mirror of
https://github.com/wassname/simpeg.git
synced 2026-10-02 12:40:56 +08:00
Fix depenencies in notebooks and got tests to work.
This commit is contained in:
GudniRos committed
1 parent
c78d5beef8
commit
2b37f27602
10 files changed
+750
-397
No files matched your search
@@ -155,7 +155,7 @@
|
||||
"else:\n",
|
||||
" reg = simpeg.Regularization.Tikhonov(m1d,mapping=mappingExpAct)\n",
|
||||
"reg.smoothModel = True\n",
|
||||
"reg.alpha_s = 1e-9\n",
|
||||
"reg.alpha_s = 1e-7\n",
|
||||
"reg.alpha_x = 1.\n",
|
||||
"# reg.alpha_xx = 0.001\n",
|
||||
"# Inversion problem\n",
|
||||
@@ -175,7 +175,7 @@
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"scrolled": true
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -190,42 +190,42 @@
|
||||
"============================ Inexact Gauss Newton ============================\n",
|
||||
" # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n",
|
||||
"-----------------------------------------------------------------------------\n",
|
||||
" 0 2.08e+05 2.18e+05 0.00e+00 2.18e+05 4.01e+04 0 \n",
|
||||
" 1 2.08e+05 2.52e+04 2.37e-04 2.52e+04 6.20e+03 0 \n",
|
||||
" 2 2.08e+05 3.46e+03 3.27e-04 3.53e+03 1.03e+03 0 Skip BFGS \n",
|
||||
" 3 2.60e+04 1.72e+03 2.64e-04 1.73e+03 2.61e+02 0 Skip BFGS \n",
|
||||
" 4 2.60e+04 1.03e+03 1.19e-02 1.34e+03 2.44e+02 0 Skip BFGS \n",
|
||||
" 5 2.60e+04 6.85e+02 1.56e-02 1.09e+03 1.35e+02 0 \n",
|
||||
" 6 3.26e+03 5.82e+02 1.78e-02 6.40e+02 1.19e+02 0 \n",
|
||||
" 7 3.26e+03 3.67e+02 4.78e-02 5.22e+02 1.40e+02 0 \n",
|
||||
" 8 3.26e+03 2.60e+02 5.72e-02 4.46e+02 2.06e+02 0 \n",
|
||||
" 9 4.07e+02 2.10e+02 6.04e-02 2.34e+02 6.61e+01 0 \n",
|
||||
" 10 4.07e+02 1.76e+02 9.60e-02 2.15e+02 1.65e+02 0 \n",
|
||||
" 11 4.07e+02 1.56e+02 1.22e-01 2.05e+02 9.68e+01 0 \n",
|
||||
" 12 5.09e+01 1.29e+02 1.28e-01 1.35e+02 5.89e+01 0 \n",
|
||||
" 13 5.09e+01 1.24e+02 1.85e-01 1.34e+02 7.24e+01 0 \n",
|
||||
" 14 5.09e+01 1.19e+02 2.56e-01 1.32e+02 7.95e+01 0 \n",
|
||||
" 15 6.36e+00 1.10e+02 2.10e-01 1.12e+02 5.28e+01 0 \n",
|
||||
" 16 6.36e+00 7.67e+01 3.60e-01 7.89e+01 5.36e+01 0 Skip BFGS \n",
|
||||
" 17 6.36e+00 7.19e+01 3.11e-01 7.39e+01 3.66e+01 0 \n",
|
||||
" 18 7.95e-01 6.99e+01 3.06e-01 7.02e+01 3.89e+01 0 Skip BFGS \n",
|
||||
" 19 7.95e-01 6.92e+01 3.20e-01 6.94e+01 3.72e+01 0 \n",
|
||||
" 20 7.95e-01 6.21e+01 3.14e-01 6.24e+01 8.88e+01 2 Skip BFGS \n",
|
||||
" 21 9.94e-02 5.69e+01 2.69e-01 5.69e+01 3.19e+01 0 \n",
|
||||
" 22 9.94e-02 5.62e+01 2.83e-01 5.63e+01 2.62e+01 0 \n",
|
||||
" 23 9.94e-02 5.46e+01 2.44e-01 5.47e+01 4.84e+01 1 \n",
|
||||
" 24 1.24e-02 5.41e+01 2.08e-01 5.41e+01 4.43e+01 1 Skip BFGS \n",
|
||||
" 25 1.24e-02 5.36e+01 2.09e-01 5.36e+01 4.26e+01 1 \n",
|
||||
" 26 1.24e-02 5.30e+01 1.99e-01 5.30e+01 2.34e+01 0 Skip BFGS \n",
|
||||
" 27 1.55e-03 5.29e+01 1.76e-01 5.29e+01 3.42e+01 0 \n",
|
||||
" 28 1.55e-03 5.28e+01 1.81e-01 5.28e+01 3.56e+01 0 \n",
|
||||
" 29 1.55e-03 5.28e+01 1.75e-01 5.28e+01 3.75e+01 1 \n",
|
||||
" 30 1.94e-04 4.75e+01 1.49e-01 4.75e+01 8.32e+01 1 \n",
|
||||
" 0 2.55e+05 2.18e+05 0.00e+00 2.18e+05 4.01e+04 0 \n",
|
||||
" 1 2.55e+05 2.50e+04 2.21e-03 2.55e+04 5.64e+03 0 \n",
|
||||
" 2 2.55e+05 3.37e+03 4.74e-03 4.58e+03 9.91e+02 0 Skip BFGS \n",
|
||||
" 3 3.19e+04 1.78e+03 4.90e-03 1.93e+03 2.83e+02 0 Skip BFGS \n",
|
||||
" 4 3.19e+04 9.92e+02 1.67e-02 1.52e+03 2.15e+02 0 Skip BFGS \n",
|
||||
" 5 3.19e+04 7.50e+02 1.83e-02 1.33e+03 1.06e+02 0 \n",
|
||||
" 6 3.99e+03 6.23e+02 2.13e-02 7.08e+02 1.40e+02 0 Skip BFGS \n",
|
||||
" 7 3.99e+03 3.26e+02 5.92e-02 5.62e+02 2.61e+02 0 \n",
|
||||
" 8 3.99e+03 3.58e+02 3.99e-02 5.17e+02 1.18e+02 0 \n",
|
||||
" 9 4.98e+02 3.33e+02 4.07e-02 3.53e+02 1.14e+02 1 \n",
|
||||
" 10 4.98e+02 2.51e+02 1.43e-01 3.22e+02 3.78e+02 0 \n",
|
||||
" 11 4.98e+02 1.75e+02 1.17e-01 2.34e+02 2.27e+02 1 \n",
|
||||
" 12 6.23e+01 7.99e+01 1.45e-01 8.89e+01 5.71e+01 0 Skip BFGS \n",
