notebooks/2_TEM_VerticalConductor_1D_stitched_inversion.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform a 1D inversion \n",
    "\n",
    "This notebook reproduces the 1D inversion (Figure 5) shown in \n",
    "\n",
    "Heagy, L., Kang, S., Cockett, R., and Oldenburg, D., 2018, _Open source software for simulations and inversions of airborne electromagnetic data_, AEM 2018 International Workshop on Airborne Electromagnetics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.constants import mu_0\n",
    "from scipy.interpolate import griddata\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "from matplotlib.colors import LogNorm\n",
    "\n",
    "from pymatsolver import Pardiso\n",
    "from SimPEG import (EM, Mesh, Maps, SolverLU, DataMisfit, Regularization,\n",
    "                    Optimization, InvProblem, Inversion, Directives, Utils)\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.rcParams['font.size'] = 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dobs = np.load(\"dobs.npy\")\n",
    "x = np.load('x.npy')\n",
    "times = np.logspace(np.log10(5e-5), np.log10(2.5e-3), 21)\n",
    "nSrc = x.size\n",
    "DOBS = dobs.reshape((nSrc, 2, times.size))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the forward simulation mesh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_1d_cyl_mesh():\n",
    "    cs, ncx, ncz, npad = 50., 10, 10, 10\n",
    "    pad_rate = 1.3\n",
    "    hx = [(cs, ncx),  (cs, npad, pad_rate)]\n",
    "    hz = [(cs, npad, -pad_rate), (cs, ncz), (cs, npad, pad_rate)]\n",
    "    mesh = Mesh.CylMesh([hx, 1, hz], '00C')\n",
    "    return mesh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_1d_tem_inversion(args):\n",
    "    sig0, dobs, std, eps, rxloc, srcloc, times = args\n",
    "    mesh = get_1d_cyl_mesh()\n",
    "    active = mesh.vectorCCz < 0.\n",
    "    actMap = Maps.InjectActiveCells(mesh, active, np.log(1e-8), nC=mesh.nCz)\n",
    "    mapping = Maps.ExpMap(mesh) * Maps.SurjectVertical1D(mesh) * actMap\n",
    "    rx = EM.TDEM.Rx.Point_dbdt(\n",
    "        rxloc,\n",
    "        times,\n",
    "        'z'\n",
    "    )\n",
    "    src = EM.TDEM.Src.CircularLoop([rx], orientation='z', loc=srcloc)\n",
    "    survey = EM.TDEM.Survey([src])\n",
    "    prb = EM.TDEM.Problem3D_b(mesh, sigmaMap=mapping)\n",
    "\n",
    "    prb.Solver = Pardiso\n",
    "    prb.timeSteps = [(1e-05, 15), (5e-5, 10), (2e-4, 10)]\n",
    "    prb.pair(survey)\n",
    "    survey.dobs = dobs\n",
    "    survey.std = std\n",
    "    survey.eps = eps\n",
    "\n",
    "    dmisfit = DataMisfit.l2_DataMisfit(survey)\n",
    "    regMesh = Mesh.TensorMesh([mesh.hz[mapping.maps[-1].indActive]])\n",
    "    reg = Regularization.Tikhonov(\n",
    "        regMesh, alpha_s=1./mesh.hx.min()**2, alpha_x=1.\n",
    "    )\n",
    "    opt = Optimization.InexactGaussNewton(maxIter=10, LSshorten=0.5)\n",
    "    invProb = InvProblem.BaseInvProblem(dmisfit, reg, opt, beta=20)\n",
    "    # Create an inversion object\n",
    "#     beta = Directives.BetaSchedule(coolingFactor=2, coolingRate=2)\n",
    "#     betaest = Directives.BetaEstimate_ByEig(beta0_ratio=1e0)\n",
    "    target=Directives.TargetMisfit()\n",
    "    inv = Inversion.BaseInversion(invProb, directiveList=[target])\n",
    "    m0 = np.log(np.ones(actMap.nP)*sig0)\n",
    "    prb.counter = opt.counter = Utils.Counter()\n",
    "    mopt = inv.run(m0)\n",
    "    return mopt, invProb.dpred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the 1D inversions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from multiprocessing import Pool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SimPEG.InvProblem will set Regularization.mref to m0.\n",
      "SimPEG.InvProblem will set Regularization.mref to m0.\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",
      "    SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
      "    ***Done using same Solver and solverOpts as the problem***\n",
      "    SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
      "    ***Done using same Solver and solverOpts as the problem***\n",
      "SimPEG.InvProblem will set Regularization.mref to m0.\n",
      "\n",
      "\n",
      "\n",
      "    SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
      "    ***Done using same Solver and solverOpts as the problem***model has any nan: 0\n",
      "\n",
      "model has any nan: 0\n",
      "model has any nan: 0\n",
