,\n",
" optimization,\n",
" inverse_problem,\n",
" inversion,\n",
")\n",
"from discretize import TensorMesh\n",
"\n",
"from wbi import wavelet_regularization as regularization\n",
"# sphinx_gallery_thumbnail_number = 3"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": []
},
{
"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": 3,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/plain": "",
"image/png": "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\n"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"nParam = 100 # Number of model parameters\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])\n",
"plt.show()"
]
},
{
"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": 4,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/plain": "Text(0.5, 1.0, 'Columns of matrix G')"
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"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": 5,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/SimPEG/simulation.py:546: UserWarning: G has not been implemented for the simulation\n",
" warnings.warn(\"G has not been implemented for the simulation\")\n"
]
}
],
"source": [
"sim = simulation.LinearSimulation(mesh, G=G, model_map=model_map)"
]
},
{
"cell_type": "raw",
"metadata": {
"pycharm": {
"name": "#%% raw\n"
}
},
"source": []
},
{
"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": 6,
"metadata": {
"pycharm": {
"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)\n"
]
},
{
"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": 7,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"#\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",
"\n",
"\n",
"dmis = data_misfit.L2DataMisfit(simulation=sim, data=data_obj)\n",
"\n",
"# Define the regularization (model objective function).\n",
"\n",
"# Play here with the wav-parameter\n",
"# - db1 = blocky\n",
"# - db2, db3, db4 = rather sharp\n",
"# - db5+ = rather smooth\n",
"\n",
"reg = regularization.WaveletRegularization1D(mesh, wav=\"db6\")\n",
"\n",
"# Define how the optimization problem is solved.\n",
"opt = optimization.InexactGaussNewton(maxIter=100, maxIterLS=20)\n",
"\n",
"# Here we define the inverse problem that is to be solved\n",
"inv_prob = inverse_problem.BaseInvProblem(dmis, reg, opt)\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",
"# Defining a starting value for the trade-off parameter (beta) between the data\n",
"# misfit and the regularization.\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 = [target_misfit]\n"
]
},
{
"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": {
"scrolled": true,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/pymatsolver/direct.py:23: PardisoTypeConversionWarning: Converting dia_matrix matrix to CSR format, will slow down.\n",
" self.solver = MKLPardisoSolver(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"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***\n",
"model has any nan: 0\n",
"============================ Inexact Gauss Newton ============================\n",
" # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n",
"-----------------------------------------------------------------------------\n",
"x0 has any nan: 0\n",
" 0 1.00e+00 1.00e+05 9.58e-03 1.00e+05 1.26e+06 0 \n",
" 1 1.00e+00 6.29e+04 2.00e-02 6.29e+04 7.20e+04 0 \n",
" 2 1.00e+00 2.93e+04 7.57e-02 2.93e+04 3.31e+04 0 \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/pymatsolver/direct.py:73: PardisoTypeConversionWarning: Converting dia_matrix matrix to CSR format, will slow down.\n",
" self.solver.refactor(self.A)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 3 1.00e+00 1.87e+04 8.19e-02 1.87e+04 1.58e+05 0 \n",
" 4 1.00e+00 1.59e+04 7.59e-02 1.59e+04 2.10e+04 0 \n",
" 5 1.00e+00 1.49e+04 7.74e-02 1.49e+04 9.11e+04 0 \n",
" 6 1.00e+00 1.44e+04 7.47e-02 1.44e+04 5.25e+04 0 \n",
" 7 1.00e+00 1.38e+04 7.31e-02 1.38e+04 1.01e+05 0 \n",
" 8 1.00e+00 6.57e+03 5.14e-02 6.57e+03 2.21e+04 0 Skip BFGS \n",
" 9 1.00e+00 4.96e+03 5.28e-02 4.96e+03 2.22e+04 0 \n",
" 10 1.00e+00 4.36e+03 5.42e-02 4.36e+03 6.78e+04 0 \n",
" 11 1.00e+00 3.51e+03 5.26e-02 3.51e+03 4.76e+04 0 \n",
" 12 1.00e+00 3.07e+03 5.49e-02 3.07e+03 3.62e+04 0 \n",
" 13 1.00e+00 2.41e+03 5.43e-02 2.41e+03 2.48e+04 0 \n",
" 14 1.00e+00 1.55e+03 4.81e-02 1.55e+03 3.26e+04 0 Skip BFGS \n",
" 15 1.00e+00 1.41e+03 4.74e-02 1.41e+03 1.96e+04 0 \n",
" 16 1.00e+00 9.10e+02 4.71e-02 9.10e+02 8.26e+04 0 \n",
" 17 1.00e+00 5.11e+02 6.27e-02 5.11e+02 2.58e+04 0 Skip BFGS \n",
" 18 1.00e+00 4.93e+02 6.23e-02 4.93e+02 2.55e+04 0 \n",
" 19 1.00e+00 3.85e+02 6.87e-02 3.85e+02 4.29e+04 0 Skip BFGS \n",
" 20 1.00e+00 3.18e+02 6.20e-02 3.18e+02 2.62e+04 0 \n",
" 21 1.00e+00 2.45e+02 5.86e-02 2.46e+02 2.60e+04 0 Skip BFGS \n",
" 22 1.00e+00 2.42e+02 5.89e-02 2.42e+02 2.57e+04 0 \n",
" 23 1.00e+00 2.42e+02 5.89e-02 2.42e+02 2.64e+04 0 Skip BFGS \n",
" 24 1.00e+00 2.42e+02 5.87e-02 2.42e+02 2.59e+04 0 \n",
" 25 1.00e+00 2.39e+02 5.52e-02 2.39e+02 2.60e+04 0 \n",
" 26 1.00e+00 3.06e+01 6.15e-02 3.06e+01 1.58e+04 0 Skip BFGS \n",
" 27 1.00e+00 3.03e+01 6.23e-02 3.04e+01 1.56e+04 0 Skip BFGS \n",
" 28 1.00e+00 3.03e+01 6.23e-02 3.04e+01 1.49e+04 0 \n",
" 29 1.00e+00 3.03e+01 6.23e-02 3.03e+01 1.56e+04 0 \n",
" 30 1.00e+00 3.01e+01 6.31e-02 3.02e+01 1.72e+04 0 Skip BFGS \n",
" 31 1.00e+00 2.99e+01 6.14e-02 3.00e+01 1.70e+04 0 \n",
" 32 1.00e+00 2.31e+01 6.14e-02 2.32e+01 7.55e+03 0 \n",
" 33 1.00e+00 1.38e+01 6.26e-02 1.38e+01 2.06e+04 0 \n",
" 34 1.00e+00 1.32e+01 6.31e-02 1.33e+01 1.25e+04 0 Skip BFGS \n",
" 35 1.00e+00 1.29e+01 6.27e-02 1.30e+01 3.82e+03 0 \n",
" 36 1.00e+00 1.15e+01 6.29e-02 1.16e+01 1.83e+03 0 \n",
" 37 1.00e+00 1.12e+01 6.44e-02 1.12e+01 1.08e+03 0 Skip BFGS \n",
" 38 1.00e+00 1.08e+01 6.31e-02 1.09e+01 1.51e+03 0 \n",
" 39 1.00e+00 8.37e+00 6.28e-02 8.43e+00 3.46e+03 0 Skip BFGS \n",
" 40 1.00e+00 8.09e+00 6.32e-02 8.15e+00 3.17e+03 0 \n",
" 41 1.00e+00 7.97e+00 6.35e-02 8.04e+00 3.57e+03 0 Skip BFGS \n",
" 42 1.00e+00 7.89e+00 6.33e-02 7.95e+00 3.54e+03 0 \n",
" 43 1.00e+00 7.86e+00 6.34e-02 7.92e+00 3.75e+03 0 Skip BFGS \n",
" 44 1.00e+00 7.83e+00 6.34e-02 7.90e+00 3.66e+03 0 \n",
" 45 1.00e+00 7.66e+00 6.42e-02 7.72e+00 4.90e+03 0 Skip BFGS \n",
" 46 1.00e+00 7.64e+00 6.39e-02 7.71e+00 4.72e+03 0 \n",
" 47 1.00e+00 7.64e+00 6.38e-02 7.71e+00 4.53e+03 0 Skip BFGS \n",
" 48 1.00e+00 7.64e+00 6.39e-02 7.70e+00 4.57e+03 0 \n",
" 49 1.00e+00 7.34e+00 6.24e-02 7.40e+00 1.06e+04 0 Skip BFGS \n",
" 50 1.00e+00 7.31e+00 6.21e-02 7.37e+00 1.11e+04 0 Skip BFGS \n",
" 51 1.00e+00 7.28e+00 6.23e-02 7.34e+00 1.05e+04 0 \n",
" 52 1.00e+00 7.28e+00 6.21e-02 7.34e+00 1.04e+04 0 Skip BFGS \n",
" 53 1.00e+00 7.27e+00 6.23e-02 7.34e+00 1.06e+04 0 \n",
" 54 1.00e+00 7.27e+00 6.24e-02 7.33e+00 1.04e+04 0 \n",
" 55 1.00e+00 7.10e+00 6.24e-02 7.16e+00 1.16e+03 0 Skip BFGS \n",
" 56 1.00e+00 7.03e+00 6.27e-02 7.09e+00 2.54e+03 0 \n",
" 57 1.00e+00 6.99e+00 6.31e-02 7.05e+00 6.12e+02 0 Skip BFGS \n",