|
||||
" 13 6.23e+01 7.36e+01 1.92e-01 8.56e+01 1.16e+02 0 Skip BFGS \n",
|
||||
" 14 6.23e+01 6.72e+01 1.99e-01 7.96e+01 7.08e+01 0 \n",
|
||||
" 15 7.79e+00 6.04e+01 2.08e-01 6.21e+01 2.97e+01 0 \n",
|
||||
" 16 7.79e+00 5.60e+01 2.35e-01 5.78e+01 4.30e+01 0 \n",
|
||||
" 17 7.79e+00 4.85e+01 3.72e-01 5.14e+01 3.44e+01 0 \n",
|
||||
" 18 9.73e-01 4.34e+01 3.81e-01 4.38e+01 7.57e+00 0 \n",
|
||||
" 19 9.73e-01 4.30e+01 3.60e-01 4.33e+01 1.87e+01 0 Skip BFGS \n",
|
||||
" 20 9.73e-01 4.20e+01 3.74e-01 4.24e+01 1.23e+01 0 \n",
|
||||
" 21 1.22e-01 4.03e+01 4.12e-01 4.04e+01 1.79e+01 2 \n",
|
||||
" 22 1.22e-01 4.00e+01 4.83e-01 4.01e+01 2.70e+01 0 Skip BFGS \n",
|
||||
" 23 1.22e-01 3.92e+01 4.79e-01 3.92e+01 2.06e+01 0 \n",
|
||||
" 24 1.52e-02 3.86e+01 5.04e-01 3.86e+01 1.97e+01 2 Skip BFGS \n",
|
||||
" 25 1.52e-02 3.82e+01 5.02e-01 3.82e+01 3.32e+01 0 \n",
|
||||
" 26 1.52e-02 3.80e+01 4.74e-01 3.80e+01 2.44e+01 0 \n",
|
||||
" 27 1.90e-03 3.76e+01 4.57e-01 3.76e+01 3.62e+01 1 Skip BFGS \n",
|
||||
" 28 1.90e-03 3.74e+01 4.77e-01 3.74e+01 3.13e+01 1 \n",
|
||||
" 29 1.90e-03 3.74e+01 4.55e-01 3.74e+01 2.68e+01 0 \n",
|
||||
" 30 2.38e-04 3.71e+01 4.30e-01 3.71e+01 2.24e+01 1 \n",
|
||||
"------------------------- STOP! -------------------------\n",
|
||||
"1 : |fc-fOld| = 5.2783e+00 <= tolF*(1+|f0|) = 2.1833e+04\n",
|
||||
"0 : |xc-x_last| = 7.4766e+00 <= tolX*(1+|x0|) = 5.1104e+00\n",
|
||||
"0 : |proj(x-g)-x| = 8.3205e+01 <= tolG = 1.0000e-01\n",
|
||||
"0 : |proj(x-g)-x| = 8.3205e+01 <= 1e3*eps = 1.0000e-02\n",
|
||||
"1 : |fc-fOld| = 3.3322e-01 <= tolF*(1+|f0|) = 2.1833e+04\n",
|
||||
"1 : |xc-x_last| = 1.0086e+00 <= tolX*(1+|x0|) = 5.1104e+00\n",
|
||||
"0 : |proj(x-g)-x| = 2.2396e+01 <= tolG = 1.0000e-01\n",
|
||||
"0 : |proj(x-g)-x| = 2.2396e+01 <= 1e3*eps = 1.0000e-02\n",
|
||||
"1 : maxIter = 30 <= iter = 30\n",
|
||||
"------------------------- DONE! -------------------------\n"
|
||||
]
|
||||
@@ -269,20 +269,6 @@
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# %matplotlib qt\n",
|
||||
"# fig= simpegmt.Utils.dataUtils.plotMT1DModelData(problem,modList[-3:-1])\n",
|
||||
"# fig.suptitle('No stopping-useMref ')\n",
|
||||
"# plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
@@ -303,67 +289,122 @@
|
||||
"arr.diagonal().copy() or arr[['f0','f1']].copy()).\n",
|
||||
" != self.__array_interface__[\"data\"][0]):\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAn0AAAIBCAYAAAA8kwl3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl8VNX5+PHPyUYCAQIoIAgEREGQRURRAYmiRVuwAi5o\nq6KgVmpd0OqvtuXktFZbtypq/bpC3VFErYq2KgRwF0QEBWQLCMi+r1nm+f1xJyHLJJkkk7kz5Hm/\nXvNK7r1nzn0SuHOfnHsWIyIopZRSSqnDW4LfASillFJKqbqnSZ9SSimlVD2gSZ9SSimlVD2gSZ9S\nSimlVD2gSZ9SSimlVD2gSZ9SSimlVD2gSZ9SSimlVD2gSZ9SSimlVD2gSZ9ShxljTDNjzD3GmPHG\nmAbGmP8zxiw0xkwyxjT3Oz6llFL+0KRPqcPPJCAF6ALMAHYCo4BVwEQf41JKKeUjo8uwKXV4McZ8\nKyI9jTEJwAagtYgEgscWiEgvfyNUSinlB23pU+rwEwAIJnpzixI+pZRS9VuS3wEoVZ8ZYxrW8K37\npeJm+p3GmMYisltEfl7iXK2AgzU8n1JKqVpyzqUAWGvz/Di/Pt5VykfGmJq0wglwsoh8Xc1zZQDN\nRGRVDc6plFKqhpxzqcBA4FZgFzDFWvt6tOPQpE8pHwWTvruAlWG+JQF4Guhb3aRPKaVU9DnnmgG/\nAoYA04BlwDPA+dbapdGMRR/vKuW/d0Tky3AKGmOS8JK+ajPGNAYG4Y3qbRbcvR1YAswSkT01qVcp\npVRowce5lwG9gHuttXOC+9cCUZ9CS5M+pfzVCVgfbmERKTDGdALWhfue4CheB4wH0oB9eMkeeMlf\nQ2CfMeZBwFbSV1AppVT19AeGAXdba+c45xKB4Xif+3OjHYyO3lXKRyKSKyLV6tAbfE9+Nd5igVuA\nbCBTRNJFpF3wlQ50CB4rKhMWY0zAGHN/ie3bjDG2GnGFqrODMebS2tRRSd1PGWOOr4u6a8oYk2OM\nWV1m35vGmN01qOs+Y8wiY8w/IhehUqqmnHNJwHXANGvt7OD2AKAfXsIXcM5FNQ/TpE+pGGSMSTLG\nNCz7qmF1Y4FbReQ+EVlT9qCI/Cgi9+N1MB5bjXrzgOHGmBZFVdUwvpI64j0KiTgRuUZEFtdF3bW0\n3RjTH4oH2xxFiN9l8NF+Za4BeojIHZEPUSlVAwIcwPusBLgEGBrcnmytLbTWFg/mc87V+eNeTfqU\nihHGmAxjzOPGmA14U6vsKfOqdutPUAawPIxyKzjU1y8c+cCTeC2EpRhjMo0xM4wxC4wxHxpj2oUo\nM8gYMz/4mmeMSQf+DgwM7rspuIzcJGPMt8aYr40xWcH3jjbGvGWMmWmM+cEYM6HEeZcYY14wxnxv\njHnNGJMWPJZjjOkT/H6PMeYuY8w3xpjPjDEtg/uPMcZ8HjzfXRW1uBljJhtjRpbY3hP8epQxZnYw\n/oXGmAHB/T8zxnwa/DlfNcY0Cr5VgCl4K6YAjABeB0zwfVnGmDnGmLeARcaYhGCL3pfB3+21wXL/\nAdKBr40xF4f3z6eUqkvW2kK8VZB+75zLAX6BN2jvPmvtzmDLH865q51z/wTecs4NqcuY4nb0rjEm\nPgNXhx0RMZGoxxgzDcgCnsJLwMo99hWRyTWo9yOgEBhR0WCNYMI1DUgUkcFh1KnXn1JKBVV2H3DO\ntQaaArnW2nJzpTrn/gZsBtYA/wDGWGtn10Wccd3SJyIVvqy1le4L9X2or0UvPZeeK9S5ImwwcIOI\n3CEiT4rI5LKvGtb7O+AEYLUx5iVjzARjzI3B15+NMS8Bq4Nlbgi30vT0dKy1TJgwgb/+9a+cc845\nZGdnIyI0bNiQgoICRIS8vDwaNmxY7vf997//nX79+jFx4kTWrl2LtZaZM2cydOjQ4nLDhw9n5syZ\nxb/3gQMH8u233zJ58mSuvPLK4nITJkzgoYce4qabbqJ9+/bF+2fMmMEFF1yAiJCVlcW8efMQERo0\naFBcZsqUKYwdOxYRIS0tjcLCQqy17Ny5s/hnLPv/YfTo0UydOrV4Oz09vXh/586dyc7O5ptvvkFE\nuPTSSzniiCPo3bs3vXv3plu3bsXny8zMZO7cuYwbN44XXniBM844o1R9M2fO5Mwzzyw+z8iRIznu\nuOOK6+rUqRMffPBBqfdU9n++qv/n4W5XtC+cYzUppz+X/lyx/HNVxVq7ITg1ywXOudOK9jvnbnTO\njQfOBj611k7Dm52hR7U+4avhsB29m5WVVem+UN9X9DUnJ0fPpecK+dU5V+m5qmkd3sjaiBKR740x\n3YHfAOfhJZdlp2y5D/g/EdlRnbqzsrLo1asXffr0KfV7S05OLvVhmJycXO59WVlZDB06lHfffZf+\n/fvzl7/8paL4Q/6blS2TkJDAqaeeyptvvllqvzHl/wAvGU9CQgIFBQXl4iv5/R//+EemT5/O3r17\nyc7OJikpiUAgQFZWFoFAgLw8r1H2yiuv5J577uGdd95h9OjRjB8/nh49vM/vl156qVwcGRkZGGMY\nNWoUw4cPD/n/qVGjRqXiefTRRznnnHMq/X1UpKr/5+FuV7QvnGM1KVedevTn0p8rnGM1KRcBs4E+\nAM65bCAT+BT4APjAOXcHcA8wsoL3115tsls/X17o0WGt1XPpuUIK/j+M1P/p84GvgQ6RqrOuXoCk\np6cX/x5uv/12ad++vTjnRETk/PPPl+eff15ERCZNmiQjRowo97tbvnx58fcXXnihvPXWWzJv3jwZ\nNGhQ8f4HH3xQxowZIyIiS5culQ4dOkheXp5MmjRJ2rRpI9u2bZN9+/ZJz549Zd68ebJq1Soxxshn\nn30mIiJjxoyRBx98UEREsrKyZN68eSIipWJ/7bXXZPTo0SIicuyxx8qUKVNEROSJJ54oVa6ku+66\nS+644w4REXnjjTfEGCMiIqtXr5aCggIREXn00Ufllltukc2bN0v79u2Lf949e/bIDz/8UC6mBx54\nQLZu3VoqvpkzZ8rQoUOLz/vkk0/KBRdcIPn5+cW/k71795b7mcqK5jURTfpzxZfD9eeqyX0gOzv7\ngezs7POzs7OTg9uTs7OzL83Ozv5FdeuqziuuH+9GSxT/CtBzxdm5IklE/gN8ASwPDk740hjzVcmv\ndXl+Y0yaMaZ9uOXz8vKKW1VvvfVWtmzZUnzskUceYdKkSfTq1YsXX3yRhx9+uNz7H374YXr06EGv\nXr1ISUnhvPPOo2fPniQmJtK7d28efvhhxo0bRyAQoGfPnowaNYp///vfJCcnY4zhlFNOYeTIkfTq\n1YsLL7yQPn36ANClSxcee+wxunXrxs6dO7n++utD/aylvi/attby4IMP0rt3b1asWEHTpk1D/uzX\nXHMNs2bNonfv3nz++eekp6cDMHPmTHr37k2fPn149dVXuemmmzjiiCOYPHkyl156Kb169eL0009n\n6dLyk/CPHz+e5s2bh4yvyNixY+nWrRt9+vShR48eXH/99RQWFpYrV1a8XhNV0Z8rvhyuP1d1OOeM\nc64h3iT5R1tr851z3YGzgB+ste/W5fnjeiBHvMauDh/GGCRyAzkewBsJ+xWhB3KIiFwViXNVcP4L\ngSkikhhGWV+vv8mTJzNv3jweeeSRUvtzc3MZNmwYCxcurFG9+/fvJy0tDYBXXnmFKVOm8MYbb9Q6\nXqVU7BDx+hofOHCA/fv306pVq5B/NOXk5JCQkEBSUhKJiYnFX3v27ElCwqE2s5rcB5xz3YA3gLfw\nHuc+Ya29t3Y/WdUO2z59SsWhMcCfRORuH2OISAJb10q2zoU6VlPz5s3jhhtuQERo1qwZzz77bI3r\nUkrFjoKCAp5++ml2797NgQMHSExMJC0tjdTUVMaOHVuu3zF4nyX5+fns37+fwsJCCgoKKCwspGfP\nnrWOx1r7vXPuF3h9/GbVdQtfEW3pU6oWItzS9xNwpYj8LxL1lah3JuFNnNwSOD4eWvqUUqq6duzY\nQVJSEmlpaSQmVvkxF7ZI3gfqmiZ9StVChJO+/wf0BS6K5H9uY0whsBT4voqibYFTNOlTSsWroke3\nDRo0iNo54ynp08e7SsWOFnhrMi41xuQA5aZPEZHba1Dvd8BiEbmkskLBPn2vhltpdnZ28dQrSinl\np0AgwJIlS/jkk09o2bIlv/zlL/0OKSZpS59StRDhlr5cvMewhvKPYw3eQI6ONaj3CeA8Eal0ZG5R\n0iciVY7q1+tPKVUXAoEAixYtorCwkAYNGpCamkqDBg1o2bJlyH53BQUFLFiwgE8//ZS0tDT69+9P\n165da9W3t7riqaVPkz6laiEeLnZjTGegG/B2ZRdNcI3aViKSG0adev0ppSLu7bffZsuWLTRv3pwD\nBw5w8OBBDh48yIUXXkizZuWXBn/qqado1KgR/fv3p3379lFN9orEw32giCZ9StVCPF3skaTXn1Kq\nLuzdu5eGDRuGnbzt3r2bxo0b13FUlYun+4AmfUrVQjxd7JGk159SSnni6T6gK3IopZRSStUDmvQp\npWokOzu7eBk2pZQKl4gwf/58pk+f7nco9Y4+3lWqFuKpWT+SjDFy5aBBAGRkZvLQ5Mn+BqSUigsH\nDx7k3XffZcOGDVx44YW0bNnS75BqLZ7uAzpPn1KqRjrOmgXAKp/jUErFh/Xr1zN16lQ6duzINddc\nE3IKlsOdcy4FwFpbdm31qNCkT6kYYow5BRgBtAFSSx7Cm6fvYl8Cq0TBgQNIIIApsQD5zaNHsyM3\nt1zZsq2C4ZZTSsW3lStX8vrrr/Pzn/+c7t27+x1O1DnnUoGBwK3ALufcFGvt69GOI+pJnzFmMHAe\n0BVohjcJ7XZgCfCeiMyIdkxKxQJjzM3Ag8BGYCWQHzxU0YTNMeGn+fP5e9OmHNm9OLine truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7f7d27dca950>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%matplotlib qt\n",
|
||||
"%matplotlib inline\n",
|
||||
"fig = simpegmt.Utils.dataUtils.plotMT1DModelData(problem,[m_0,mopt])\n",
|
||||
"fig.suptitle('No stopping-useMref')\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"reg.alpha_xx = 0.001\n",
|
||||
"saveModel.fileName = 'Inversion_NoStoppingregMesh_smoothTrueWxx'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 4.26403069e+01, 3.00486580e+02, 1.66374083e+02,\n",