      "============================ Inexact Gauss Newton ============================\n",
      "============================ Inexact Gauss Newton ============================\n",
      "============================ Inexact Gauss Newton ============================\n",
      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
      "-----------------------------------------------------------------------------\n",
      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
      "-----------------------------------------------------------------------------\n",
      "model has any nan: 0\n",
      "x0 has any nan: 0\n",
      "-----------------------------------------------------------------------------\n",
      "x0 has any nan: 0\n",
      "============================ Inexact Gauss Newton ============================\n",
      "x0 has any nan: 0\n",
      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
      "-----------------------------------------------------------------------------\n",
      "x0 has any nan: 0\n",
      "   0  2.00e+01  1.87e+03  0.00e+00  1.87e+03    7.78e+02      0              \n",
      "   0  2.00e+01  1.04e+03  0.00e+00  1.04e+03    7.17e+02      0              \n",
      "   0  2.00e+01  2.84e+03  0.00e+00  2.84e+03    6.16e+02      0              \n",
      "   0  2.00e+01  6.58e+03  0.00e+00  6.58e+03    5.51e+03      0              \n",
      "   1  2.00e+01  2.41e+03  1.74e-01  2.41e+03    2.36e+03      0              \n",
      "   1  2.00e+01  2.39e+02  2.08e-01  2.43e+02    6.76e+02      1              \n",
      "   1  2.00e+01  4.29e+02  3.36e-02  4.30e+02    2.80e+02      1              \n",
      "   1  2.00e+01  3.71e+02  2.78e-01  3.77e+02    5.64e+02      2              \n",
      "   2  2.00e+01  1.27e+02  1.65e-01  1.30e+02    5.51e+01      0              \n",
      "   2  2.00e+01  1.92e+02  2.83e-01  1.98e+02    8.72e+02      0   Skip BFGS  \n",
      "   2  2.00e+01  2.16e+03  4.92e-01  2.17e+03    3.24e+03      1   Skip BFGS  \n",
      "   2  2.00e+01  3.09e+02  8.94e-02  3.11e+02    8.90e+01      2              \n",
      "   3  2.00e+01  7.09e+01  2.57e-01  7.60e+01    7.49e+01      0              \n",
      "   3  2.00e+01  1.44e+03  4.90e-01  1.45e+03    4.41e+02      0              \n",
      "   3  2.00e+01  1.01e+02  1.58e-01  1.04e+02    1.08e+02      2              \n",
      "   3  2.00e+01  3.02e+02  1.72e-01  3.06e+02    2.37e+02      0   Skip BFGS  \n",
      "   4  2.00e+01  5.94e+01  2.86e-01  6.51e+01    2.14e+02      1   Skip BFGS  \n",
      "   4  2.00e+01  1.31e+03  7.12e-01  1.32e+03    7.91e+02      3   Skip BFGS  \n",
      "   4  2.00e+01  9.78e+01  1.74e-01  1.01e+02    1.95e+02      2   Skip BFGS  \n",
      "   4  2.00e+01  2.96e+02  1.96e-01  3.00e+02    3.38e+02      3              \n",
      "   5  2.00e+01  5.10e+01  2.89e-01  5.68e+01    2.29e+02      1              \n",
      "   5  2.00e+01  1.12e+03  1.28e+00  1.15e+03    7.87e+02      2   Skip BFGS  \n",
      "   5  2.00e+01  9.61e+01  1.71e-01  9.96e+01    2.01e+02      3              \n",
      "   6  2.00e+01  4.76e+01  2.72e-01  5.30e+01    2.28e+02      0   Skip BFGS  \n",
      "   5  2.00e+01  2.93e+02  2.54e-01  2.98e+02    2.43e+02      0              \n",
      "   6  2.00e+01  9.69e+02  2.71e+00  1.02e+03    9.35e+02      1   Skip BFGS  \n",
      "   6  2.00e+01  9.43e+01  1.67e-01  9.77e+01    1.23e+02      0   Skip BFGS  \n",
      "   7  2.00e+01  8.76e+02  2.72e+00  9.30e+02    7.30e+01      0              \n",
      "   7  2.00e+01  4.55e+01  2.90e-01  5.13e+01    2.09e+02      2              \n",
      "   6  2.00e+01  2.86e+02  2.72e-01  2.92e+02    2.74e+02      3              \n",
      "   7  2.00e+01  9.26e+01  1.67e-01  9.60e+01    1.24e+02      1              \n",
      "   8  2.00e+01  8.30e+02  3.34e+00  8.97e+02    1.77e+02      3              \n",
      "   8  2.00e+01  4.10e+01  2.86e-01  4.68e+01    9.80e+01      0   Skip BFGS  \n",
      "   7  2.00e+01  2.85e+02  2.48e-01  2.90e+02    2.82e+02      4   Skip BFGS  \n",
      "   8  2.00e+01  9.23e+01  1.75e-01  9.58e+01    1.31e+02      3              \n",
      "   9  2.00e+01  4.07e+01  2.95e-01  4.66e+01    1.04e+02      1              \n",
      "   9  2.00e+01  7.89e+02  3.92e+00  8.68e+02    4.36e+02      3              \n",
      "   8  2.00e+01  2.83e+02  2.55e-01  2.89e+02    2.67e+02      2   Skip BFGS  \n",
      "   9  2.00e+01  9.21e+01  1.73e-01  9.56e+01    1.32e+02      3              \n",