" 58 1.00e+00 6.99e+00 6.31e-02 7.05e+00 5.35e+02 0 Skip BFGS \n",
" 59 1.00e+00 6.99e+00 6.31e-02 7.05e+00 5.53e+02 0 \n",
" 60 1.00e+00 6.71e+00 6.45e-02 6.77e+00 1.35e+03 0 \n",
" 61 1.00e+00 6.67e+00 6.45e-02 6.74e+00 1.75e+03 0 Skip BFGS \n",
" 62 1.00e+00 6.67e+00 6.45e-02 6.73e+00 2.39e+03 0 \n",
" 63 1.00e+00 6.64e+00 6.48e-02 6.71e+00 2.30e+03 0 \n",
" 64 1.00e+00 6.59e+00 6.47e-02 6.65e+00 1.80e+03 0 Skip BFGS \n",
" 65 1.00e+00 6.59e+00 6.47e-02 6.65e+00 1.83e+03 0 Skip BFGS \n",
" 66 1.00e+00 6.59e+00 6.47e-02 6.65e+00 1.81e+03 0 \n",
" 67 1.00e+00 6.59e+00 6.47e-02 6.65e+00 1.86e+03 0 \n",
" 68 1.00e+00 6.59e+00 6.48e-02 6.65e+00 2.00e+03 0 Skip BFGS \n",
" 69 1.00e+00 6.58e+00 6.47e-02 6.65e+00 1.90e+03 0 \n",
" 70 1.00e+00 6.56e+00 6.46e-02 6.63e+00 2.18e+03 0 \n",
" 71 1.00e+00 6.56e+00 6.46e-02 6.62e+00 2.29e+03 0 Skip BFGS \n",
" 72 1.00e+00 6.55e+00 6.46e-02 6.62e+00 2.30e+03 0 \n",
" 73 1.00e+00 6.54e+00 6.45e-02 6.61e+00 3.14e+03 0 Skip BFGS \n",
" 74 1.00e+00 6.53e+00 6.40e-02 6.60e+00 2.23e+03 0 \n",
" 75 1.00e+00 6.51e+00 6.38e-02 6.57e+00 2.55e+03 0 Skip BFGS \n",
" 76 1.00e+00 6.48e+00 6.34e-02 6.55e+00 1.57e+03 0 \n",
" 77 1.00e+00 6.31e+00 6.75e-02 6.38e+00 2.14e+03 0 Skip BFGS \n",
" 78 1.00e+00 6.28e+00 6.83e-02 6.34e+00 1.04e+03 0 \n",
" 79 1.00e+00 6.27e+00 6.85e-02 6.34e+00 1.95e+03 0 Skip BFGS \n",
" 80 1.00e+00 6.27e+00 6.82e-02 6.34e+00 1.25e+03 0 \n",
" 81 1.00e+00 6.27e+00 6.83e-02 6.33e+00 1.48e+03 0 \n",
" 82 1.00e+00 6.26e+00 6.83e-02 6.33e+00 2.06e+03 0 \n",
" 83 1.00e+00 6.24e+00 6.96e-02 6.31e+00 1.64e+03 0 Skip BFGS \n",
" 84 1.00e+00 6.24e+00 6.96e-02 6.31e+00 1.54e+03 0 Skip BFGS \n",
" 85 1.00e+00 6.24e+00 6.96e-02 6.31e+00 1.63e+03 0 \n",
" 86 1.00e+00 6.23e+00 6.87e-02 6.30e+00 1.93e+03 0 Skip BFGS \n",
" 87 1.00e+00 6.22e+00 6.91e-02 6.29e+00 1.51e+03 0 \n",
" 88 1.00e+00 6.22e+00 6.92e-02 6.29e+00 1.42e+03 0 Skip BFGS \n",
" 89 1.00e+00 6.21e+00 6.92e-02 6.28e+00 1.34e+03 0 \n",
" 90 1.00e+00 6.21e+00 6.93e-02 6.28e+00 1.44e+03 0 Skip BFGS \n",
" 91 1.00e+00 6.21e+00 6.92e-02 6.28e+00 1.51e+03 0 \n",
" 92 1.00e+00 6.21e+00 6.92e-02 6.28e+00 1.55e+03 0 Skip BFGS \n",
" 93 1.00e+00 6.21e+00 6.92e-02 6.28e+00 1.58e+03 0 \n",
" 94 1.00e+00 6.20e+00 6.91e-02 6.27e+00 2.13e+03 0 Skip BFGS \n",
" 95 1.00e+00 6.20e+00 6.91e-02 6.27e+00 2.08e+03 0 \n",
" 96 1.00e+00 6.19e+00 6.92e-02 6.25e+00 1.56e+03 0 \n",
" 97 1.00e+00 6.19e+00 6.93e-02 6.25e+00 1.98e+03 0 Skip BFGS \n",
" 98 1.00e+00 6.19e+00 6.93e-02 6.25e+00 1.58e+03 0 \n",
" 99 1.00e+00 6.17e+00 6.98e-02 6.24e+00 8.79e+02 0 Skip BFGS \n",
" 100 1.00e+00 6.16e+00 6.90e-02 6.23e+00 4.08e+02 0 \n",
"------------------------- STOP! -------------------------\n",
"1 : |fc-fOld| = 7.7596e-03 <= tolF*(1+|f0|) = 1.0000e+04\n",
"1 : |xc-x_last| = 1.8055e-02 <= tolX*(1+|x0|) = 1.0000e-01\n",
"0 : |proj(x-g)-x| = 4.0766e+02 <= tolG = 1.0000e-01\n",
"0 : |proj(x-g)-x| = 4.0766e+02 <= 1e3*eps = 1.0000e-02\n",
"1 : maxIter = 100 <= iter = 100\n",
"------------------------- DONE! -------------------------\n"
]
}
],
"source": [
"inv = inversion.BaseInversion(inv_prob)\n",
"\n",
"# Starting model\n",
"starting_model = np.random.rand(nParam)\n",
"starting_model = np.zeros(nParam)\n",
"\n",
"\n",
"# The inversion class is kinda broken\n",
"# Run inversion\n",
"recovered_model = inv.run(starting_model)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": []
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"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])\n",
"ax[1].set_title(\"Wavelet-type \" + reg.wavelets.wav)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"source": [
"## Generate ensemble of inversion results"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The callback on the InexactGaussNewton Optimization was replaced.\n",
"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***\n",
"model has any nan: 0\n",
"============================ Inexact Gauss Newton ============================\n",
" # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n",
"-----------------------------------------------------------------------------\n",
"x0 has any nan: 0\n",
" 0 1.00e+03 1.00e+05 8.85e-03 1.00e+05 1.26e+06 0 \n",
" 1 1.00e+03 6.29e+04 5.09e-02 6.30e+04 7.20e+04 0 \n",
" 2 1.00e+03 2.93e+04 2.27e-01 2.95e+04 3.22e+04 0 \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/pymatsolver/direct.py:23: PardisoTypeConversionWarning: Converting dia_matrix matrix to CSR format, will slow down.\n",
" self.solver = MKLPardisoSolver(\n",
"/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/pymatsolver/direct.py:73: PardisoTypeConversionWarning: Converting dia_matrix matrix to CSR format, will slow down.\n",
" self.solver.refactor(self.A)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 3 1.00e+03 1.90e+04 2.73e-01 1.92e+04 4.81e+04 0 Skip BFGS \n",
" 4 1.00e+03 1.63e+04 2.71e-01 1.66e+04 2.01e+04 0 \n",
" 5 1.00e+03 1.45e+04 2.55e-01 1.48e+04 3.54e+04 0 \n",
" 6 1.00e+03 1.21e+04 2.69e-01 1.24e+04 2.86e+04 0 \n",
" 7 1.00e+03 8.02e+03 3.01e-01 8.32e+03 2.03e+04 0 Skip BFGS \n",
" 8 1.00e+03 6.79e+03 2.99e-01 7.09e+03 2.38e+04 0 \n",
" 9 1.00e+03 5.47e+03 3.48e-01 5.81e+03 2.63e+04 0 \n",
" 10 1.00e+03 4.63e+03 3.25e-01 4.95e+03 2.82e+04 0 \n",
" 11 1.00e+03 4.33e+03 3.50e-01 4.68e+03 2.49e+04 0 \n",
" 12 1.00e+03 4.21e+03 3.46e-01 4.56e+03 7.22e+04 0 \n",
" 13 1.00e+03 4.14e+03 3.53e-01 4.49e+03 3.32e+04 0 \n",
" 14 1.00e+03 4.11e+03 3.53e-01 4.46e+03 4.80e+04 0 Skip BFGS \n",
" 15 1.00e+03 4.10e+03 3.54e-01 4.45e+03 3.63e+04 0 \n",
" 16 1.00e+03 2.44e+03 3.15e-01 2.75e+03 3.15e+04 0 Skip BFGS \n",
" 17 1.00e+03 1.91e+03 3.36e-01 2.25e+03 4.74e+04 0 \n",
" 18 1.00e+03 1.70e+03 3.24e-01 2.02e+03 1.58e+04 0 \n",
" 19 1.00e+03 1.36e+03 3.31e-01 1.69e+03 2.74e+04 0 Skip BFGS \n",
" 20 1.00e+03 1.05e+03 3.42e-01 1.39e+03 2.94e+04 0 \n",
" 21 1.00e+03 7.49e+02 4.11e-01 1.16e+03 1.83e+04 0 Skip BFGS \n",
" 22 1.00e+03 6.86e+02 4.36e-01 1.12e+03 2.09e+04 0 Skip BFGS \n",
" 23 1.00e+03 6.96e+02 3.99e-01 1.10e+03 1.84e+04 0 \n",
" 24 1.00e+03 7.08e+02 3.79e-01 1.09e+03 3.19e+04 0 Skip BFGS \n",
" 25 1.00e+03 6.97e+02 3.81e-01 1.08e+03 1.69e+04 0 \n",
" 26 1.00e+03 7.11e+02 3.63e-01 1.07e+03 1.34e+04 1 \n",
" 27 1.00e+03 6.88e+02 3.74e-01 1.06e+03 3.25e+04 1 \n",
" 28 1.00e+03 6.90e+02 3.59e-01 1.05e+03 3.88e+04 2 \n",
" 29 1.00e+03 6.81e+02 3.62e-01 1.04e+03 2.98e+04 1 \n",
" 30 1.00e+03 6.77e+02 3.49e-01 1.03e+03 3.31e+04 1 \n",
" 31 1.00e+03 6.60e+02 3.60e-01 1.02e+03 3.87e+04 0 \n",
" 32 1.00e+03 3.38e+02 3.59e-01 6.97e+02 1.89e+04 0 \n",
" 33 1.00e+03 2.05e+02 3.61e-01 5.66e+02 1.83e+04 0 \n",
" 34 1.00e+03 4.55e+01 3.72e-01 4.17e+02 4.30e+03 0 Skip BFGS \n",
" 35 1.00e+03 4.43e+01 3.58e-01 4.02e+02 4.04e+03 0 \n",
" 36 1.00e+03 3.27e+01 3.20e-01 3.53e+02 2.49e+04 1 Skip BFGS \n",
" 37 1.00e+03 2.52e+01 3.15e-01 3.41e+02 2.38e+03 0 \n",
" 38 1.00e+03 2.67e+01 3.11e-01 3.38e+02 4.74e+04 0 \n",