|
||||
" 7.56749193e+00, 1.95232762e+00, 1.28591697e+01,\n",
|
||||
" 9.25428204e+01, 1.58941963e+02, 1.23328303e+02,\n",
|
||||
" 7.51647545e+01, 4.59620120e+01, 2.84873346e+01,\n",
|
||||
" 1.67854087e+01, 9.65288171e+00, 5.69417961e+00,\n",
|
||||
" 4.11415753e+00, 4.04091270e+00, 5.89702878e+00,\n",
|
||||
" 1.03685013e+01, 1.49615226e+01, 1.78450685e+01,\n",
|
||||
" 2.21298842e+01, 3.14894964e+01, 5.03867813e+01,\n",
|
||||
" 8.57783978e+01, 1.39225191e+02, 2.10683526e+02,\n",
|
||||
" 3.13430053e+02, 4.54348237e+02, 6.35619644e+02,\n",
|
||||
" 8.20077602e+02, 9.86597201e+02, 1.13311026e+03,\n",
|
||||
" 1.22681391e+03, 1.23177188e+03, 1.13462480e+03,\n",
|
||||
" 9.82900015e+02, 7.79370193e+02, 5.53800277e+02,\n",
|
||||
" 3.42690620e+02, 1.91257904e+02, 1.00287868e+02,\n",
|
||||
" 4.40936077e+01, 1.57081219e+01, 4.84819824e+00,\n",
|
||||
" 2.37649055e+00, 2.81483584e+00, 7.91239926e+00,\n",
|
||||
" 3.26193232e+01, 1.10105145e+02, 2.29971251e+02,\n",
|
||||
" 3.48665470e+02, 4.30985817e+02, 4.28578461e+02,\n",
|
||||
" 3.43909988e+02, 2.60243364e+02, 2.15181922e+02,\n",
|
||||
" 2.03527866e+02, 2.38158572e+02, 3.70346010e+02,\n",
|
||||
" 6.61570166e+02, 1.12877904e+03, 1.82064196e+03,\n",
|
||||
" 2.57897162e+03, 3.02323079e+03])"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
|
||||
" ***Done using same solver as the problem***\n",
|
||||
"SimPEG.SaveModelEveryIteration will save your models as: '###-Inversion_NoStoppingregMesh_smoothTrueWxx.npy'\n",
|
||||
"============================ Inexact Gauss Newton ============================\n",
|
||||
" # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n",
|
||||
"-----------------------------------------------------------------------------\n",
|
||||
" 0 2.54e+05 2.18e+05 0.00e+00 2.18e+05 4.01e+04 0 \n",
|
||||
" 1 2.54e+05 2.50e+04 2.21e-03 2.55e+04 5.64e+03 0 \n",
|
||||
" 2 2.54e+05 3.37e+03 4.74e-03 4.57e+03 9.91e+02 0 Skip BFGS \n",
|
||||
" 3 3.17e+04 1.77e+03 4.92e-03 1.93e+03 2.83e+02 0 Skip BFGS \n",
|
||||
" 4 3.17e+04 9.89e+02 1.68e-02 1.52e+03 2.15e+02 0 Skip BFGS \n",
|
||||
" 5 3.17e+04 7.47e+02 1.84e-02 1.33e+03 1.06e+02 0 \n",
|
||||
" 6 3.96e+03 6.19e+02 2.14e-02 7.04e+02 1.39e+02 0 Skip BFGS \n",
|
||||
" 7 3.96e+03 3.25e+02 5.92e-02 5.60e+02 2.61e+02 0 \n",
|
||||
" 8 3.96e+03 3.55e+02 4.00e-02 5.14e+02 1.17e+02 0 \n",
|
||||
" 9 4.95e+02 3.29e+02 4.10e-02 3.50e+02 1.13e+02 1 \n",
|
||||
" 10 4.95e+02 2.48e+02 1.43e-01 3.19e+02 3.78e+02 0 \n",
|
||||
" 11 4.95e+02 1.75e+02 1.18e-01 2.33e+02 2.31e+02 1 \n",
|
||||
" 12 6.19e+01 7.93e+01 1.45e-01 8.83e+01 5.76e+01 0 Skip BFGS \n",
|
||||
" 13 6.19e+01 6.97e+01 1.87e-01 8.13e+01 9.12e+01 0 Skip BFGS \n",
|
||||
" 14 6.19e+01 6.36e+01 1.88e-01 7.53e+01 2.36e+01 0 \n",
|
||||
" 15 7.74e+00 6.22e+01 1.94e-01 6.37e+01 2.16e+01 0 \n",
|
||||
" 16 7.74e+00 5.72e+01 2.23e-01 5.89e+01 3.88e+01 1 \n",
|
||||
" 17 7.74e+00 5.15e+01 3.19e-01 5.40e+01 4.78e+01 1 Skip BFGS \n",
|
||||
" 18 9.68e-01 4.36e+01 3.51e-01 4.39e+01 1.50e+01 0 \n",
|
||||
" 19 9.68e-01 4.22e+01 4.45e-01 4.27e+01 2.01e+01 0 Skip BFGS \n",
|
||||
" 20 9.68e-01 4.13e+01 4.20e-01 4.17e+01 9.91e+00 0 \n",
|
||||
" 21 1.21e-01 4.02e+01 4.47e-01 4.02e+01 2.47e+01 1 \n",
|
||||
" 22 1.21e-01 3.96e+01 4.82e-01 3.96e+01 2.93e+01 0 Skip BFGS \n",
|
||||
" 23 1.21e-01 3.92e+01 4.94e-01 3.93e+01 2.75e+01 0 \n",
|
||||
" 24 1.51e-02 3.90e+01 5.51e-01 3.90e+01 2.27e+01 0 Skip BFGS \n",
|
||||
" 25 1.51e-02 3.82e+01 5.19e-01 3.82e+01 1.94e+01 0 \n",
|
||||
" 26 1.51e-02 3.79e+01 5.52e-01 3.79e+01 1.71e+01 1 Skip BFGS \n",
|
||||
" 27 1.89e-03 3.76e+01 5.39e-01 3.76e+01 2.16e+01 1 \n",
|
||||
" 28 1.89e-03 3.76e+01 6.14e-01 3.76e+01 3.82e+01 1 Skip BFGS \n",
|
||||
" 29 1.89e-03 3.69e+01 5.53e-01 3.69e+01 3.71e+01 0 \n",
|
||||
" 30 2.36e-04 3.57e+01 5.65e-01 3.57e+01 1.91e+01 0 \n",
|