      "  10  2.00e+01  4.07e+01  2.91e-01  4.65e+01    8.58e+01      0              \n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 9.0314e-02 <= tolF*(1+|f0|) = 2.8400e+02\n",
      "1 : |xc-x_last| = 3.2420e-01 <= tolX*(1+|x0|) = 2.7754e+00\n",
      "0 : |proj(x-g)-x|    = 8.5758e+01 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 8.5758e+01 <= 1e3*eps       = 1.0000e-02\n",
      "1 : maxIter   =      10    <= iter          =     10\n",
      "------------------------- DONE! -------------------------\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",
      "  10  2.00e+01  6.98e+02  6.40e+00  8.25e+02    5.14e+02      1              \n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 4.2141e+01 <= tolF*(1+|f0|) = 6.5822e+02\n",
      "1 : |xc-x_last| = 1.9078e+00 <= tolX*(1+|x0|) = 2.7754e+00\n",
      "0 : |proj(x-g)-x|    = 5.1433e+02 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 5.1433e+02 <= 1e3*eps       = 1.0000e-02\n",
      "1 : maxIter   =      10    <= iter          =     10\n",
      "------------------------- DONE! -------------------------\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  2.00e+01  3.21e+03  0.00e+00  3.21e+03    4.71e+02      0              \n",
      "   0  2.00e+01  8.64e+02  0.00e+00  8.64e+02    4.85e+02      0              \n",
      "   9  2.00e+01  2.82e+02  2.79e-01  2.88e+02    2.38e+02      1              \n",
      "   1  2.00e+01  1.80e+03  1.40e-01  1.80e+03    8.25e+02      3              \n",
      "  10  2.00e+01  9.20e+01  1.73e-01  9.54e+01    1.27e+02      3              \n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 1.9567e-01 <= tolF*(1+|f0|) = 1.8706e+02\n",
      "1 : |xc-x_last| = 9.6501e-02 <= tolX*(1+|x0|) = 2.7754e+00\n",
      "0 : |proj(x-g)-x|    = 1.2694e+02 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 1.2694e+02 <= 1e3*eps       = 1.0000e-02\n",
      "1 : maxIter   =      10    <= iter          =     10\n",
      "------------------------- DONE! -------------------------\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  2.00e+01  2.37e+03  0.00e+00  2.37e+03    7.27e+02      0              \n",
      "  10  2.00e+01  2.81e+02  2.38e-01  2.86e+02    2.07e+02      1   Skip BFGS  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 1.5012e+00 <= tolF*(1+|f0|) = 1.0371e+02\n",
      "1 : |xc-x_last| = 4.5179e-01 <= tolX*(1+|x0|) = 2.7754e+00\n",
      "0 : |proj(x-g)-x|    = 2.0707e+02 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 2.0707e+02 <= 1e3*eps       = 1.0000e-02\n",
      "1 : maxIter   =      10    <= iter          =     10\n",
      "------------------------- DONE! -------------------------\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",
      "   1  2.00e+01  6.15e+02  1.38e-01  6.18e+02    3.49e+02      2              \n",
      "   0  2.00e+01  1.43e+03  0.00e+00  1.43e+03    7.73e+02      0              \n",
      "   2  2.00e+01  4.05e+02  2.46e-01  4.10e+02    7.72e+02      1              \n",
      "   1  2.00e+01  7.44e+02  1.18e-01  7.46e+02    8.47e+02      2              \n",
      "   1  2.00e+01  2.75e+02  9.42e-02  2.77e+02    1.06e+02      1              \n",
      "   2  2.00e+01  5.57e+02  3.52e-01  5.64e+02    7.82e+02      2              \n",
      "   2  2.00e+01  4.79e+02  2.82e-01  4.84e+02    1.46e+03      0              \n",
      "   3  2.00e+01  2.90e+02  2.47e-01  2.95e+02    8.11e+02      2              \n",
      "   2  2.00e+01  2.27e+02  1.01e-01  2.29e+02    1.32e+02      2              \n",
      "   3  2.00e+01  4.56e+02  4.36e-01  4.64e+02    6.91e+02      1              \n",
      "   3  2.00e+01  1.04e+02  2.28e-01  1.09e+02    1.85e+02      0              \n",
      "   4  2.00e+01  2.15e+02  2.85e-01  2.21e+02    8.12e+02      2   Skip BFGS  \n",
      "   4  2.00e+01  4.10e+02  1.15e+00  4.33e+02    7.43e+02      2              \n",
      "   4  2.00e+01  9.19e+01  2.19e-01  9.63e+01    2.23e+02      2              \n",
      "   3  2.00e+01  2.22e+02  1.07e-01  2.24e+02    1.61e+02      3   Skip BFGS  \n",
      "   5  2.00e+01  1.55e+02  4.96e-01  1.65e+02    9.83e+02      1   Skip BFGS  \n",
      "   6  2.00e+01  8.97e+01  4.90e-01  9.95e+01    6.57e+01      0              \n",
      "   5  2.00e+01  3.88e+02  1.98e+00  4.27e+02    7.00e+02      1   Skip BFGS  \n",
      "   5  2.00e+01  8.54e+01  2.62e-01  9.06e+01    2.44e+02      2   Skip BFGS  \n",
      "   4  2.00e+01  2.19e+02  1.13e-01  2.21e+02    1.50e+02      2              \n",