" 39 1.00e+03 2.75e+01 3.08e-01 3.36e+02 4.72e+03 0 \n",
" 40 1.00e+03 1.77e+01 3.11e-01 3.28e+02 3.13e+04 1 \n",
" 41 1.00e+03 2.72e+01 2.94e-01 3.21e+02 1.73e+04 0 \n",
" 42 1.00e+03 2.78e+01 2.90e-01 3.18e+02 2.26e+04 0 \n",
" 43 1.00e+03 2.02e+01 2.80e-01 3.00e+02 4.41e+04 1 \n",
" 44 1.00e+03 2.21e+01 2.67e-01 2.89e+02 3.18e+04 1 \n",
" 45 1.00e+03 2.30e+01 2.59e-01 2.82e+02 3.33e+04 0 \n",
" 46 1.00e+03 1.75e+01 2.48e-01 2.65e+02 5.72e+04 1 \n",
" 47 1.00e+03 1.48e+01 2.38e-01 2.53e+02 3.02e+04 1 \n",
" 48 1.00e+03 1.36e+01 2.34e-01 2.47e+02 2.46e+04 2 Skip BFGS \n",
" 49 1.00e+03 1.31e+01 2.32e-01 2.45e+02 2.32e+04 2 \n",
" 50 1.00e+03 1.26e+01 2.31e-01 2.44e+02 2.01e+04 3 Skip BFGS \n",
" 51 1.00e+03 1.27e+01 2.26e-01 2.38e+02 2.59e+04 3 \n",
" 52 1.00e+03 1.33e+01 2.24e-01 2.37e+02 1.64e+04 1 \n",
" 53 1.00e+03 1.33e+01 2.23e-01 2.36e+02 1.54e+04 3 \n",
" 54 1.00e+03 1.55e+01 2.20e-01 2.35e+02 3.77e+04 0 \n",
" 55 1.00e+03 1.50e+01 2.19e-01 2.34e+02 7.35e+03 0 Skip BFGS \n",
" 56 1.00e+03 1.21e+01 2.14e-01 2.26e+02 7.93e+03 0 \n",
" 57 1.00e+03 1.43e+01 2.07e-01 2.22e+02 1.18e+04 1 \n",
" 58 1.00e+03 1.23e+01 2.08e-01 2.20e+02 2.84e+04 0 \n",
" 59 1.00e+03 1.29e+01 2.03e-01 2.16e+02 2.01e+04 3 \n",
" 60 1.00e+03 1.28e+01 1.98e-01 2.11e+02 1.59e+04 3 \n",
" 61 1.00e+03 1.27e+01 1.96e-01 2.09e+02 1.56e+04 1 \n",
" 62 1.00e+03 1.21e+01 1.94e-01 2.06e+02 1.86e+04 1 \n",
" 63 1.00e+03 1.11e+01 1.91e-01 2.02e+02 2.72e+04 1 \n",
" 64 1.00e+03 1.30e+01 1.85e-01 1.98e+02 3.61e+04 1 \n",
" 65 1.00e+03 1.45e+01 1.81e-01 1.95e+02 1.91e+04 0 Skip BFGS \n",
" 66 1.00e+03 1.21e+01 1.80e-01 1.92e+02 1.64e+04 1 \n",
" 67 1.00e+03 1.29e+01 1.77e-01 1.90e+02 3.48e+04 2 \n",
" 68 1.00e+03 1.24e+01 1.76e-01 1.88e+02 3.13e+03 0 \n",
" 69 1.00e+03 1.25e+01 1.75e-01 1.87e+02 1.31e+04 1 \n",
" 70 1.00e+03 1.17e+01 1.73e-01 1.85e+02 7.62e+03 2 \n",
" 71 1.00e+03 1.14e+01 1.72e-01 1.83e+02 7.62e+03 3 \n",
" 72 1.00e+03 1.15e+01 1.71e-01 1.82e+02 4.92e+03 2 Skip BFGS \n",
" 73 1.00e+03 1.12e+01 1.70e-01 1.81e+02 4.05e+03 2 \n",
" 74 1.00e+03 1.09e+01 1.68e-01 1.79e+02 9.17e+03 3 Skip BFGS \n",
" 75 1.00e+03 1.09e+01 1.67e-01 1.78e+02 2.62e+03 3 \n",
" 76 1.00e+03 1.09e+01 1.66e-01 1.77e+02 6.58e+03 3 \n",
" 77 1.00e+03 1.08e+01 1.65e-01 1.76e+02 8.52e+03 3 \n",
" 78 1.00e+03 1.10e+01 1.64e-01 1.75e+02 5.20e+03 2 \n",
" 79 1.00e+03 1.12e+01 1.63e-01 1.74e+02 7.65e+03 2 Skip BFGS \n",
" 80 1.00e+03 1.09e+01 1.63e-01 1.73e+02 4.48e+03 3 \n",
" 81 1.00e+03 1.12e+01 1.62e-01 1.73e+02 5.03e+03 1 \n",
" 82 1.00e+03 1.09e+01 1.61e-01 1.72e+02 3.72e+03 2 \n",
" 83 1.00e+03 1.09e+01 1.61e-01 1.72e+02 3.19e+03 4 \n",
" 84 1.00e+03 1.07e+01 1.60e-01 1.71e+02 2.79e+03 3 \n",
" 85 1.00e+03 1.06e+01 1.60e-01 1.70e+02 2.42e+03 4 \n",
" 86 1.00e+03 1.08e+01 1.59e-01 1.70e+02 2.95e+03 2 Skip BFGS \n",
" 87 1.00e+03 1.05e+01 1.59e-01 1.69e+02 2.19e+03 2 \n",
" 88 1.00e+03 1.04e+01 1.59e-01 1.69e+02 2.37e+03 1 Skip BFGS \n",
" 89 1.00e+03 1.04e+01 1.58e-01 1.68e+02 2.01e+03 4 \n",
" 90 1.00e+03 1.05e+01 1.56e-01 1.67e+02 1.90e+03 4 \n",
" 91 1.00e+03 1.06e+01 1.56e-01 1.66e+02 2.12e+03 4 \n",
" 92 1.00e+03 1.09e+01 1.54e-01 1.65e+02 1.73e+03 2 \n",
" 93 1.00e+03 1.18e+01 1.50e-01 1.62e+02 2.26e+03 0 \n",
" 94 1.00e+03 1.20e+01 1.48e-01 1.60e+02 2.77e+03 1 \n",
" 95 1.00e+03 1.24e+01 1.48e-01 1.60e+02 3.78e+03 1 \n",
" 96 1.00e+03 1.24e+01 1.48e-01 1.60e+02 4.96e+03 1 \n",
" 97 1.00e+03 1.18e+01 1.47e-01 1.59e+02 2.82e+03 1 \n",
" 98 1.00e+03 1.17e+01 1.47e-01 1.59e+02 2.50e+03 1 \n",
" 99 1.00e+03 1.16e+01 1.46e-01 1.57e+02 1.94e+03 4 \n",
" 100 1.00e+03 1.17e+01 1.45e-01 1.57e+02 2.35e+03 2 \n",
"------------------------- STOP! -------------------------\n",
"1 : |fc-fOld| = 4.8734e-01 <= tolF*(1+|f0|) = 1.0001e+04\n",
"1 : |xc-x_last| = 7.7389e-03 <= tolX*(1+|x0|) = 1.0000e-01\n",
"0 : |proj(x-g)-x| = 2.3529e+03 <= tolG = 1.0000e-01\n",
"0 : |proj(x-g)-x| = 2.3529e+03 <= 1e3*eps = 1.0000e-02\n",
"1 : maxIter = 100 <= iter = 100\n",
"------------------------- DONE! -------------------------\n",
"The callback on the InexactGaussNewton Optimization was replaced.\n",
"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***\n",
"model has any nan: 0\n",
"============================ Inexact Gauss Newton ============================\n",
" # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n",
"-----------------------------------------------------------------------------\n",
"x0 has any nan: 0\n",
" 0 1.00e+04 1.00e+05 9.09e-03 1.00e+05 1.26e+06 0 \n",
" 1 1.00e+04 6.29e+04 3.09e-02 6.32e+04 7.24e+04 0 \n",
" 2 1.00e+04 3.07e+04 1.97e-01 3.26e+04 2.85e+04 0 \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/SimPEG/directives/directives.py:60: UserWarning: InversionDirective TargetMisfit has switched to a new inversion.\n",
" warnings.warn(\n",
"/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/pymatsolver/direct.py:23: PardisoTypeConversionWarning: Converting dia_matrix matrix to CSR format, will slow down.\n",
" self.solver = MKLPardisoSolver(\n",
"/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/pymatsolver/direct.py:73: PardisoTypeConversionWarning: Converting dia_matrix matrix to CSR format, will slow down.\n",
" self.solver.refactor(self.A)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 3 1.00e+04 1.85e+04 2.18e-01 2.07e+04 2.51e+04 0 Skip BFGS \n",
" 4 1.00e+04 1.41e+04 2.21e-01 1.63e+04 3.87e+04 0 \n",
" 5 1.00e+04 1.11e+04 1.81e-01 1.29e+04 2.42e+04 0 \n",
" 6 1.00e+04 8.39e+03 2.60e-01 1.10e+04 3.43e+04 0 \n",
" 7 1.00e+04 6.17e+03 2.92e-01 9.09e+03 3.48e+04 0 Skip BFGS \n",
" 8 1.00e+04 4.79e+03 2.63e-01 7.41e+03 2.99e+04 0 Skip BFGS \n",
" 9 1.00e+04 5.11e+03 1.77e-01 6.88e+03 3.95e+04 0 \n",
" 10 1.00e+04 4.42e+03 2.05e-01 6.47e+03 2.98e+04 0 \n",
" 11 1.00e+04 4.49e+03 1.94e-01 6.43e+03 3.33e+04 1 \n",
" 12 1.00e+04 4.46e+03 1.61e-01 6.07e+03 4.65e+04 0 \n",
" 13 1.00e+04 4.11e+03 1.91e-01 6.02e+03 4.05e+04 0 \n",
" 14 1.00e+04 3.68e+03 2.15e-01 5.83e+03 4.46e+04 0 \n",
" 15 1.00e+04 3.58e+03 1.99e-01 5.57e+03 4.49e+04 0 \n",
" 16 1.00e+04 2.89e+03 2.58e-01 5.48e+03 2.29e+04 0 \n",
" 17 1.00e+04 2.45e+03 2.51e-01 4.96e+03 1.86e+04 1 \n",
" 18 1.00e+04 2.26e+03 2.69e-01 4.95e+03 1.89e+04 1 \n",
" 19 1.00e+04 2.22e+03 2.41e-01 4.63e+03 2.01e+04 2 \n",
" 20 1.00e+04 2.13e+03 2.34e-01 4.47e+03 2.03e+04 3 \n",
" 21 1.00e+04 2.04e+03 2.27e-01 4.31e+03 2.00e+04 3 \n",
" 22 1.00e+04 1.86e+03 2.14e-01 4.00e+03 2.37e+04 1 \n",
" 23 1.00e+04 1.81e+03 1.99e-01 3.80e+03 2.42e+04 3 \n",
" 24 1.00e+04 1.73e+03 2.01e-01 3.74e+03 2.37e+04 2 \n",
" 25 1.00e+04 1.50e+03 2.04e-01 3.54e+03 1.91e+04 2 \n",
" 26 1.00e+04 1.40e+03 2.09e-01 3.48e+03 1.36e+04 1 \n",
" 27 1.00e+04 1.25e+03 2.00e-01 3.25e+03 1.28e+04 2 \n",
" 28 1.00e+04 1.19e+03 1.95e-01 3.14e+03 1.26e+04 3 \n",
" 29 1.00e+04 1.12e+03 1.89e-01 3.01e+03 1.19e+04 2 \n",
" 30 1.00e+04 1.02e+03 1.89e-01 2.91e+03 1.16e+04 1 \n",
" 31 1.00e+04 1.03e+03 1.84e-01 2.87e+03 1.10e+04 4 \n",