||||
"------------------------- STOP! -------------------------\n",
|
||||
"1 : |fc-fOld| = 1.1797e+00 <= tolF*(1+|f0|) = 2.1833e+04\n",
|
||||
"1 : |xc-x_last| = 1.0384e+00 <= tolX*(1+|x0|) = 5.1104e+00\n",
|
||||
"0 : |proj(x-g)-x| = 1.9125e+01 <= tolG = 1.0000e-01\n",
|
||||
"0 : |proj(x-g)-x| = 1.9125e+01 <= 1e3*eps = 1.0000e-02\n",
|
||||
"1 : maxIter = 30 <= iter = 30\n",
|
||||
"------------------------- DONE! -------------------------\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"1/np.exp(mopt)"
|
||||
"moptWxx = inv.run(m_0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAoYAAAIBCAYAAADUP34ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7f7d1ef376d0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%matplotlib inline\n",
|
||||
"fig = simpegmt.Utils.dataUtils.plotMT1DModelData(problem,[mopt,moptWxx])\n",
|
||||
"fig.suptitle('No stopping-useMref - Wxx')\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -173,8 +173,7 @@
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"scrolled": true
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -252,30 +251,31 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": 22,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/home/gudni/anaconda/lib/python2.7/site-packages/numpy/ma/core.py:2834: FutureWarning: Numpy has detected that you (may be) writing to an array returned\n",
|
||||
"by numpy.diagonal or by selecting multiple fields in a record\n",
|
||||
"array. This code will likely break in a future numpy release --\n",
|
||||
"see numpy.diagonal or arrays.indexing reference docs for details.\n",
|
||||
"The quick fix is to make an explicit copy (e.g., do\n",
|
||||
"arr.diagonal().copy() or arr[['f0','f1']].copy()).\n",
|
||||
" if (obj.__array_interface__[\"data\"][0]\n",
|
||||
"/home/gudni/anaconda/lib/python2.7/site-packages/numpy/ma/core.py:2835: FutureWarning: Numpy has detected that you (may be) writing to an array returned\n",
|
||||
"by numpy.diagonal or by selecting multiple fields in a record\n",
|
||||
"array. This code will likely break in a future numpy release --\n",
|
||||
"see numpy.diagonal or arrays.indexing reference docs for details.\n",
|
||||
"The quick fix is to make an explicit copy (e.g., do\n",
|
||||
"arr.diagonal().copy() or arr[['f0','f1']].copy()).\n",
|
||||
" != self.__array_interface__[\"data\"][0]):\n"
|
||||
"ename": "AttributeError",
|
||||
"evalue": "'Figure' object has no attribute 'supplot'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-22-599803cc310e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mu'matplotlib inline'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msimpegmt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mUtils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataUtils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplotMT1DModelData\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mproblem\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mm_0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmopt\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msupplot\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mAttributeError\u001b[0m: 'Figure' object has no attribute 'supplot'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAn0AAAIBCAYAAAA8kwl3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7f7c525e7090>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
@@ -306,9 +306,9 @@
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%matplotlib qt\n",
|
||||
"simpegmt.Utils.dataUtils.plotMT1DModelData(problem,modList[-3:-1])\n",
|
||||
"plt.show()"
|
||||
"# %matplotlib qt\n",
|
||||
"# simpegmt.Utils.dataUtils.plotMT1DModelData(problem,modList[-3:-1])\n",
|
||||
"# plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -148,7 +148,6 @@
|
||||
"reg.smoothModel = True\n",
|
||||
"reg.alpha_s = 1e-7\n",
|
||||
"reg.alpha_x = 1.\n",
|
||||
"\n",
|
||||
"# Inversion problem\n",
|