      "   7  2.00e+01  8.52e+01  6.85e-01  9.89e+01    1.68e+02      1   Skip BFGS  \n",
      "   6  2.00e+01  3.61e+02  1.98e+00  4.01e+02    5.82e+02      2              \n",
      "   6  2.00e+01  8.42e+01  2.66e-01  8.95e+01    2.43e+02      3              \n",
      "   5  2.00e+01  2.18e+02  1.17e-01  2.20e+02    1.71e+02      3              \n",
      "   8  2.00e+01  8.43e+01  6.31e-01  9.69e+01    2.59e+02      2              \n",
      "   7  2.00e+01  3.50e+02  1.12e+00  3.73e+02    3.72e+02      1   Skip BFGS  \n",
      "   7  2.00e+01  7.67e+01  4.08e-01  8.49e+01    2.10e+02      1              \n",
      "   6  2.00e+01  2.17e+02  1.39e-01  2.20e+02    1.88e+02      3              \n",
      "   9  2.00e+01  8.29e+01  6.36e-01  9.56e+01    2.50e+02      3              \n",
      "   8  2.00e+01  3.37e+02  1.45e+00  3.66e+02    3.37e+02      2              \n",
      "   8  2.00e+01  7.39e+01  4.32e-01  8.25e+01    1.90e+02      2              \n",
      "   7  2.00e+01  2.16e+02  1.28e-01  2.19e+02    1.48e+02      1              \n",
      "  10  2.00e+01  7.38e+01  8.40e-01  9.06e+01    2.35e+02      1              \n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 5.0216e+00 <= tolF*(1+|f0|) = 3.2073e+02\n",
      "1 : |xc-x_last| = 1.1347e+00 <= tolX*(1+|x0|) = 2.7754e+00\n",
      "0 : |proj(x-g)-x|    = 2.3519e+02 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 2.3519e+02 <= 1e3*eps       = 1.0000e-02\n",
      "1 : maxIter   =      10    <= iter          =     10\n",
      "------------------------- DONE! -------------------------\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",
      "   9  2.00e+01  3.09e+02  2.38e+00  3.57e+02    4.01e+02      1              \n",
      "   0  2.00e+01  3.23e+03  0.00e+00  3.23e+03    3.20e+02      0              \n",
      "   9  2.00e+01  7.34e+01  3.93e-01  8.12e+01    1.32e+02      0              \n",
      "   8  2.00e+01  2.16e+02  1.37e-01  2.18e+02    1.64e+02      1              \n",
      "  10  2.00e+01  3.07e+02  1.84e+00  3.44e+02    3.26e+02      1              \n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 1.2639e+01 <= tolF*(1+|f0|) = 8.6478e+01\n",
      "1 : |xc-x_last| = 1.2733e+00 <= tolX*(1+|x0|) = 2.7754e+00\n",
      "0 : |proj(x-g)-x|    = 3.2562e+02 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 3.2562e+02 <= 1e3*eps       = 1.0000e-02\n",
      "1 : maxIter   =      10    <= iter          =     10\n",
      "------------------------- DONE! -------------------------\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  2.00e+01  3.20e+03  0.00e+00  3.20e+03    4.75e+02      0              \n",
      "   1  2.00e+01  3.09e+03  2.99e-01  3.09e+03    3.49e+03      3              \n",
      "  10  2.00e+01  7.05e+01  4.08e-01  7.87e+01    1.21e+02      2              \n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 2.5726e+00 <= tolF*(1+|f0|) = 2.3747e+02\n",
      "1 : |xc-x_last| = 4.9046e-01 <= tolX*(1+|x0|) = 2.7754e+00\n",
      "0 : |proj(x-g)-x|    = 1.2091e+02 <= tolG          = 1.0000e-01\n",
      "   9  2.00e+01  2.15e+02  1.52e-01  2.18e+02    1.23e+02      0              \n",
      "0 : |proj(x-g)-x|    = 1.2091e+02 <= 1e3*eps       = 1.0000e-02\n",
      "1 : maxIter   =      10    <= iter          =     10\n",
      "------------------------- DONE! -------------------------\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  2.00e+01  2.36e+03  0.00e+00  2.36e+03    7.21e+02      0              \n",
      "   1  2.00e+01  1.79e+03  1.41e-01  1.79e+03    8.23e+02      3              \n",
      "   2  2.00e+01  2.83e+03  3.02e-01  2.83e+03    4.06e+03      3              \n",
      "   1  2.00e+01  8.13e+02  1.11e-01  8.16e+02    8.34e+02      2              \n",
      "  10  2.00e+01  2.14e+02  1.70e-01  2.18e+02    1.49e+02      2              \n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 7.2271e-01 <= tolF*(1+|f0|) = 1.4276e+02\n",
      "1 : |xc-x_last| = 2.2174e-01 <= tolX*(1+|x0|) = 2.7754e+00\n",
      "0 : |proj(x-g)-x|    = 1.4935e+02 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 1.4935e+02 <= 1e3*eps       = 1.0000e-02\n",
      "1 : maxIter   =      10    <= iter          =     10\n",
      "------------------------- DONE! -------------------------\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  2.00e+01  1.43e+03  0.00e+00  1.43e+03    7.82e+02      0              \n",