" 32 1.00e+04 8.57e+02 1.93e-01 2.79e+03 1.04e+04 1 \n",
" 33 1.00e+04 8.37e+02 1.92e-01 2.76e+03 1.02e+04 3 \n",
" 34 1.00e+04 7.80e+02 1.85e-01 2.63e+03 9.99e+03 2 \n",
" 35 1.00e+04 7.51e+02 1.87e-01 2.62e+03 1.32e+04 2 \n",
" 36 1.00e+04 6.98e+02 1.87e-01 2.57e+03 1.15e+04 3 \n",
" 37 1.00e+04 6.96e+02 1.80e-01 2.50e+03 1.14e+04 4 \n",
" 38 1.00e+04 6.80e+02 1.76e-01 2.44e+03 1.07e+04 3 \n",
" 39 1.00e+04 6.55e+02 1.75e-01 2.40e+03 1.05e+04 3 \n",
" 40 1.00e+04 6.23e+02 1.77e-01 2.39e+03 1.02e+04 3 \n",
" 41 1.00e+04 6.13e+02 1.72e-01 2.33e+03 9.84e+03 4 \n",
" 42 1.00e+04 6.11e+02 1.68e-01 2.29e+03 9.12e+03 4 \n",
" 43 1.00e+04 6.06e+02 1.66e-01 2.27e+03 9.11e+03 5 \n",
" 44 1.00e+04 5.57e+02 1.65e-01 2.21e+03 8.40e+03 2 \n",
" 45 1.00e+04 5.39e+02 1.63e-01 2.17e+03 8.52e+03 4 \n",
" 46 1.00e+04 5.01e+02 1.63e-01 2.13e+03 9.35e+03 2 \n",
" 47 1.00e+04 4.86e+02 1.60e-01 2.09e+03 9.03e+03 4 \n",
" 48 1.00e+04 4.74e+02 1.58e-01 2.05e+03 8.97e+03 4 \n",
" 49 1.00e+04 4.66e+02 1.57e-01 2.03e+03 9.29e+03 4 \n",
" 50 1.00e+04 4.57e+02 1.55e-01 2.01e+03 8.60e+03 4 \n",
" 51 1.00e+04 4.54e+02 1.52e-01 1.97e+03 8.46e+03 4 \n",
" 52 1.00e+04 4.50e+02 1.51e-01 1.96e+03 8.55e+03 5 \n",
" 53 1.00e+04 4.00e+02 1.51e-01 1.91e+03 8.41e+03 2 \n",
" 54 1.00e+04 3.98e+02 1.47e-01 1.87e+03 7.88e+03 5 \n",
" 55 1.00e+04 3.71e+02 1.49e-01 1.87e+03 8.24e+03 3 \n",
" 56 1.00e+04 3.71e+02 1.47e-01 1.84e+03 7.98e+03 4 \n",
" 57 1.00e+04 3.46e+02 1.48e-01 1.82e+03 8.22e+03 2 \n",
" 58 1.00e+04 3.44e+02 1.45e-01 1.79e+03 7.69e+03 5 \n",
" 59 1.00e+04 3.38e+02 1.44e-01 1.78e+03 7.24e+03 4 \n",
" 60 1.00e+04 3.33e+02 1.44e-01 1.77e+03 7.13e+03 4 \n",
" 61 1.00e+04 3.26e+02 1.43e-01 1.76e+03 6.98e+03 4 \n",
" 62 1.00e+04 3.18e+02 1.44e-01 1.75e+03 7.03e+03 3 \n",
" 63 1.00e+04 3.11e+02 1.42e-01 1.73e+03 6.55e+03 4 \n",
" 64 1.00e+04 3.08e+02 1.42e-01 1.73e+03 6.82e+03 5 \n",
" 65 1.00e+04 3.06e+02 1.41e-01 1.72e+03 6.53e+03 5 \n",
" 66 1.00e+04 3.03e+02 1.41e-01 1.71e+03 6.44e+03 5 \n",
" 67 1.00e+04 2.95e+02 1.42e-01 1.71e+03 6.86e+03 4 \n",
" 68 1.00e+04 2.92e+02 1.41e-01 1.70e+03 6.84e+03 5 \n",
" 69 1.00e+04 2.85e+02 1.41e-01 1.69e+03 6.87e+03 3 \n",
" 70 1.00e+04 2.82e+02 1.40e-01 1.68e+03 6.51e+03 5 \n",
" 71 1.00e+04 2.71e+02 1.40e-01 1.67e+03 6.11e+03 3 \n",
" 72 1.00e+04 2.69e+02 1.39e-01 1.66e+03 6.14e+03 5 \n",
" 73 1.00e+04 2.67e+02 1.39e-01 1.66e+03 6.06e+03 5 \n",
" 74 1.00e+04 2.65e+02 1.39e-01 1.66e+03 6.32e+03 5 \n",
" 75 1.00e+04 2.64e+02 1.39e-01 1.66e+03 6.16e+03 5 \n",
" 76 1.00e+04 2.62e+02 1.39e-01 1.65e+03 6.21e+03 5 \n",
" 77 1.00e+04 2.60e+02 1.39e-01 1.65e+03 6.04e+03 5 \n",
" 78 1.00e+04 2.58e+02 1.39e-01 1.64e+03 5.98e+03 5 \n",
" 79 1.00e+04 2.52e+02 1.39e-01 1.64e+03 6.06e+03 3 \n",
" 80 1.00e+04 2.49e+02 1.39e-01 1.64e+03 5.94e+03 5 \n",
" 81 1.00e+04 2.44e+02 1.38e-01 1.63e+03 5.55e+03 4 \n",
" 82 1.00e+04 2.42e+02 1.39e-01 1.63e+03 5.71e+03 5 \n",
" 83 1.00e+04 2.36e+02 1.39e-01 1.62e+03 6.09e+03 3 \n",
" 84 1.00e+04 2.34e+02 1.38e-01 1.62e+03 5.49e+03 5 \n",
" 85 1.00e+04 2.32e+02 1.38e-01 1.62e+03 5.59e+03 5 \n",
" 86 1.00e+04 2.30e+02 1.39e-01 1.62e+03 4.95e+03 4 \n",
" 87 1.00e+04 2.28e+02 1.38e-01 1.61e+03 5.29e+03 5 \n",
" 88 1.00e+04 2.26e+02 1.38e-01 1.61e+03 4.95e+03 5 Skip BFGS \n",
" 89 1.00e+04 2.26e+02 1.38e-01 1.60e+03 4.91e+03 6 \n",
" 90 1.00e+04 2.24e+02 1.38e-01 1.60e+03 4.94e+03 5 \n",
" 91 1.00e+04 2.23e+02 1.38e-01 1.60e+03 4.86e+03 5 \n",
" 92 1.00e+04 2.22e+02 1.38e-01 1.60e+03 4.57e+03 6 \n",
" 93 1.00e+04 2.15e+02 1.38e-01 1.59e+03 4.67e+03 3 \n",
" 94 1.00e+04 2.14e+02 1.38e-01 1.59e+03 4.89e+03 5 \n",
" 95 1.00e+04 2.10e+02 1.38e-01 1.59e+03 4.60e+03 4 \n",
" 96 1.00e+04 2.08e+02 1.38e-01 1.59e+03 4.75e+03 4 \n",
" 97 1.00e+04 2.06e+02 1.38e-01 1.58e+03 4.52e+03 5 \n",
" 98 1.00e+04 2.05e+02 1.37e-01 1.58e+03 4.46e+03 5 Skip BFGS \n",
" 99 1.00e+04 2.04e+02 1.37e-01 1.58e+03 4.23e+03 6 \n",
" 100 1.00e+04 2.03e+02 1.37e-01 1.57e+03 4.08e+03 5 Skip BFGS \n",
"------------------------- STOP! -------------------------\n",
"1 : |fc-fOld| = 3.9421e+00 <= tolF*(1+|f0|) = 1.0009e+04\n",
"1 : |xc-x_last| = 4.3781e-03 <= tolX*(1+|x0|) = 1.0000e-01\n",
"0 : |proj(x-g)-x| = 4.0834e+03 <= tolG = 1.0000e-01\n",
"0 : |proj(x-g)-x| = 4.0834e+03 <= 1e3*eps = 1.0000e-02\n",
"1 : maxIter = 100 <= iter = 100\n",
"------------------------- DONE! -------------------------\n",
"The callback on the InexactGaussNewton Optimization was replaced.\n",
"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***\n",
"model has any nan: 0\n",
"============================ Inexact Gauss Newton ============================\n",
" # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n",
"-----------------------------------------------------------------------------\n",
"x0 has any nan: 0\n",
" 0 1.00e+04 1.00e+05 9.19e-03 1.00e+05 1.26e+06 0 Skip BFGS \n",
" 1 1.00e+04 6.29e+04 2.88e-02 6.32e+04 7.21e+04 0 Skip BFGS \n",
" 2 1.00e+04 3.05e+04 1.37e-01 3.19e+04 2.84e+04 0 \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/SimPEG/directives/directives.py:60: UserWarning: InversionDirective TargetMisfit has switched to a new inversion.\n",
" warnings.warn(\n",
"/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/pymatsolver/direct.py:23: PardisoTypeConversionWarning: Converting dia_matrix matrix to CSR format, will slow down.\n",
" self.solver = MKLPardisoSolver(\n",
"/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/pymatsolver/direct.py:73: PardisoTypeConversionWarning: Converting dia_matrix matrix to CSR format, will slow down.\n",
" self.solver.refactor(self.A)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 3 1.00e+04 1.83e+04 1.43e-01 1.97e+04 2.21e+04 0 Skip BFGS \n",
" 4 1.00e+04 1.40e+04 1.62e-01 1.57e+04 2.86e+04 0 \n",
" 5 1.00e+04 1.15e+04 1.17e-01 1.27e+04 2.55e+04 0 \n",
" 6 1.00e+04 8.69e+03 1.70e-01 1.04e+04 1.89e+04 0 \n",
" 7 1.00e+04 6.10e+03 1.82e-01 7.92e+03 2.05e+04 0 Skip BFGS \n",
" 8 1.00e+04 5.00e+03 1.66e-01 6.66e+03 1.55e+04 0 Skip BFGS \n",
" 9 1.00e+04 4.53e+03 1.44e-01 5.96e+03 2.16e+04 1 \n",
" 10 1.00e+04 4.48e+03 1.04e-01 5.52e+03 1.59e+04 0 \n",
" 11 1.00e+04 3.81e+03 1.11e-01 4.92e+03 1.42e+04 0 \n",
" 12 1.00e+04 3.23e+03 1.42e-01 4.64e+03 1.73e+04 1 \n",
" 13 1.00e+04 2.90e+03 1.32e-01 4.22e+03 1.62e+04 0 \n",
" 14 1.00e+04 2.31e+03 1.61e-01 3.93e+03 2.00e+04 1 \n",
" 15 1.00e+04 1.98e+03 1.58e-01 3.56e+03 4.49e+04 0 \n",
" 16 1.00e+04 1.90e+03 1.32e-01 3.22e+03 2.63e+04 1 \n",
" 17 1.00e+04 1.62e+03 1.42e-01 3.05e+03 3.03e+04 1 \n",
" 18 1.00e+04 1.37e+03 1.37e-01 2.74e+03 1.97e+04 2 \n",
" 19 1.00e+04 1.25e+03 1.22e-01 2.47e+03 1.06e+04 1 \n",
" 20 1.00e+04 1.00e+03 1.34e-01 2.35e+03 1.83e+04 1 \n",
" 21 1.00e+04 9.88e+02 1.19e-01 2.18e+03 1.78e+04 3 \n",
" 22 1.00e+04 9.85e+02 1.15e-01 2.13e+03 1.72e+04 3 \n",
" 23 1.00e+04 9.74e+02 1.11e-01 2.08e+03 1.72e+04 4 \n",
" 24 1.00e+04 9.68e+02 1.09e-01 2.06e+03 1.78e+04 4 \n",
" 25 1.00e+04 9.55e+02 1.06e-01 2.02e+03 1.74e+04 3 \n",