||||
"invProb = simpeg.InvProblem.BaseInvProblem(dmis, reg, opt)\n",
|
||||
"invProb.counter = C\n",
|
||||
@@ -156,6 +155,7 @@
|
||||
"beta = simpeg.Directives.BetaSchedule()\n",
|
||||
"betaest = simpeg.Directives.BetaEstimate_ByEig(beta0_ratio=0.75)\n",
|
||||
"targmis = simpeg.Directives.TargetMisfit()\n",
|
||||
"targmis.target = 1/2 * survey.nD\n",
|
||||
"saveModel = simpeg.Directives.SaveModelEveryIteration()\n",
|
||||
"saveModel.fileName = 'Inversion_TargMisEqnD_smoothTrue'\n",
|
||||
"# Create an inversion object\n",
|
||||
@@ -182,57 +182,283 @@
|
||||
"============================ Inexact Gauss Newton ============================\n",
|
||||
" # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n",
|
||||
"-----------------------------------------------------------------------------\n",
|
||||
" 0 2.78e+05 2.18e+05 0.00e+00 2.18e+05 4.01e+04 0 \n",
|
||||
" 1 2.78e+05 2.52e+04 3.34e-05 2.52e+04 6.20e+03 0 \n",
|
||||
" 2 2.78e+05 3.46e+03 1.32e-04 3.50e+03 1.03e+03 0 Skip BFGS \n",
|
||||
" 3 3.48e+04 1.76e+03 6.52e-04 1.78e+03 2.62e+02 0 Skip BFGS \n",
|
||||
" 4 3.48e+04 1.12e+03 8.86e-03 1.43e+03 1.98e+02 0 Skip BFGS \n",
|
||||
" 5 3.48e+04 8.39e+02 1.18e-02 1.25e+03 1.18e+02 0 \n",
|
||||
" 6 4.35e+03 6.98e+02 1.46e-02 7.61e+02 1.46e+02 0 \n",
|
||||
" 7 4.35e+03 4.05e+02 4.96e-02 6.20e+02 1.60e+02 0 \n",
|
||||
" 8 4.35e+03 2.67e+02 5.64e-02 5.12e+02 1.81e+02 0 \n",
|
||||
" 9 5.43e+02 2.25e+02 5.88e-02 2.56e+02 7.07e+01 0 \n",
|
||||
" 10 5.43e+02 1.96e+02 7.46e-02 2.37e+02 1.23e+02 0 \n",
|
||||
" 11 5.43e+02 1.63e+02 7.82e-02 2.05e+02 5.88e+01 0 \n",
|
||||
" 12 6.79e+01 1.21e+02 1.32e-01 1.30e+02 1.24e+02 0 Skip BFGS \n",
|
||||
" 13 6.79e+01 1.16e+02 1.57e-01 1.27e+02 1.15e+02 0 \n",
|
||||
" 14 6.79e+01 1.04e+02 1.99e-01 1.17e+02 8.66e+01 1 \n",
|
||||
" 15 8.49e+00 9.98e+01 1.83e-01 1.01e+02 8.00e+01 1 \n",
|
||||
" 16 8.49e+00 9.88e+01 2.31e-01 1.01e+02 8.13e+01 0 \n",
|
||||
" 17 8.49e+00 9.59e+01 2.05e-01 9.76e+01 9.01e+01 0 \n",
|
||||
" 18 1.06e+00 6.64e+01 2.03e-01 6.66e+01 5.84e+01 0 Skip BFGS \n",
|
||||
" 0 3.72e+05 2.18e+05 0.00e+00 2.18e+05 4.01e+04 0 \n",
|
||||
" 1 3.72e+05 2.50e+04 2.19e-03 2.58e+04 5.62e+03 0 \n",
|
||||
" 2 3.72e+05 3.39e+03 4.60e-03 5.10e+03 1.01e+03 0 Skip BFGS \n",
|
||||
" 3 4.65e+04 1.91e+03 4.23e-03 2.10e+03 3.07e+02 0 Skip BFGS \n",
|
||||
" 4 4.65e+04 1.18e+03 1.15e-02 1.72e+03 1.62e+02 0 Skip BFGS \n",
|
||||
" 5 4.65e+04 1.00e+03 1.30e-02 1.60e+03 8.35e+01 0 \n",
|
||||
" 6 5.81e+03 8.78e+02 1.50e-02 9.66e+02 1.87e+02 0 Skip BFGS \n",
|
||||
" 7 5.81e+03 3.96e+02 5.56e-02 7.19e+02 2.51e+02 0 \n",
|
||||
" 8 5.81e+03 3.44e+02 3.87e-02 5.69e+02 1.35e+02 1 \n",
|
||||
" 9 7.26e+02 2.93e+02 4.41e-02 3.25e+02 9.13e+01 0 Skip BFGS \n",
|
||||
" 10 7.26e+02 2.41e+02 6.62e-02 2.89e+02 1.21e+02 1 \n",
|
||||
" 11 7.26e+02 1.47e+02 1.33e-01 2.44e+02 1.94e+02 0 \n",
|
||||
" 12 9.08e+01 1.35e+02 1.15e-01 1.45e+02 7.35e+01 0 \n",
|
||||
" 13 9.08e+01 8.40e+01 2.31e-01 1.05e+02 1.27e+02 0 \n",
|
||||
" 14 9.08e+01 7.09e+01 2.78e-01 9.61e+01 7.14e+01 0 \n",
|
||||
" 15 1.13e+01 6.65e+01 2.55e-01 6.94e+01 2.89e+01 0 \n",
|
||||
" 16 1.13e+01 6.41e+01 3.38e-01 6.80e+01 5.48e+01 0 \n",
|
||||
" 17 1.13e+01 5.44e+01 3.65e-01 5.85e+01 2.19e+01 0 \n",
|
||||
" 18 1.42e+00 5.04e+01 4.33e-01 5.10e+01 7.08e+01 0 Skip BFGS \n",
|
||||
" 19 1.42e+00 4.76e+01 4.52e-01 4.82e+01 1.68e+01 0 \n",
|
||||
" 20 1.42e+00 4.62e+01 4.72e-01 4.69e+01 1.39e+01 0 \n",
|
||||
" 21 1.77e-01 4.61e+01 4.68e-01 4.62e+01 1.08e+01 0 \n",
|
||||
" 22 1.77e-01 4.54e+01 4.44e-01 4.55e+01 3.34e+01 0 Skip BFGS \n",
|
||||
" 23 1.77e-01 4.40e+01 4.46e-01 4.41e+01 1.57e+01 0 \n",
|
||||
" 24 2.22e-02 4.20e+01 3.77e-01 4.20e+01 2.55e+01 2 \n",
|
||||
" 25 2.22e-02 4.03e+01 3.16e-01 4.03e+01 2.54e+01 0 \n",
|
||||
" 26 2.22e-02 3.99e+01 3.82e-01 3.99e+01 2.41e+01 0 \n",
|
||||
" 27 2.77e-03 3.93e+01 3.49e-01 3.93e+01 3.42e+01 3 Skip BFGS \n",