      "   2  2.00e+01  3.81e+02  2.49e-01  3.86e+02    7.08e+02      1              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   2  2.00e+01  6.86e+02  2.92e-01  6.91e+02    1.82e+03      0              \n",
      "   1  2.00e+01  1.56e+02  1.04e-01  1.58e+02    7.50e+01      1              \n",
      "   3  2.00e+01  2.81e+03  3.12e-01  2.82e+03    4.05e+03      7              \n",
      "   3  2.00e+01  1.61e+02  2.32e-01  1.66e+02    2.26e+02      0              \n",
      "   3  2.00e+01  2.74e+02  2.49e-01  2.79e+02    7.71e+02      2              \n",
      "   2  2.00e+01  1.52e+02  1.45e-01  1.54e+02    4.88e+02      1              \n",
      "   4  2.00e+01  2.79e+03  3.58e-01  2.80e+03    3.94e+03      5   Skip BFGS  \n",
      "   4  2.00e+01  2.03e+02  2.85e-01  2.09e+02    8.01e+02      2   Skip BFGS  \n",
      "   4  2.00e+01  1.48e+02  2.32e-01  1.53e+02    3.36e+02      2              \n",
      "   3  2.00e+01  1.14e+02  1.11e-01  1.16e+02    1.25e+02      0              \n",
      "   5  2.00e+01  1.46e+02  4.90e-01  1.56e+02    9.84e+02      1   Skip BFGS  \n",
      "   5  2.00e+01  2.78e+03  3.51e-01  2.79e+03    3.99e+03      7              \n",
      "   6  2.00e+01  7.95e+01  4.85e-01  8.92e+01    6.63e+01      0              \n",
      "   5  2.00e+01  1.42e+02  2.52e-01  1.47e+02    3.36e+02      3   Skip BFGS  \n",
      "   4  2.00e+01  1.12e+02  1.15e-01  1.14e+02    1.39e+02      1              \n",
      "   7  2.00e+01  7.53e+01  5.30e-01  8.59e+01    9.21e+01      2   Skip BFGS  \n",
      "   6  2.00e+01  1.34e+02  3.38e-01  1.41e+02    3.07e+02      2              \n",
      "   6  2.00e+01  2.75e+03  3.50e-01  2.75e+03    3.85e+03      5   Skip BFGS  \n",
      "   5  2.00e+01  1.11e+02  1.30e-01  1.13e+02    1.49e+02      3   Skip BFGS  \n",
      "   8  2.00e+01  7.33e+01  5.44e-01  8.42e+01    1.07e+02      3              \n",
      "   7  2.00e+01  1.31e+02  3.35e-01  1.38e+02    3.05e+02      3              \n",
      "   7  2.00e+01  2.74e+03  3.50e-01  2.75e+03    3.82e+03      7              \n",
      "   6  2.00e+01  1.11e+02  1.34e-01  1.13e+02    1.51e+02      3              \n",
      "   9  2.00e+01  7.18e+01  5.81e-01  8.34e+01    8.89e+01      2              \n",
      "   8  2.00e+01  1.25e+02  4.04e-01  1.33e+02    1.66e+02      0              \n",
      "   7  2.00e+01  1.10e+02  1.38e-01  1.13e+02    1.58e+02      4              \n",
      "   8  2.00e+01  2.73e+03  3.48e-01  2.74e+03    3.83e+03      6              \n",
      "  10  2.00e+01  7.00e+01  6.44e-01  8.29e+01    1.04e+02      2              \n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 4.6364e-01 <= tolF*(1+|f0|) = 3.2019e+02\n",
      "1 : |xc-x_last| = 5.7625e-01 <= tolX*(1+|x0|) = 2.7754e+00\n",
      "0 : |proj(x-g)-x|    = 1.0449e+02 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 1.0449e+02 <= 1e3*eps       = 1.0000e-02\n",
      "1 : maxIter   =      10    <= iter          =     10\n",
      "------------------------- DONE! -------------------------\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",
      "   9  2.00e+01  1.19e+02  5.33e-01  1.29e+02    1.71e+02      2              \n",
      "   0  2.00e+01  2.83e+03  0.00e+00  2.83e+03    6.22e+02      0              \n",
      "   8  2.00e+01  1.10e+02  1.49e-01  1.13e+02    1.57e+02      3              \n",
      "   9  2.00e+01  2.72e+03  3.50e-01  2.73e+03    3.75e+03      6              \n",
      "   1  2.00e+01  4.22e+02  2.62e-01  4.28e+02    6.41e+02      2              \n",
      "  10  2.00e+01  1.15e+02  4.64e-01  1.24e+02    7.47e+01      0              \n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 5.3439e+00 <= tolF*(1+|f0|) = 2.3629e+02\n",
      "1 : |xc-x_last| = 1.1690e+00 <= tolX*(1+|x0|) = 2.7754e+00\n",
      "0 : |proj(x-g)-x|    = 7.4716e+01 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 7.4716e+01 <= 1e3*eps       = 1.0000e-02\n",
      "1 : maxIter   =      10    <= iter          =     10\n",
      "------------------------- DONE! -------------------------\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  2.00e+01  1.88e+03  0.00e+00  1.88e+03    8.01e+02      0              \n",
      "   9  2.00e+01  1.10e+02  1.50e-01  1.13e+02    1.48e+02      2              \n",
      "   2  2.00e+01  2.06e+02  2.83e-01  2.11e+02    8.73e+02      0              \n",
      "   1  2.00e+01  2.76e+02  2.10e-01  2.80e+02    7.72e+02      1              \n",
      "  10  2.00e+01  2.72e+03  3.54e-01  2.72e+03    3.74e+03      7              \n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 7.0289e+00 <= tolF*(1+|f0|) = 3.2309e+02\n",