" 26 1.00e+04 9.59e+02 1.03e-01 1.99e+03 1.76e+04 4 \n",
" 27 1.00e+04 8.64e+02 1.04e-01 1.90e+03 1.78e+04 2 \n",
" 28 1.00e+04 8.10e+02 1.07e-01 1.88e+03 1.43e+04 1 \n",
" 29 1.00e+04 6.64e+02 1.03e-01 1.70e+03 1.25e+04 2 \n",
" 30 1.00e+04 6.47e+02 9.94e-02 1.64e+03 1.11e+04 4 \n",
" 31 1.00e+04 6.37e+02 9.97e-02 1.63e+03 1.17e+04 4 \n",
" 32 1.00e+04 6.08e+02 9.85e-02 1.59e+03 1.17e+04 2 \n",
" 33 1.00e+04 6.20e+02 9.53e-02 1.57e+03 1.20e+04 3 \n",
" 34 1.00e+04 5.37e+02 1.01e-01 1.55e+03 1.10e+04 2 \n",
" 35 1.00e+04 5.38e+02 9.69e-02 1.51e+03 1.20e+04 3 \n",
" 36 1.00e+04 5.16e+02 9.54e-02 1.47e+03 1.14e+04 3 \n",
" 37 1.00e+04 4.74e+02 9.94e-02 1.47e+03 1.05e+04 2 \n",
" 38 1.00e+04 4.23e+02 1.03e-01 1.46e+03 1.01e+04 2 \n",
" 39 1.00e+04 4.21e+02 8.92e-02 1.31e+03 9.34e+03 4 \n",
" 40 1.00e+04 3.98e+02 8.70e-02 1.27e+03 8.85e+03 3 \n",
" 41 1.00e+04 3.93e+02 8.69e-02 1.26e+03 8.84e+03 4 \n",
" 42 1.00e+04 3.89e+02 8.70e-02 1.26e+03 9.19e+03 3 \n",
" 43 1.00e+04 3.81e+02 8.65e-02 1.25e+03 9.52e+03 4 \n",
" 44 1.00e+04 3.82e+02 8.23e-02 1.20e+03 9.08e+03 4 \n",
" 45 1.00e+04 3.80e+02 8.08e-02 1.19e+03 8.55e+03 5 \n",
" 46 1.00e+04 3.74e+02 7.87e-02 1.16e+03 8.90e+03 5 \n",
" 47 1.00e+04 3.40e+02 7.94e-02 1.13e+03 9.07e+03 2 \n",
" 48 1.00e+04 3.28e+02 8.00e-02 1.13e+03 8.44e+03 4 \n",
" 49 1.00e+04 3.13e+02 7.50e-02 1.06e+03 8.42e+03 3 \n",
" 50 1.00e+04 3.10e+02 7.13e-02 1.02e+03 8.88e+03 5 \n",
" 51 1.00e+04 3.09e+02 6.78e-02 9.87e+02 7.50e+03 5 \n",
" 52 1.00e+04 3.05e+02 6.68e-02 9.73e+02 8.11e+03 5 \n",
" 53 1.00e+04 3.02e+02 6.70e-02 9.72e+02 7.99e+03 3 \n",
" 54 1.00e+04 2.87e+02 6.59e-02 9.46e+02 8.57e+03 4 \n",
" 55 1.00e+04 2.80e+02 6.51e-02 9.30e+02 8.40e+03 3 \n",
" 56 1.00e+04 2.59e+02 6.64e-02 9.23e+02 8.13e+03 3 \n",
" 57 1.00e+04 2.58e+02 6.64e-02 9.21e+02 8.05e+03 4 \n",
" 58 1.00e+04 2.54e+02 6.53e-02 9.06e+02 8.46e+03 5 \n",
" 59 1.00e+04 2.51e+02 6.29e-02 8.80e+02 7.38e+03 5 \n",
" 60 1.00e+04 2.49e+02 6.16e-02 8.65e+02 7.62e+03 5 \n",
" 61 1.00e+04 2.47e+02 6.13e-02 8.61e+02 7.33e+03 6 \n",
" 62 1.00e+04 2.43e+02 6.14e-02 8.57e+02 6.91e+03 4 \n",
" 63 1.00e+04 2.39e+02 6.16e-02 8.55e+02 7.55e+03 4 \n",
" 64 1.00e+04 2.35e+02 6.14e-02 8.49e+02 7.91e+03 4 \n",
" 65 1.00e+04 2.34e+02 6.08e-02 8.42e+02 7.35e+03 5 \n",
" 66 1.00e+04 2.30e+02 6.10e-02 8.40e+02 7.84e+03 4 \n",
" 67 1.00e+04 2.26e+02 5.97e-02 8.22e+02 6.47e+03 5 \n",
" 68 1.00e+04 2.07e+02 6.14e-02 8.21e+02 7.89e+03 2 \n",
" 69 1.00e+04 2.02e+02 5.83e-02 7.85e+02 6.95e+03 5 \n",
" 70 1.00e+04 1.90e+02 5.75e-02 7.65e+02 6.48e+03 3 \n",
" 71 1.00e+04 1.89e+02 5.76e-02 7.65e+02 7.16e+03 5 \n",
" 72 1.00e+04 1.88e+02 5.71e-02 7.59e+02 6.72e+03 5 \n",
" 73 1.00e+04 1.86e+02 5.71e-02 7.56e+02 6.74e+03 5 \n",
" 74 1.00e+04 1.82e+02 5.64e-02 7.46e+02 6.43e+03 4 \n",
" 75 1.00e+04 1.78e+02 5.62e-02 7.39e+02 5.63e+03 4 \n",
" 76 1.00e+04 1.76e+02 5.61e-02 7.37e+02 6.75e+03 5 \n",
" 77 1.00e+04 1.75e+02 5.61e-02 7.35e+02 6.24e+03 5 \n",
" 78 1.00e+04 1.72e+02 5.53e-02 7.25e+02 6.03e+03 5 \n",
" 79 1.00e+04 1.71e+02 5.49e-02 7.20e+02 5.20e+03 5 \n",
" 80 1.00e+04 1.69e+02 5.51e-02 7.20e+02 6.38e+03 5 \n",
" 81 1.00e+04 1.66e+02 5.52e-02 7.18e+02 6.42e+03 4 Skip BFGS \n",
" 82 1.00e+04 1.65e+02 5.49e-02 7.14e+02 6.41e+03 5 \n",
" 83 1.00e+04 1.63e+02 5.48e-02 7.10e+02 6.10e+03 4 \n",
" 84 1.00e+04 1.62e+02 5.42e-02 7.03e+02 5.65e+03 6 \n",
" 85 1.00e+04 1.61e+02 5.39e-02 7.00e+02 4.18e+03 6 Skip BFGS \n",
" 86 1.00e+04 1.61e+02 5.38e-02 6.99e+02 4.89e+03 6 \n",
" 87 1.00e+04 1.59e+02 5.38e-02 6.97e+02 4.66e+03 5 Skip BFGS \n",
" 88 1.00e+04 1.58e+02 5.38e-02 6.96e+02 5.31e+03 5 \n",
" 89 1.00e+04 1.57e+02 5.38e-02 6.95e+02 4.47e+03 5 \n",
" 90 1.00e+04 1.56e+02 5.39e-02 6.94e+02 5.37e+03 5 \n",
" 91 1.00e+04 1.51e+02 5.40e-02 6.91e+02 5.74e+03 3 \n",
" 92 1.00e+04 1.50e+02 5.37e-02 6.87e+02 5.35e+03 6 \n",
" 93 1.00e+04 1.13e+02 5.42e-02 6.55e+02 6.11e+03 0 Skip BFGS \n",
" 94 1.00e+04 1.13e+02 5.32e-02 6.45e+02 5.12e+03 5 \n",
" 95 1.00e+04 1.13e+02 5.30e-02 6.43e+02 5.26e+03 3 \n",
" 96 1.00e+04 1.11e+02 5.30e-02 6.42e+02 5.39e+03 5 \n",
" 97 1.00e+04 1.11e+02 5.28e-02 6.39e+02 5.42e+03 6 \n",
" 98 1.00e+04 1.10e+02 5.28e-02 6.38e+02 5.13e+03 5 Skip BFGS \n",
" 99 1.00e+04 1.10e+02 5.26e-02 6.36e+02 4.88e+03 6 \n",
" 100 1.00e+04 1.07e+02 5.28e-02 6.35e+02 6.00e+03 3 \n",
"------------------------- STOP! -------------------------\n",
"1 : |fc-fOld| = 1.5744e+00 <= tolF*(1+|f0|) = 1.0009e+04\n",
"1 : |xc-x_last| = 7.4404e-03 <= tolX*(1+|x0|) = 1.0000e-01\n",
"0 : |proj(x-g)-x| = 5.9960e+03 <= tolG = 1.0000e-01\n",
"0 : |proj(x-g)-x| = 5.9960e+03 <= 1e3*eps = 1.0000e-02\n",
"1 : maxIter = 100 <= iter = 100\n",
"------------------------- DONE! -------------------------\n",
"The callback on the InexactGaussNewton Optimization was replaced.\n",
"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***\n",
"model has any nan: 0\n",
"============================ Inexact Gauss Newton ============================\n",
" # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n",
"-----------------------------------------------------------------------------\n",
"x0 has any nan: 0\n",
" 0 1.00e+04 1.00e+05 9.24e-03 1.00e+05 1.26e+06 0 \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/SimPEG/directives/directives.py:60: UserWarning: InversionDirective TargetMisfit has switched to a new inversion.\n",
" warnings.warn(\n",
"/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/pymatsolver/direct.py:23: PardisoTypeConversionWarning: Converting dia_matrix matrix to CSR format, will slow down.\n",
" self.solver = MKLPardisoSolver(\n",
"/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/pymatsolver/direct.py:73: PardisoTypeConversionWarning: Converting dia_matrix matrix to CSR format, will slow down.\n",
" self.solver.refactor(self.A)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 1 1.00e+04 6.29e+04 2.07e-02 6.31e+04 7.16e+04 0 \n",
" 2 1.00e+04 3.06e+04 1.09e-01 3.17e+04 2.80e+04 0 \n",
" 3 1.00e+04 1.84e+04 1.22e-01 1.96e+04 2.15e+04 0 Skip BFGS \n",
" 4 1.00e+04 1.41e+04 1.45e-01 1.55e+04 2.93e+04 0 \n",
" 5 1.00e+04 1.14e+04 1.01e-01 1.24e+04 1.87e+04 0 \n",
" 6 1.00e+04 8.49e+03 1.47e-01 9.95e+03 1.84e+04 0 \n",
" 7 1.00e+04 5.99e+03 1.53e-01 7.53e+03 1.71e+04 0 Skip BFGS \n",
" 8 1.00e+04 4.92e+03 1.41e-01 6.33e+03 1.60e+04 0 Skip BFGS \n",
" 9 1.00e+04 4.54e+03 1.07e-01 5.62e+03 1.70e+04 1 \n",
" 10 1.00e+04 4.13e+03 1.28e-01 5.41e+03 2.27e+04 0 \n",
" 11 1.00e+04 3.52e+03 1.13e-01 4.64e+03 1.59e+04 0 \n",
" 12 1.00e+04 3.31e+03 1.00e-01 4.31e+03 2.03e+04 0 \n",
" 13 1.00e+04 3.10e+03 1.09e-01 4.19e+03 1.50e+04 0 \n",
" 14 1.00e+04 2.50e+03 1.50e-01 4.00e+03 1.66e+04 1 \n",
" 15 1.00e+04 1.92e+03 1.90e-01 3.82e+03 2.53e+04 0 \n",
" 16 1.00e+04 2.12e+03 1.29e-01 3.42e+03 2.60e+04 0 \n",
" 17 1.00e+04 1.60e+03 1.73e-01 3.32e+03 1.67e+04 1 \n",
" 18 1.00e+04 1.54e+03 1.60e-01 3.13e+03 1.28e+04 2 \n",