|
||||
" 28 2.77e-03 3.88e+01 4.32e-01 3.88e+01 2.44e+01 0 \n",
|
||||
" 29 2.77e-03 3.87e+01 4.49e-01 3.87e+01 3.02e+01 2 Skip BFGS \n",
|
||||
" 30 3.46e-04 3.86e+01 4.63e-01 3.86e+01 2.54e+01 0 \n",
|
||||
" 31 3.46e-04 3.86e+01 4.39e-01 3.86e+01 2.32e+01 0 \n",
|
||||
" 32 3.46e-04 3.84e+01 4.42e-01 3.84e+01 2.16e+01 1 Skip BFGS \n",
|
||||
" 33 4.33e-05 3.84e+01 4.54e-01 3.84e+01 2.25e+01 0 Skip BFGS \n",
|
||||
" 34 4.33e-05 3.83e+01 4.44e-01 3.83e+01 2.15e+01 0 \n",
|
||||
" 35 4.33e-05 3.72e+01 4.57e-01 3.72e+01 1.65e+01 0 \n",
|
||||
" 36 5.41e-06 3.64e+01 4.85e-01 3.64e+01 2.32e+01 1 Skip BFGS \n",
|
||||
" 37 5.41e-06 3.61e+01 4.70e-01 3.61e+01 2.34e+01 2 \n",
|
||||
" 38 5.41e-06 3.56e+01 4.35e-01 3.56e+01 2.75e+01 2 Skip BFGS \n",
|
||||
" 39 6.76e-07 3.56e+01 4.43e-01 3.56e+01 2.21e+01 0 \n",
|
||||
" 40 6.76e-07 3.55e+01 4.44e-01 3.55e+01 2.07e+01 2 \n",
|
||||
" 41 6.76e-07 3.50e+01 4.62e-01 3.50e+01 2.21e+01 2 Skip BFGS \n",
|
||||
" 42 8.45e-08 3.49e+01 4.41e-01 3.49e+01 2.31e+01 2 \n",
|
||||
" 43 8.45e-08 3.28e+01 4.83e-01 3.28e+01 3.06e+01 0 Skip BFGS \n",
|
||||
" 44 8.45e-08 3.22e+01 5.30e-01 3.22e+01 2.44e+01 1 Skip BFGS \n",
|
||||
" 45 1.06e-08 3.19e+01 5.73e-01 3.19e+01 1.06e+01 0 Skip BFGS \n",
|
||||
" 46 1.06e-08 3.18e+01 5.27e-01 3.18e+01 1.20e+01 1 \n",
|
||||
" 47 1.06e-08 3.17e+01 4.59e-01 3.17e+01 9.10e+00 0 Skip BFGS \n",
|
||||
" 48 1.32e-09 3.17e+01 5.04e-01 3.17e+01 1.08e+01 2 \n",
|
||||
" 49 1.32e-09 3.16e+01 5.22e-01 3.16e+01 9.32e+00 0 \n",
|
||||
" 50 1.32e-09 3.13e+01 4.58e-01 3.13e+01 1.42e+01 1 \n",
|
||||
"------------------------- STOP! -------------------------\n",
|
||||
"1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 2.1833e+04\n",
|
||||
"1 : |xc-x_last| = 2.2460e+00 <= tolX*(1+|x0|) = 5.1104e+00\n",
|
||||
"0 : |proj(x-g)-x| = 5.8352e+01 <= tolG = 1.0000e-01\n",
|
||||
"0 : |proj(x-g)-x| = 5.8352e+01 <= 1e3*eps = 1.0000e-02\n",
|
||||
"0 : maxIter = 50 <= iter = 19\n",
|
||||
"------------------------- DONE! -------------------------\n"
|
||||
"1 : |fc-fOld| = 3.0199e-01 <= tolF*(1+|f0|) = 2.1833e+04\n",
|
||||
"1 : |xc-x_last| = 4.2553e+00 <= tolX*(1+|x0|) = 5.1104e+00\n",
|
||||
"0 : |proj(x-g)-x| = 1.4222e+01 <= tolG = 1.0000e-01\n",
|
||||
"0 : |proj(x-g)-x| = 1.4222e+01 <= 1e3*eps = 1.0000e-02\n",
|
||||
"1 : maxIter = 50 <= iter = 50\n",
|
||||
"------------------------- DONE! -------------------------\n",
|
||||
"SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
|
||||
" ***Done using same solver as the problem***\n",
|
||||
"SimPEG.SaveModelEveryIteration will save your models as: '###-Inversion_TargMisEqnD_smoothTrue.npy'\n",
|
||||
"============================ Inexact Gauss Newton ============================\n",
|
||||
" # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n",
|
||||
"-----------------------------------------------------------------------------\n",
|
||||
" 0 1.70e+05 2.18e+05 0.00e+00 2.18e+05 4.01e+04 0 \n",
|
||||
" 1 1.70e+05 2.50e+04 2.22e-03 2.53e+04 5.66e+03 0 \n",
|
||||
" 2 1.70e+05 3.35e+03 4.85e-03 4.18e+03 9.85e+02 0 Skip BFGS \n",
|
||||
" 3 2.13e+04 1.64e+03 5.93e-03 1.77e+03 2.67e+02 0 Skip BFGS \n",
|
||||
" 4 2.13e+04 8.27e+02 2.45e-02 1.35e+03 2.67e+02 0 \n",
|
||||
" 5 2.13e+04 7.95e+02 1.83e-02 1.18e+03 1.28e+02 0 \n",
|
||||
" 6 2.66e+03 7.09e+02 2.17e-02 7.67e+02 1.49e+02 0 \n",
|
||||
" 7 2.66e+03 4.57e+02 7.33e-02 6.52e+02 2.10e+02 0 \n",
|
||||
" 8 2.66e+03 4.28e+02 5.88e-02 5.84e+02 1.75e+02 1 \n",
|
||||
" 9 3.32e+02 3.56e+02 7.66e-02 3.82e+02 1.12e+02 0 \n",
|
||||
" 10 3.32e+02 2.71e+02 1.23e-01 3.12e+02 1.62e+02 1 \n",
|
||||
" 11 3.32e+02 2.06e+02 1.90e-01 2.69e+02 1.31e+02 1 \n",
|
||||
" 12 4.15e+01 1.69e+02 2.30e-01 1.79e+02 1.30e+02 1 \n",
|
||||
" 13 4.15e+01 1.53e+02 6.02e-01 1.78e+02 2.84e+02 0 Skip BFGS \n",
|
||||
" 14 4.15e+01 1.08e+02 4.27e-01 1.26e+02 1.01e+02 0 \n",
|
||||
" 15 5.19e+00 7.04e+01 5.63e-01 7.33e+01 7.23e+01 0 \n",