      "1 : |xc-x_last| = 1.1303e-01 <= tolX*(1+|x0|) = 2.7754e+00\n",
      "0 : |proj(x-g)-x|    = 3.7446e+03 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 3.7446e+03 <= 1e3*eps       = 1.0000e-02\n",
      "1 : maxIter   =      10    <= iter          =     10\n",
      "------------------------- DONE! -------------------------\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  2.00e+01  3.21e+03  0.00e+00  3.21e+03    3.05e+02      0              \n",
      "   3  2.00e+01  7.93e+01  2.53e-01  8.44e+01    6.90e+01      0              \n",
      "   2  2.00e+01  1.31e+02  1.61e-01  1.34e+02    5.54e+01      0              \n",
      "  10  2.00e+01  1.09e+02  1.57e-01  1.12e+02    1.40e+02      2              \n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 3.8098e-01 <= tolF*(1+|f0|) = 1.4273e+02\n",
      "1 : |xc-x_last| = 2.5755e-01 <= tolX*(1+|x0|) = 2.7754e+00\n",
      "0 : |proj(x-g)-x|    = 1.4022e+02 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 1.4022e+02 <= 1e3*eps       = 1.0000e-02\n",
      "1 : maxIter   =      10    <= iter          =     10\n",
      "------------------------- DONE! -------------------------\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  2.00e+01  1.00e+03  0.00e+00  1.00e+03    6.74e+02      0              \n",
      "   4  2.00e+01  7.09e+01  2.27e-01  7.55e+01    2.40e+02      1   Skip BFGS  \n",
      "   1  2.00e+01  2.65e+03  7.73e-02  2.65e+03    5.26e+02      4              \n",
      "   1  2.00e+01  3.81e+02  3.48e-02  3.82e+02    2.47e+02      1              \n",
      "   3  2.00e+01  1.05e+02  1.57e-01  1.08e+02    1.18e+02      2              \n",
      "   5  2.00e+01  6.94e+01  2.23e-01  7.39e+01    3.43e+02      0              \n",
      "   2  2.00e+01  2.44e+03  4.09e-01  2.45e+03    2.86e+03      3              \n",
      "   4  2.00e+01  9.88e+01  1.53e-01  1.02e+02    1.62e+02      2   Skip BFGS  \n",
      "   2  2.00e+01  2.70e+02  8.26e-02  2.72e+02    9.93e+01      2              \n",
      "   6  2.00e+01  5.38e+01  2.12e-01  5.80e+01    2.80e+01      0              \n",
      "   3  2.00e+01  2.44e+03  3.40e-01  2.45e+03    4.20e+03      3              \n",
      "   5  2.00e+01  9.77e+01  1.57e-01  1.01e+02    1.62e+02      3   Skip BFGS  \n",
      "   3  2.00e+01  2.57e+02  1.51e-01  2.60e+02    1.41e+02      3   Skip BFGS  \n",
      "   7  2.00e+01  5.28e+01  2.20e-01  5.72e+01    3.85e+01      0              \n",
      "   4  2.00e+01  2.24e+03  3.80e-01  2.25e+03    5.35e+03      2   Skip BFGS  \n",
      "   6  2.00e+01  9.68e+01  1.78e-01  1.00e+02    1.66e+02      2              \n",
      "   8  2.00e+01  5.19e+01  2.24e-01  5.64e+01    7.27e+01      0              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   5  2.00e+01  1.57e+03  5.46e-01  1.58e+03    4.08e+03      1   Skip BFGS  \n",
      "   4  2.00e+01  2.52e+02  1.55e-01  2.55e+02    1.98e+02      3              \n",
      "   6  2.00e+01  9.71e+02  9.02e-01  9.89e+02    6.17e+02      0   Skip BFGS  \n",
      "   9  2.00e+01  5.09e+01  2.26e-01  5.55e+01    2.08e+01      0              \n",
      "   7  2.00e+01  9.59e+01  1.74e-01  9.94e+01    1.55e+02      2              \n",
      "   5  2.00e+01  2.49e+02  1.72e-01  2.53e+02    2.14e+02      4              \n",
      "   7  2.00e+01  9.03e+02  1.11e+00  9.26e+02    8.70e+02      4              \n",
      "  10  2.00e+01  5.02e+01  2.43e-01  5.51e+01    3.68e+01      1              \n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 4.0479e-01 <= tolF*(1+|f0|) = 2.8295e+02\n",
      "1 : |xc-x_last| = 3.5702e-01 <= tolX*(1+|x0|) = 2.7754e+00\n",
      "0 : |proj(x-g)-x|    = 3.6811e+01 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 3.6811e+01 <= 1e3*eps       = 1.0000e-02\n",
      "1 : maxIter   =      10    <= iter          =     10\n",
      "------------------------- DONE! -------------------------\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",
      "   8  2.00e+01  9.49e+01  1.96e-01  9.89e+01    1.34e+02      1              \n",
      "   0  2.00e+01  9.94e+02  0.00e+00  9.94e+02    4.62e+02      0              \n",
      "   6  2.00e+01  2.44e+02  1.80e-01  2.48e+02    1.88e+02      1              \n",
      "   8  2.00e+01  8.79e+02  1.28e+00  9.04e+02    9.10e+02      3              \n",
      "   9  2.00e+01  9.53e+01  1.76e-01  9.88e+01    1.32e+02      2              \n",