" 19 1.00e+04 1.60e+03 1.41e-01 3.01e+03 1.63e+04 0 \n",
" 20 1.00e+04 1.40e+03 1.37e-01 2.77e+03 1.29e+04 1 \n",
" 21 1.00e+04 1.41e+03 1.28e-01 2.69e+03 1.34e+04 2 \n",
" 22 1.00e+04 1.31e+03 1.36e-01 2.68e+03 1.13e+04 2 \n",
" 23 1.00e+04 1.27e+03 1.31e-01 2.58e+03 1.13e+04 2 \n",
" 24 1.00e+04 1.19e+03 1.31e-01 2.50e+03 1.07e+04 2 \n",
" 25 1.00e+04 1.15e+03 1.29e-01 2.44e+03 1.30e+04 2 \n",
" 26 1.00e+04 8.66e+02 1.51e-01 2.38e+03 1.27e+04 1 \n",
" 27 1.00e+04 7.35e+02 1.38e-01 2.12e+03 1.09e+04 1 \n",
" 28 1.00e+04 6.47e+02 1.38e-01 2.03e+03 1.48e+04 1 \n",
" 29 1.00e+04 5.91e+02 1.24e-01 1.83e+03 1.37e+04 2 \n",
" 30 1.00e+04 5.73e+02 1.12e-01 1.70e+03 1.48e+04 2 \n",
" 31 1.00e+04 5.53e+02 1.11e-01 1.66e+03 1.37e+04 3 \n",
" 32 1.00e+04 4.90e+02 1.05e-01 1.54e+03 1.08e+04 2 \n",
" 33 1.00e+04 4.80e+02 1.02e-01 1.50e+03 1.07e+04 4 \n",
" 34 1.00e+04 4.49e+02 1.01e-01 1.46e+03 1.08e+04 3 \n",
" 35 1.00e+04 4.46e+02 9.85e-02 1.43e+03 1.11e+04 4 \n",
" 36 1.00e+04 4.07e+02 9.82e-02 1.39e+03 1.08e+04 2 \n",
" 37 1.00e+04 3.94e+02 9.13e-02 1.31e+03 1.02e+04 4 \n",
" 38 1.00e+04 3.83e+02 8.70e-02 1.25e+03 1.02e+04 4 \n",
" 39 1.00e+04 3.70e+02 8.27e-02 1.20e+03 9.43e+03 3 \n",
" 40 1.00e+04 3.55e+02 7.50e-02 1.10e+03 9.01e+03 3 \n",
" 41 1.00e+04 3.53e+02 7.00e-02 1.05e+03 9.89e+03 4 \n",
" 42 1.00e+04 3.09e+02 6.55e-02 9.64e+02 9.01e+03 2 \n",
" 43 1.00e+04 2.95e+02 6.23e-02 9.18e+02 9.05e+03 4 \n",
" 44 1.00e+04 2.94e+02 5.98e-02 8.92e+02 8.98e+03 3 \n",
" 45 1.00e+04 2.82e+02 5.36e-02 8.18e+02 8.34e+03 4 \n",
" 46 1.00e+04 2.59e+02 5.03e-02 7.63e+02 9.01e+03 2 \n",
" 47 1.00e+04 2.49e+02 4.68e-02 7.17e+02 8.76e+03 4 \n",
" 48 1.00e+04 2.48e+02 4.49e-02 6.97e+02 8.17e+03 4 \n",
" 49 1.00e+04 2.35e+02 4.38e-02 6.72e+02 8.15e+03 3 \n",
" 50 1.00e+04 2.32e+02 4.27e-02 6.58e+02 8.01e+03 2 \n",
" 51 1.00e+04 2.13e+02 4.38e-02 6.51e+02 8.01e+03 3 \n",
" 52 1.00e+04 2.11e+02 4.35e-02 6.46e+02 8.55e+03 4 \n",
" 53 1.00e+04 2.15e+02 4.24e-02 6.39e+02 8.12e+03 4 \n",
" 54 1.00e+04 2.06e+02 4.25e-02 6.31e+02 8.28e+03 4 \n",
" 55 1.00e+04 2.00e+02 4.19e-02 6.18e+02 7.96e+03 4 \n",
" 56 1.00e+04 1.98e+02 4.07e-02 6.05e+02 7.50e+03 5 \n",
" 57 1.00e+04 1.97e+02 3.99e-02 5.96e+02 7.05e+03 5 \n",
" 58 1.00e+04 1.95e+02 3.92e-02 5.87e+02 7.04e+03 5 \n",
" 59 1.00e+04 1.93e+02 3.89e-02 5.82e+02 6.43e+03 4 \n",
" 60 1.00e+04 1.90e+02 3.91e-02 5.81e+02 6.84e+03 4 \n",
" 61 1.00e+04 1.83e+02 3.90e-02 5.74e+02 8.08e+03 4 \n",
" 62 1.00e+04 1.81e+02 3.79e-02 5.59e+02 7.32e+03 5 \n",
" 63 1.00e+04 1.76e+02 3.74e-02 5.50e+02 7.18e+03 3 \n",
" 64 1.00e+04 1.73e+02 3.67e-02 5.40e+02 7.03e+03 5 \n",
" 65 1.00e+04 1.69e+02 3.57e-02 5.26e+02 6.94e+03 3 \n",
" 66 1.00e+04 1.67e+02 3.52e-02 5.19e+02 6.49e+03 5 \n",
" 67 1.00e+04 1.38e+02 3.63e-02 5.01e+02 6.07e+03 1 Skip BFGS \n",
" 68 1.00e+04 1.35e+02 3.55e-02 4.90e+02 6.11e+03 4 \n",
" 69 1.00e+04 1.35e+02 3.50e-02 4.86e+02 5.74e+03 5 \n",
" 70 1.00e+04 1.33e+02 3.47e-02 4.80e+02 5.46e+03 5 \n",
" 71 1.00e+04 1.34e+02 3.45e-02 4.79e+02 5.84e+03 4 \n",
" 72 1.00e+04 1.32e+02 3.37e-02 4.69e+02 5.59e+03 5 Skip BFGS \n",
" 73 1.00e+04 1.30e+02 3.35e-02 4.66e+02 5.63e+03 5 \n",
" 74 1.00e+04 1.29e+02 3.34e-02 4.63e+02 5.66e+03 5 \n",
" 75 1.00e+04 1.27e+02 3.34e-02 4.61e+02 5.58e+03 4 \n",
" 76 1.00e+04 1.18e+02 3.36e-02 4.54e+02 6.20e+03 2 \n",
" 77 1.00e+04 1.20e+02 3.34e-02 4.54e+02 6.38e+03 4 \n",
" 78 1.00e+04 1.16e+02 3.33e-02 4.49e+02 6.07e+03 4 \n",
" 79 1.00e+04 1.13e+02 3.35e-02 4.47e+02 6.08e+03 4 \n",
" 80 1.00e+04 1.07e+02 3.31e-02 4.38e+02 5.39e+03 3 \n",
" 81 1.00e+04 1.07e+02 3.27e-02 4.34e+02 5.09e+03 6 \n",
" 82 1.00e+04 1.04e+02 3.28e-02 4.33e+02 4.86e+03 4 Skip BFGS \n",
" 83 1.00e+04 1.04e+02 3.27e-02 4.31e+02 5.21e+03 5 \n",
" 84 1.00e+04 1.01e+02 3.27e-02 4.28e+02 5.10e+03 3 \n",
" 85 1.00e+04 1.00e+02 3.28e-02 4.28e+02 5.35e+03 5 \n",
" 86 1.00e+04 9.97e+01 3.26e-02 4.26e+02 5.11e+03 5 \n",
" 87 1.00e+04 9.98e+01 3.25e-02 4.25e+02 5.05e+03 5 \n",
" 88 1.00e+04 9.60e+01 3.29e-02 4.25e+02 5.06e+03 2 Skip BFGS \n",
" 89 1.00e+04 9.58e+01 3.23e-02 4.19e+02 4.65e+03 6 \n",
" 90 1.00e+04 9.65e+01 3.20e-02 4.17e+02 4.48e+03 4 Skip BFGS \n",
" 91 1.00e+04 9.51e+01 3.17e-02 4.12e+02 4.16e+03 5 \n",
" 92 1.00e+04 9.49e+01 3.15e-02 4.10e+02 3.98e+03 6 \n",
" 93 1.00e+04 9.45e+01 3.15e-02 4.09e+02 3.83e+03 6 \n",
" 94 1.00e+04 9.44e+01 3.13e-02 4.08e+02 3.56e+03 6 Skip BFGS \n",
" 95 1.00e+04 9.41e+01 3.13e-02 4.07e+02 3.52e+03 5 \n",
" 96 1.00e+04 9.23e+01 3.14e-02 4.06e+02 3.84e+03 4 \n",
" 97 1.00e+04 9.18e+01 3.13e-02 4.05e+02 3.66e+03 6 \n",
" 98 1.00e+04 9.00e+01 3.10e-02 4.01e+02 3.27e+03 4 Skip BFGS \n",
" 99 1.00e+04 8.97e+01 3.09e-02 3.99e+02 3.02e+03 6 \n",
" 100 1.00e+04 8.92e+01 3.09e-02 3.98e+02 3.03e+03 5 \n",
"------------------------- STOP! -------------------------\n",
"1 : |fc-fOld| = 6.4978e-01 <= tolF*(1+|f0|) = 1.0009e+04\n",
"1 : |xc-x_last| = 1.7763e-03 <= tolX*(1+|x0|) = 1.0000e-01\n",
"0 : |proj(x-g)-x| = 3.0323e+03 <= tolG = 1.0000e-01\n",
"0 : |proj(x-g)-x| = 3.0323e+03 <= 1e3*eps = 1.0000e-02\n",
"1 : maxIter = 100 <= iter = 100\n",
"------------------------- DONE! -------------------------\n",
"The callback on the InexactGaussNewton Optimization was replaced.\n",
"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***\n",
"model has any nan: 0\n",
"============================ Inexact Gauss Newton ============================\n",
" # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n",
"-----------------------------------------------------------------------------\n",
"x0 has any nan: 0\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/SimPEG/directives/directives.py:60: UserWarning: InversionDirective TargetMisfit has switched to a new inversion.\n",
" warnings.warn(\n",
"/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/pymatsolver/direct.py:23: PardisoTypeConversionWarning: Converting dia_matrix matrix to CSR format, will slow down.\n",
" self.solver = MKLPardisoSolver(\n",
"/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/pymatsolver/direct.py:73: PardisoTypeConversionWarning: Converting dia_matrix matrix to CSR format, will slow down.\n",
" self.solver.refactor(self.A)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0 1.00e+04 1.00e+05 9.39e-03 1.00e+05 1.26e+06 0 \n",
" 1 1.00e+04 6.29e+04 2.35e-02 6.32e+04 7.21e+04 0 \n",
" 2 1.00e+04 3.06e+04 7.37e-02 3.13e+04 2.79e+04 0 \n",
" 3 1.00e+04 1.84e+04 8.77e-02 1.93e+04 2.06e+04 0 Skip BFGS \n",
" 4 1.00e+04 1.39e+04 1.07e-01 1.50e+04 2.82e+04 0 \n",
" 5 1.00e+04 1.15e+04 7.35e-02 1.22e+04 1.94e+04 0 \n",
" 6 1.00e+04 8.65e+03 1.12e-01 9.77e+03 1.70e+04 0 \n",
" 7 1.00e+04 6.15e+03 1.32e-01 7.46e+03 1.38e+04 0 Skip BFGS \n",
" 8 1.00e+04 4.97e+03 1.24e-01 6.21e+03 1.47e+04 0 Skip BFGS \n",
" 9 1.00e+04 4.78e+03 1.16e-01 5.93e+03 1.97e+04 0 \n",
" 10 1.00e+04 3.96e+03 8.94e-02 4.85e+03 1.60e+04 0 \n",
" 11 1.00e+04 3.59e+03 8.02e-02 4.39e+03 1.26e+04 1 \n",
" 12 1.00e+04 3.25e+03 1.03e-01 4.28e+03 1.42e+04 0 \n",