      "   1  2.00e+01  8.12e+02  1.67e-01  8.15e+02    7.02e+02      2              \n",
      "   7  2.00e+01  2.42e+02  2.08e-01  2.47e+02    2.21e+02      3              \n",
      "  10  2.00e+01  9.50e+01  1.87e-01  9.87e+01    1.28e+02      2              \n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 1.1329e-01 <= tolF*(1+|f0|) = 1.8859e+02\n",
      "1 : |xc-x_last| = 1.4394e-01 <= tolX*(1+|x0|) = 2.7754e+00\n",
      "0 : |proj(x-g)-x|    = 1.2828e+02 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 1.2828e+02 <= 1e3*eps       = 1.0000e-02\n",
      "1 : maxIter   =      10    <= iter          =     10\n",
      "------------------------- DONE! -------------------------\n",
      "   9  2.00e+01  8.57e+02  1.54e+00  8.88e+02    1.18e+03      4              \n",
      "   8  2.00e+01  2.37e+02  3.99e-01  2.45e+02    2.46e+02      1   Skip BFGS  \n",
      "   2  2.00e+01  7.25e+02  2.67e-01  7.30e+02    7.83e+02      3              \n",
      "  10  2.00e+01  8.54e+02  1.52e+00  8.85e+02    1.26e+03      5              \n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 3.4905e+00 <= tolF*(1+|f0|) = 3.2117e+02\n",
      "1 : |xc-x_last| = 4.5112e-01 <= tolX*(1+|x0|) = 2.7754e+00\n",
      "0 : |proj(x-g)-x|    = 1.2582e+03 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 1.2582e+03 <= 1e3*eps       = 1.0000e-02\n",
      "1 : maxIter   =      10    <= iter          =     10\n",
      "------------------------- DONE! -------------------------\n",
      "   9  2.00e+01  2.38e+02  3.30e-01  2.44e+02    2.28e+02      2              \n",
      "   3  2.00e+01  6.43e+02  9.12e-01  6.61e+02    1.12e+03      2   Skip BFGS  \n",
      "   4  2.00e+01  5.48e+02  8.51e-01  5.65e+02    8.67e+02      1              \n",
      "  10  2.00e+01  2.37e+02  3.71e-01  2.44e+02    2.38e+02      3              \n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 1.8687e-01 <= tolF*(1+|f0|) = 1.0011e+02\n",
      "1 : |xc-x_last| = 2.4558e-01 <= tolX*(1+|x0|) = 2.7754e+00\n",
      "0 : |proj(x-g)-x|    = 2.3783e+02 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 2.3783e+02 <= 1e3*eps       = 1.0000e-02\n",
      "1 : maxIter   =      10    <= iter          =     10\n",
      "------------------------- DONE! -------------------------\n",
      "   5  2.00e+01  5.34e+02  1.09e+00  5.56e+02    9.26e+02      3   Skip BFGS  \n",
      "   6  2.00e+01  5.22e+02  1.45e+00  5.51e+02    9.70e+02      3              \n",
      "   7  2.00e+01  4.27e+02  1.92e+00  4.65e+02    4.04e+02      0   Skip BFGS  \n",
      "   8  2.00e+01  4.12e+02  2.05e+00  4.53e+02    4.43e+02      3              \n",
      "   9  2.00e+01  4.08e+02  2.07e+00  4.49e+02    4.50e+02      5   Skip BFGS  \n",
      "  10  2.00e+01  3.96e+02  2.00e+00  4.36e+02    3.23e+02      1              \n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 1.3757e+01 <= tolF*(1+|f0|) = 9.9464e+01\n",
      "1 : |xc-x_last| = 8.4051e-01 <= tolX*(1+|x0|) = 2.7754e+00\n",
      "0 : |proj(x-g)-x|    = 3.2325e+02 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 3.2325e+02 <= 1e3*eps       = 1.0000e-02\n",
      "1 : maxIter   =      10    <= iter          =     10\n",
      "------------------------- DONE! -------------------------\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  2.00e+01  4.35e+03  0.00e+00  4.35e+03    3.52e+03      0              \n",
      "   1  2.00e+01  2.90e+03  1.66e-01  2.90e+03    3.32e+03      1              \n",
      "   2  2.00e+01  1.47e+03  2.62e-01  1.48e+03    9.59e+02      0              \n",
      "   3  2.00e+01  1.32e+03  4.41e-01  1.33e+03    1.23e+03      2   Skip BFGS  \n",
      "   4  2.00e+01  1.23e+03  6.87e-01  1.24e+03    1.38e+03      2   Skip BFGS  \n",
      "   5  2.00e+01  1.14e+03  1.01e+00  1.16e+03    1.43e+03      2   Skip BFGS  \n",
      "   6  2.00e+01  9.79e+02  1.04e+00  1.00e+03    1.04e+02      0   Skip BFGS  \n",
      "   7  2.00e+01  9.25e+02  1.53e+00  9.56e+02    5.00e+02      2   Skip BFGS  \n",
      "   8  2.00e+01  9.04e+02  1.83e+00  9.40e+02    6.26e+02      2   Skip BFGS  \n",
      "   9  2.00e+01  8.73e+02  1.82e+00  9.09e+02    4.94e+01      0   Skip BFGS  \n",
      "  10  2.00e+01  8.49e+02  2.19e+00  8.93e+02    1.94e+02      2   Skip BFGS  \n",
      "------------------------- STOP! -------------------------\n",
      "1 : |fc-fOld| = 1.6944e+01 <= tolF*(1+|f0|) = 4.3471e+02\n",
      "1 : |xc-x_last| = 6.8639e-01 <= tolX*(1+|x0|) = 2.7754e+00\n",
      "0 : |proj(x-g)-x|    = 1.9445e+02 <= tolG          = 1.0000e-01\n",
      "0 : |proj(x-g)-x|    = 1.9445e+02 <= 1e3*eps       = 1.0000e-02\n",
      "1 : maxIter   =      10    <= iter          =     10\n",
      "------------------------- DONE! -------------------------\n"
     ]
    }
   ],
   "source": [
    "p = Pool()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sig0 = 1e-3\n",
    "std = 0.05\n",
    "eps = 0.\n",
    "rxloc = np.array([[0., 0., 30.]])\n",
    "srcloc = np.array([[0., 0., 30.]])\n",
    "results = p.map(run_1d_tem_inversion, [(sig0, DOBS[iSrc, 0, :], std, eps, rxloc, srcloc, times) for iSrc in range(len(x))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicted data and resultant model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "DPRED = [results[i][1] for i in range (nSrc)]\n",
    "MOPT = [results[i][0] for i in range (nSrc)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = np.vstack(DPRED)\n",
    "sigma = Utils.mkvc(np.exp(np.vstack(MOPT)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "global misfit 14534, target misfit 378\n",
      "chifact: 38.4\n"
     ]
    }
   ],
   "source": [
    "dobs = DOBS[:, 0, :].flatten()\n",
    "dpred = pred.flatten()\n",
    "W = Utils.sdiag(1./(dobs*std + eps))\n",
    "misfit = W*(dobs-dpred)\n",
    "misfit = misfit.dot(misfit)\n",
    "\n",
    "print('global misfit {:1.0f}, target misfit {:1.0f}'.format(misfit, len(dobs)))\n",
    "print('chifact: {:1.1f}'.format(misfit/len(dobs)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Voltage (V/Am$^2$)')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd14b43048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig = plt.figure(figsize=(5, 3))\n",
    "fig = plt.figure(figsize=(9, 4))\n",
    "for i in range(times.size):\n",
    "    plt.plot(x, -DOBS[:, 0, i].flatten(), 'k-', lw=1, label='obs' if i == 0 else None)\n",
    "    plt.plot(x, -pred[:,i].flatten(), 'bx', markeredgewidth=1.5, label='pred' if i == 0 else None)    \n",
    "plt.yscale('log')   \n",
    "plt.grid(which=\"both\")\n",
    "plt.legend(bbox_to_anchor=[1, 1])\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('Voltage (V/Am$^2$)')\n",
    "# plt.ylim(1e-15, 1e-9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh1d = get_1d_cyl_mesh()\n",
    "hx = np.ones(nSrc) * 50\n",
    "mesh2D = Mesh.TensorMesh([hx, mesh1d.hz[mesh1d.vectorCCz<0.]], x0=\"CN\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rect2D(p1, p2):    \n",
    "    xy = np.c_[np.r_[p1[0], p2[0], p2[0], p1[0], p1[0]], np.r_[p1[1], p1[1], p2[1], p2[1], p1[1]]]\n",
    "    return xy\n",
    "\n",
    "p1 = np.r_[-50, -450]\n",
    "p2 = np.r_[50, -50]\n",
    "xy_box = rect2D(p1, p2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_base.py:1398: UserWarning: aspect is not supported for Axes with xscale=linear, yscale=log\n",
      "  'yscale=%s' % (xscale, yscale))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd15cbbc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import LogNorm\n",
    "\n",
    "fig, ax = plt.subplots(2, 1, figsize=(8, 3.5*2))\n",
    "\n",
    "out = mesh2D.plotImage(sigma, pcolorOpts={'norm':LogNorm()}, clim=[1e-3, 1e-1], ax=ax[0])\n",
    "cb = plt.colorbar(out[0], fraction=0.025, extend=\"min\", ax=ax[0])\n",
    "ax[0].set_ylim(-500, 0.)\n",
    "plt.gca().set_aspect(1)\n",
    "cb.set_label('Conductivity (S/m)')\n",
    "ax[0].set_title(\"(a)\")\n",
    "\n",
    "ax[0].plot(xy_box[:,0], xy_box[:,1], 'w--')\n",
    "\n",
    "for i in range(times.size):\n",
    "    ax[1].plot(x, -DOBS[:, 0, i].flatten(), 'k-', lw=1, label='obs' if i == 0 else None)\n",
    "    ax[1].plot(x, -pred[:,i].flatten(), 'bx', markeredgewidth=1.5, label='pred' if i == 0 else None)    \n",
    "ax[1].set_yscale('log')   \n",
    "ax[1].grid(which=\"both\")\n",
    "ax[1].legend(bbox_to_anchor=[1, 1])\n",
    "ax[1].set_xlabel('x')\n",
    "ax[1].set_ylabel('Voltage (V/Am$^2$)')\n",
    "ax[1].set_title(\"(b)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "fig.savefig('../figures/1Dinversion.png', dpi=350, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/lindseyjh/git/simpeg-research/heagy-2018-aem/notebooks/1Dinversion/mesh_1d_stitched.json'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.save('1Dinversion/dpred_1d_stitched', pred)\n",
    "np.save('1Dinversion/sigma_1d_stitched', sigma)\n",
    "mesh2D.save(filename=\"1Dinversion/mesh_1d_stitched.json\")"
   ]
  },
  {
   "cell_type": "code",
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