" 13 1.00e+04 2.93e+03 8.58e-02 3.79e+03 1.53e+04 0 \n",
" 14 1.00e+04 2.71e+03 8.59e-02 3.57e+03 1.45e+04 0 \n",
" 15 1.00e+04 1.73e+03 1.55e-01 3.28e+03 1.86e+04 0 \n",
" 16 1.00e+04 1.50e+03 1.37e-01 2.87e+03 1.47e+04 2 \n",
" 17 1.00e+04 1.44e+03 1.13e-01 2.57e+03 1.36e+04 3 \n",
" 18 1.00e+04 1.37e+03 1.14e-01 2.51e+03 1.28e+04 3 \n",
" 19 1.00e+04 1.27e+03 1.07e-01 2.34e+03 1.18e+04 1 \n",
" 20 1.00e+04 1.08e+03 1.11e-01 2.19e+03 1.25e+04 1 \n",
" 21 1.00e+04 9.80e+02 1.08e-01 2.06e+03 1.16e+04 2 \n",
" 22 1.00e+04 9.41e+02 1.03e-01 1.97e+03 1.02e+04 2 \n",
" 23 1.00e+04 9.30e+02 9.48e-02 1.88e+03 1.07e+04 2 \n",
" 24 1.00e+04 8.91e+02 8.98e-02 1.79e+03 1.05e+04 3 \n",
" 25 1.00e+04 8.78e+02 8.45e-02 1.72e+03 1.14e+04 3 \n",
" 26 1.00e+04 7.35e+02 8.09e-02 1.54e+03 1.03e+04 1 \n",
" 27 1.00e+04 5.95e+02 8.33e-02 1.43e+03 1.35e+04 1 \n",
" 28 1.00e+04 5.47e+02 8.20e-02 1.37e+03 1.26e+04 3 \n",
" 29 1.00e+04 5.33e+02 6.43e-02 1.18e+03 1.16e+04 3 \n",
" 30 1.00e+04 4.85e+02 6.28e-02 1.11e+03 9.95e+03 2 \n",
" 31 1.00e+04 4.57e+02 6.41e-02 1.10e+03 1.02e+04 3 \n",
" 32 1.00e+04 4.26e+02 5.81e-02 1.01e+03 1.10e+04 1 \n",
" 33 1.00e+04 4.20e+02 5.33e-02 9.53e+02 1.08e+04 4 \n",
" 34 1.00e+04 4.32e+02 4.77e-02 9.09e+02 1.08e+04 4 \n",
" 35 1.00e+04 3.76e+02 5.18e-02 8.94e+02 1.03e+04 2 \n",
" 36 1.00e+04 3.36e+02 5.47e-02 8.83e+02 9.95e+03 3 \n",
" 37 1.00e+04 3.35e+02 5.04e-02 8.38e+02 9.60e+03 4 \n",
" 38 1.00e+04 3.04e+02 5.14e-02 8.18e+02 9.76e+03 3 \n",
" 39 1.00e+04 3.00e+02 4.73e-02 7.73e+02 9.10e+03 4 \n",
" 40 1.00e+04 2.96e+02 4.47e-02 7.43e+02 9.09e+03 5 \n",
" 41 1.00e+04 2.89e+02 4.32e-02 7.21e+02 9.00e+03 3 \n",
" 42 1.00e+04 2.77e+02 4.25e-02 7.02e+02 8.85e+03 4 \n",
" 43 1.00e+04 2.68e+02 4.10e-02 6.78e+02 9.09e+03 4 \n",
" 44 1.00e+04 2.65e+02 3.98e-02 6.63e+02 8.61e+03 5 \n",
" 45 1.00e+04 2.51e+02 4.04e-02 6.54e+02 8.29e+03 3 \n",
" 46 1.00e+04 2.44e+02 3.95e-02 6.39e+02 8.27e+03 4 \n",
" 47 1.00e+04 2.26e+02 3.83e-02 6.09e+02 8.02e+03 2 \n",
" 48 1.00e+04 2.01e+02 3.55e-02 5.56e+02 8.71e+03 2 \n",
" 49 1.00e+04 1.93e+02 3.57e-02 5.50e+02 8.65e+03 4 \n",
" 50 1.00e+04 1.87e+02 3.42e-02 5.29e+02 7.83e+03 4 \n",
" 51 1.00e+04 1.84e+02 3.39e-02 5.23e+02 8.04e+03 4 \n",
" 52 1.00e+04 1.84e+02 3.37e-02 5.20e+02 8.40e+03 3 \n",
" 53 1.00e+04 1.74e+02 3.21e-02 4.95e+02 7.79e+03 4 \n",
" 54 1.00e+04 1.32e+02 3.60e-02 4.92e+02 8.65e+03 1 \n",
" 55 1.00e+04 1.21e+02 2.98e-02 4.19e+02 7.22e+03 3 \n",
" 56 1.00e+04 1.17e+02 2.93e-02 4.10e+02 7.61e+03 4 \n",
" 57 1.00e+04 1.16e+02 2.81e-02 3.97e+02 7.12e+03 5 \n",
" 58 1.00e+04 1.16e+02 2.75e-02 3.91e+02 6.90e+03 5 \n",
" 59 1.00e+04 1.09e+02 2.78e-02 3.87e+02 7.01e+03 3 \n",
" 60 1.00e+04 9.97e+01 2.75e-02 3.75e+02 7.02e+03 3 \n",
" 61 1.00e+04 9.86e+01 2.42e-02 3.40e+02 6.54e+03 4 \n",
" 62 1.00e+04 9.58e+01 2.29e-02 3.25e+02 9.40e+03 0 Skip BFGS \n",
" 63 1.00e+04 9.58e+01 2.26e-02 3.22e+02 9.24e+03 6 \n",
" 64 1.00e+04 9.31e+01 2.21e-02 3.14e+02 8.85e+03 5 \n",
" 65 1.00e+04 8.90e+01 2.25e-02 3.14e+02 9.64e+03 3 Skip BFGS \n",
" 66 1.00e+04 8.08e+01 2.28e-02 3.09e+02 7.88e+03 2 \n",
" 67 1.00e+04 8.15e+01 2.26e-02 3.08e+02 7.66e+03 5 \n",
" 68 1.00e+04 7.91e+01 2.21e-02 3.00e+02 7.23e+03 5 \n",
" 69 1.00e+04 7.43e+01 2.23e-02 2.97e+02 7.78e+03 3 \n",
" 70 1.00e+04 7.52e+01 2.14e-02 2.89e+02 8.75e+03 3 \n",
" 71 1.00e+04 7.07e+01 2.13e-02 2.84e+02 7.90e+03 4 \n",
" 72 1.00e+04 7.00e+01 2.11e-02 2.81e+02 7.84e+03 5 \n",
" 73 1.00e+04 6.94e+01 2.11e-02 2.80e+02 8.15e+03 4 \n",
" 74 1.00e+04 6.70e+01 2.10e-02 2.77e+02 7.96e+03 5 \n",
" 75 1.00e+04 6.51e+01 2.01e-02 2.66e+02 7.09e+03 3 \n",
" 76 1.00e+04 6.36e+01 2.01e-02 2.64e+02 7.20e+03 5 \n",
" 77 1.00e+04 5.46e+01 2.07e-02 2.61e+02 7.72e+03 2 \n",
" 78 1.00e+04 5.20e+01 2.07e-02 2.59e+02 7.43e+03 4 \n",
" 79 1.00e+04 5.15e+01 2.03e-02 2.54e+02 6.69e+03 5 \n",
" 80 1.00e+04 5.12e+01 2.00e-02 2.51e+02 6.79e+03 6 \n",
" 81 1.00e+04 5.09e+01 1.97e-02 2.48e+02 6.47e+03 5 Skip BFGS \n",
" 82 1.00e+04 5.03e+01 1.95e-02 2.46e+02 6.51e+03 5 \n",
" 83 1.00e+04 4.99e+01 1.93e-02 2.43e+02 6.14e+03 6 \n",
" 84 1.00e+04 4.95e+01 1.93e-02 2.42e+02 6.06e+03 6 \n",
" 85 1.00e+04 4.88e+01 1.86e-02 2.35e+02 5.90e+03 5 Skip BFGS \n",
" 86 1.00e+04 4.84e+01 1.82e-02 2.30e+02 5.63e+03 6 \n",
" 87 1.00e+04 4.70e+01 1.80e-02 2.27e+02 5.76e+03 4 Skip BFGS \n",
" 88 1.00e+04 4.63e+01 1.79e-02 2.26e+02 5.56e+03 5 \n",
" 89 1.00e+04 4.48e+01 1.80e-02 2.25e+02 5.59e+03 4 \n",
" 90 1.00e+04 4.42e+01 1.80e-02 2.24e+02 5.69e+03 5 \n",
" 91 1.00e+04 4.14e+01 1.76e-02 2.18e+02 5.39e+03 3 \n",
" 92 1.00e+04 4.08e+01 1.75e-02 2.16e+02 5.64e+03 5 \n",
" 93 1.00e+04 4.04e+01 1.67e-02 2.08e+02 4.63e+03 6 \n",
" 94 1.00e+04 3.94e+01 1.62e-02 2.01e+02 3.99e+03 5 Skip BFGS \n",
" 95 1.00e+04 3.92e+01 1.60e-02 1.99e+02 3.65e+03 6 \n",
" 96 1.00e+04 3.88e+01 1.60e-02 1.99e+02 3.85e+03 5 Skip BFGS \n",
" 97 1.00e+04 3.85e+01 1.59e-02 1.98e+02 3.54e+03 6 \n",
" 98 1.00e+04 3.84e+01 1.59e-02 1.97e+02 3.41e+03 6 \n",
" 99 1.00e+04 3.79e+01 1.59e-02 1.97e+02 3.50e+03 5 \n",
" 100 1.00e+04 3.75e+01 1.58e-02 1.96e+02 3.53e+03 5 Skip BFGS \n",
"------------------------- STOP! -------------------------\n",
"1 : |fc-fOld| = 7.2861e-01 <= tolF*(1+|f0|) = 1.0009e+04\n",
"1 : |xc-x_last| = 2.0508e-03 <= tolX*(1+|x0|) = 1.0000e-01\n",
"0 : |proj(x-g)-x| = 3.5288e+03 <= tolG = 1.0000e-01\n",
"0 : |proj(x-g)-x| = 3.5288e+03 <= 1e3*eps = 1.0000e-02\n",
"1 : maxIter = 100 <= iter = 100\n",
"------------------------- DONE! -------------------------\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/SimPEG/directives/directives.py:60: UserWarning: InversionDirective TargetMisfit has switched to a new inversion.\n",
" warnings.warn(\n",
"/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/pymatsolver/direct.py:23: PardisoTypeConversionWarning: Converting dia_matrix matrix to CSR format, will slow down.\n",
" self.solver = MKLPardisoSolver(\n",
"/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/pymatsolver/direct.py:73: PardisoTypeConversionWarning: Converting dia_matrix matrix to CSR format, will slow down.\n",
" self.solver.refactor(self.A)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The callback on the InexactGaussNewton Optimization was replaced.\n",
"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***\n",
"model has any nan: 0\n",
"============================ Inexact Gauss Newton ============================\n",
" # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n",
"-----------------------------------------------------------------------------\n",
"x0 has any nan: 0\n",
" 0 1.00e+04 1.00e+05 9.58e-03 1.00e+05 1.26e+06 0 Skip BFGS \n",
" 1 1.00e+04 6.29e+04 1.99e-02 6.31e+04 7.15e+04 0 Skip BFGS \n",
" 2 1.00e+04 3.06e+04 7.97e-02 3.14e+04 2.78e+04 0 \n",
" 3 1.00e+04 1.85e+04 1.09e-01 1.96e+04 2.10e+04 0 Skip BFGS \n",
" 4 1.00e+04 1.42e+04 1.26e-01 1.55e+04 2.84e+04 0 \n",
" 5 1.00e+04 1.17e+04 9.14e-02 1.26e+04 2.00e+04 0 \n",
" 6 1.00e+04 8.82e+03 1.25e-01 1.01e+04 1.74e+04 0 \n",
" 7 1.00e+04 6.17e+03 1.37e-01 7.55e+03 1.46e+04 0 Skip BFGS \n",
" 8 1.00e+04 4.98e+03 1.16e-01 6.14e+03 1.61e+04 0 Skip BFGS \n",
" 9 1.00e+04 4.68e+03 1.08e-01 5.76e+03 2.03e+04 0 \n",
" 10 1.00e+04 3.97e+03 7.60e-02 4.73e+03 1.74e+04 0 \n",
" 11 1.00e+04 3.65e+03 6.43e-02 4.29e+03 1.47e+04 0 \n",
" 12 1.00e+04 3.32e+03 8.02e-02 4.12e+03 1.44e+04 0 \n",
" 13 1.00e+04 3.12e+03 6.44e-02 3.76e+03 1.23e+04 0 \n",
" 14 1.00e+04 2.54e+03 1.16e-01 3.71e+03 1.73e+04 0 \n",
" 15 1.00e+04 2.01e+03 1.23e-01 3.25e+03 1.30e+04 0 \n",
" 16 1.00e+04 1.98e+03 9.39e-02 2.91e+03 1.36e+04 1 \n",
" 17 1.00e+04 1.55e+03 1.36e-01 2.91e+03 1.49e+04 0 \n",
" 18 1.00e+04 1.31e+03 1.21e-01 2.52e+03 1.03e+04 1 \n",
" 19 1.00e+04 1.31e+03 1.11e-01 2.42e+03 1.11e+04 1 \n",
" 20 1.00e+04 1.26e+03 1.15e-01 2.41e+03 1.14e+04 2 \n",
" 21 1.00e+04 1.22e+03 1.09e-01 2.31e+03 1.12e+04 2 \n",
" 22 1.00e+04 1.08e+03 1.15e-01 2.24e+03 9.90e+03 1 \n",
" 23 1.00e+04 1.02e+03 1.16e-01 2.19e+03 1.17e+04 1 \n",
" 24 1.00e+04 9.68e+02 1.10e-01 2.07e+03 1.02e+04 1 \n",
" 25 1.00e+04 9.83e+02 1.07e-01 2.05e+03 1.04e+04 3 \n",
" 26 1.00e+04 8.10e+02 1.02e-01 1.83e+03 9.95e+03 2 \n",
" 27 1.00e+04 7.07e+02 9.70e-02 1.68e+03 1.19e+04 1 \n",
" 28 1.00e+04 5.73e+02 9.91e-02 1.56e+03 1.13e+04 1 \n",
" 29 1.00e+04 5.12e+02 1.01e-01 1.53e+03 1.02e+04 2 \n",
" 30 1.00e+04 4.98e+02 8.85e-02 1.38e+03 9.62e+03 3 \n",
" 31 1.00e+04 4.96e+02 8.14e-02 1.31e+03 9.53e+03 3 \n",
" 32 1.00e+04 4.96e+02 7.59e-02 1.26e+03 9.37e+03 3 \n",
" 33 1.00e+04 4.78e+02 6.52e-02 1.13e+03 9.57e+03 2 \n",
" 34 1.00e+04 4.42e+02 6.05e-02 1.05e+03 8.61e+03 3 \n",
" 35 1.00e+04 4.36e+02 5.77e-02 1.01e+03 8.00e+03 5 \n",
" 36 1.00e+04 4.05e+02 5.92e-02 9.96e+02 9.00e+03 2 \n",
" 37 1.00e+04 3.90e+02 5.56e-02 9.46e+02 8.70e+03 4 \n",
" 38 1.00e+04 3.55e+02 5.84e-02 9.38e+02 8.81e+03 2 \n",
" 39 1.00e+04 3.46e+02 5.81e-02 9.26e+02 8.56e+03 4 \n",
" 40 1.00e+04 3.43e+02 5.43e-02 8.86e+02 8.39e+03 4 \n",
" 41 1.00e+04 3.50e+02 5.26e-02 8.76e+02 8.98e+03 3 \n",
" 42 1.00e+04 3.17e+02 5.17e-02 8.34e+02 8.68e+03 3 \n",
" 43 1.00e+04 3.13e+02 4.85e-02 7.98e+02 8.33e+03 5 \n",
" 44 1.00e+04 3.12e+02 4.79e-02 7.91e+02 8.78e+03 3 \n",
" 45 1.00e+04 3.01e+02 4.49e-02 7.50e+02 8.18e+03 4 \n",
" 46 1.00e+04 2.56e+02 4.18e-02 6.73e+02 7.66e+03 2 \n",
" 47 1.00e+04 2.53e+02 4.07e-02 6.60e+02 7.74e+03 5 \n",
" 48 1.00e+04 2.37e+02 3.96e-02 6.33e+02 7.22e+03 3 \n",
" 49 1.00e+04 2.36e+02 3.85e-02 6.21e+02 6.89e+03 5 \n",
" 50 1.00e+04 2.35e+02 3.86e-02 6.20e+02 7.25e+03 5 \n",
" 51 1.00e+04 2.29e+02 3.83e-02 6.12e+02 7.41e+03 4 \n",
" 52 1.00e+04 2.26e+02 3.70e-02 5.96e+02 6.95e+03 4 \n",
" 53 1.00e+04 2.18e+02 3.55e-02 5.72e+02 6.89e+03 4 \n",
" 54 1.00e+04 2.14e+02 3.50e-02 5.64e+02 6.45e+03 4 \n",
" 55 1.00e+04 2.14e+02 3.39e-02 5.53e+02 6.17e+03 4 \n",
" 56 1.00e+04 1.97e+02 3.49e-02 5.45e+02 6.39e+03 2 \n",
" 57 1.00e+04 1.89e+02 3.35e-02 5.23e+02 6.34e+03 2 \n",
" 58 1.00e+04 1.53e+02 3.39e-02 4.92e+02 7.54e+03 0 \n",
" 59 1.00e+04 1.21e+02 3.42e-02 4.63e+02 9.42e+03 0 \n",
" 60 1.00e+04 1.01e+02 3.43e-02 4.45e+02 6.85e+03 2 \n",
" 61 1.00e+04 1.01e+02 3.36e-02 4.37e+02 6.11e+03 4 \n",
" 62 1.00e+04 1.01e+02 3.32e-02 4.33e+02 5.79e+03 5 \n",
" 63 1.00e+04 9.93e+01 3.30e-02 4.29e+02 6.07e+03 4 \n",
" 64 1.00e+04 9.71e+01 3.30e-02 4.27e+02 5.95e+03 3 \n",
" 65 1.00e+04 9.46e+01 3.23e-02 4.18e+02 6.66e+03 2 \n",
" 66 1.00e+04 8.67e+01 3.29e-02 4.16e+02 6.11e+03 2 \n",
" 67 1.00e+04 8.55e+01 3.26e-02 4.12e+02 5.83e+03 5 \n",
" 68 1.00e+04 8.54e+01 3.24e-02 4.10e+02 6.05e+03 4 \n",
" 69 1.00e+04 8.56e+01 3.22e-02 4.08e+02 5.54e+03 5 \n",
" 70 1.00e+04 8.34e+01 3.21e-02 4.05e+02 6.26e+03 4 \n",
" 71 1.00e+04 8.23e+01 3.22e-02 4.04e+02 6.08e+03 4 Skip BFGS \n",
" 72 1.00e+04 8.13e+01 3.19e-02 4.00e+02 6.22e+03 5 \n",
" 73 1.00e+04 8.10e+01 3.18e-02 3.99e+02 5.90e+03 5 \n",
" 74 1.00e+04 8.07e+01 3.12e-02 3.92e+02 5.51e+03 6 \n",
" 75 1.00e+04 8.03e+01 3.11e-02 3.91e+02 5.21e+03 5 \n",
" 76 1.00e+04 8.00e+01 3.10e-02 3.90e+02 5.22e+03 6 \n",
" 77 1.00e+04 7.91e+01 3.09e-02 3.88e+02 5.23e+03 4 \n",
" 78 1.00e+04 7.81e+01 3.09e-02 3.87e+02 5.10e+03 5 \n",
" 79 1.00e+04 7.80e+01 3.07e-02 3.85e+02 5.13e+03 5 \n",
" 80 1.00e+04 7.68e+01 3.06e-02 3.83e+02 4.88e+03 5 \n",
" 81 1.00e+04 7.67e+01 3.05e-02 3.81e+02 4.42e+03 5 \n",
" 82 1.00e+04 7.55e+01 3.05e-02 3.80e+02 4.76e+03 5 \n",
" 83 1.00e+04 7.51e+01 3.03e-02 3.79e+02 4.72e+03 5 Skip BFGS \n",
" 84 1.00e+04 7.46e+01 3.02e-02 3.77e+02 4.53e+03 6 \n",
" 85 1.00e+04 7.42e+01 2.99e-02 3.73e+02 4.33e+03 5 Skip BFGS \n",
" 86 1.00e+04 7.40e+01 2.99e-02 3.73e+02 4.49e+03 5 \n",
" 87 1.00e+04 7.36e+01 2.96e-02 3.70e+02 4.01e+03 6 \n",
" 88 1.00e+04 7.35e+01 2.95e-02 3.69e+02 3.92e+03 6 \n",
" 89 1.00e+04 7.33e+01 2.95e-02 3.69e+02 3.94e+03 5 Skip BFGS \n",
" 90 1.00e+04 7.32e+01 2.94e-02 3.67e+02 3.89e+03 6 \n",
" 91 1.00e+04 7.28e+01 2.94e-02 3.67e+02 4.24e+03 4 \n",
" 92 1.00e+04 7.23e+01 2.92e-02 3.65e+02 3.91e+03 6 \n",
" 93 1.00e+04 7.19e+01 2.93e-02 3.64e+02 4.09e+03 4 \n",
" 94 1.00e+04 7.07e+01 2.92e-02 3.63e+02 4.09e+03 5 \n",
" 95 1.00e+04 7.00e+01 2.92e-02 3.62e+02 4.74e+03 3 \n",
" 96 1.00e+04 6.89e+01 2.89e-02 3.58e+02 4.36e+03 5 \n",
" 97 1.00e+04 6.88e+01 2.88e-02 3.57e+02 4.17e+03 6 \n",
" 98 1.00e+04 6.85e+01 2.88e-02 3.57e+02 4.08e+03 5 Skip BFGS \n",
" 99 1.00e+04 6.84e+01 2.87e-02 3.56e+02 4.01e+03 6 \n",
" 100 1.00e+04 6.82e+01 2.87e-02 3.55e+02 3.99e+03 4 Skip BFGS \n",
"------------------------- STOP! -------------------------\n",
"1 : |fc-fOld| = 6.9116e-01 <= tolF*(1+|f0|) = 1.0010e+04\n",
"1 : |xc-x_last| = 3.9399e-03 <= tolX*(1+|x0|) = 1.0000e-01\n",
"0 : |proj(x-g)-x| = 3.9878e+03 <= tolG = 1.0000e-01\n",
"0 : |proj(x-g)-x| = 3.9878e+03 <= 1e3*eps = 1.0000e-02\n",
"1 : maxIter = 100 <= iter = 100\n",
"------------------------- DONE! -------------------------\n"
]
},
{
"data": {
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"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax_ls = plt.subplots(2,3, figsize=(12, 6))\n",
"wav_list = ['db1', 'db2', 'db3', 'db4', 'db5', 'db6']\n",
"betalist = [1e3, 1e4, 1e4, 1e4, 1e4, 1e4]\n",
"for idx, wav in enumerate(wav_list):\n",
" reg = regularization.WaveletRegularization1D(mesh, wav=wav)\n",
" inv_prob = inverse_problem.BaseInvProblem(dmis, reg, opt)\n",
" inv_prob.beta = betalist[idx]\n",
" inv = inversion.BaseInversion(inv_prob, directives_list)\n",
"\n",
" recovered_model = inv.run(starting_model)\n",
"\n",
" ax_ls[idx//3, idx%3].plot(mesh.vectorCCx, true_model, \"b-\")\n",
" ax_ls[idx//3, idx%3].plot(mesh.vectorCCx, recovered_model, \"r-\")\n",
" ax_ls[idx//3, idx%3].legend((\"True Model\", \"Recovered Model\"))\n",
" # ax[1].set_ylim([-2, 2])\n",
" ax_ls[idx//3, idx%3].set_title(\"Wavelet-type \" + reg.wavelets.wav)\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
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