|
||||
" 16 5.19e+00 6.28e+01 6.89e-01 6.64e+01 1.31e+02 1 Skip BFGS \n",
|
||||
" 17 5.19e+00 5.21e+01 9.36e-01 5.70e+01 1.20e+02 0 Skip BFGS \n",
|
||||
" 18 6.49e-01 4.37e+01 9.80e-01 4.43e+01 7.78e+00 0 Skip BFGS \n",
|
||||
" 19 6.49e-01 4.28e+01 1.10e+00 4.35e+01 1.60e+01 0 \n",
|
||||
" 20 6.49e-01 4.27e+01 1.18e+00 4.35e+01 1.31e+01 0 \n",
|
||||
" 21 8.11e-02 4.27e+01 1.23e+00 4.28e+01 1.39e+01 2 Skip BFGS \n",
|
||||
" 22 8.11e-02 4.26e+01 1.20e+00 4.27e+01 1.26e+01 2 \n",
|
||||
" 23 8.11e-02 4.16e+01 1.47e+00 4.17e+01 1.59e+01 2 \n",
|
||||
" 24 1.01e-02 4.13e+01 1.38e+00 4.13e+01 3.20e+01 0 Skip BFGS \n",
|
||||
" 25 1.01e-02 4.10e+01 1.37e+00 4.10e+01 1.70e+01 0 \n",
|
||||
" 26 1.01e-02 4.04e+01 1.44e+00 4.05e+01 2.21e+01 1 Skip BFGS \n",
|
||||
" 27 1.27e-03 4.01e+01 1.33e+00 4.01e+01 2.03e+01 2 \n",
|
||||
" 28 1.27e-03 4.00e+01 1.49e+00 4.00e+01 2.19e+01 0 \n",
|
||||
" 29 1.27e-03 4.00e+01 1.48e+00 4.00e+01 2.50e+01 2 \n",
|
||||
" 30 1.58e-04 4.00e+01 1.49e+00 4.00e+01 2.38e+01 2 \n",
|
||||
" 31 1.58e-04 3.99e+01 1.44e+00 3.99e+01 2.75e+01 2 \n",
|
||||
" 32 1.58e-04 3.97e+01 1.64e+00 3.97e+01 2.91e+01 1 \n",
|
||||
" 33 1.98e-05 3.93e+01 1.79e+00 3.93e+01 3.19e+01 3 Skip BFGS \n",
|
||||
" 34 1.98e-05 3.90e+01 2.03e+00 3.90e+01 3.42e+01 2 Skip BFGS \n",
|
||||
" 35 1.98e-05 3.88e+01 2.44e+00 3.88e+01 3.22e+01 1 Skip BFGS \n",
|
||||
" 36 2.47e-06 3.86e+01 1.90e+00 3.86e+01 1.92e+01 0 \n",
|
||||
" 37 2.47e-06 3.85e+01 2.26e+00 3.85e+01 2.25e+01 2 \n",
|
||||
" 38 2.47e-06 3.84e+01 2.25e+00 3.84e+01 2.03e+01 0 Skip BFGS \n",
|
||||
" 39 3.09e-07 3.84e+01 2.36e+00 3.84e+01 2.10e+01 0 \n",
|
||||
" 40 3.09e-07 3.84e+01 2.49e+00 3.84e+01 2.08e+01 2 Skip BFGS \n",
|
||||
" 41 3.09e-07 3.84e+01 2.37e+00 3.84e+01 2.08e+01 1 \n",
|
||||
" 42 3.87e-08 3.78e+01 2.59e+00 3.78e+01 1.62e+01 1 Skip BFGS \n",
|
||||
" 43 3.87e-08 3.77e+01 2.62e+00 3.77e+01 1.14e+01 0 Skip BFGS \n",
|
||||
" 44 3.87e-08 3.76e+01 2.83e+00 3.76e+01 1.67e+01 3 \n",
|
||||
" 45 4.83e-09 3.75e+01 2.80e+00 3.75e+01 2.17e+01 2 Skip BFGS \n",
|
||||
" 46 4.83e-09 3.73e+01 2.73e+00 3.73e+01 1.19e+01 0 \n",
|
||||
" 47 4.83e-09 3.71e+01 2.46e+00 3.71e+01 1.67e+01 2 \n",
|
||||
" 48 6.04e-10 3.71e+01 2.33e+00 3.71e+01 1.45e+01 0 \n",
|
||||
" 49 6.04e-10 3.71e+01 2.46e+00 3.71e+01 1.85e+01 1 \n",
|
||||
" 50 6.04e-10 3.70e+01 2.39e+00 3.70e+01 1.99e+01 3 Skip BFGS \n",
|
||||
"------------------------- STOP! -------------------------\n",
|
||||
"1 : |fc-fOld| = 2.5694e-02 <= tolF*(1+|f0|) = 2.1833e+04\n",
|
||||
"1 : |xc-x_last| = 1.9839e+00 <= tolX*(1+|x0|) = 5.1104e+00\n",
|
||||
"0 : |proj(x-g)-x| = 1.9850e+01 <= tolG = 1.0000e-01\n",
|
||||
"0 : |proj(x-g)-x| = 1.9850e+01 <= 1e3*eps = 1.0000e-02\n",
|
||||
"1 : maxIter = 50 <= iter = 50\n",
|
||||
"------------------------- DONE! -------------------------\n",
|
||||
"SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
|
||||
" ***Done using same solver as the problem***\n",
|
||||
"SimPEG.SaveModelEveryIteration will save your models as: '###-Inversion_TargMisEqnD_smoothTrue.npy'\n",
|
||||
"============================ Inexact Gauss Newton ============================\n",
|
||||
" # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n",
|
||||
"-----------------------------------------------------------------------------\n",
|
||||
" 0 1.19e+05 2.18e+05 0.00e+00 2.18e+05 4.01e+04 0 Skip BFGS \n",
|
||||
" 1 1.19e+05 2.50e+04 2.22e-03 2.52e+04 5.67e+03 0 Skip BFGS \n",
|
||||
" 2 1.19e+05 3.35e+03 4.92e-03 3.93e+03 9.84e+02 0 Skip BFGS \n",
|
||||
" 3 1.49e+04 1.53e+03 7.13e-03 1.63e+03 2.55e+02 0 Skip BFGS \n",
|
||||
" 4 1.49e+04 7.29e+02 3.29e-02 1.22e+03 2.94e+02 0 \n",
|
||||
" 5 1.49e+04 6.80e+02 2.16e-02 1.00e+03 1.41e+02 0 \n",
|
||||
" 6 1.86e+03 6.17e+02 2.45e-02 6.63e+02 1.44e+02 0 \n",
|
||||
" 7 1.86e+03 4.77e+02 7.62e-02 6.19e+02 2.19e+02 0 \n",
|
||||
" 8 1.86e+03 4.43e+02 6.42e-02 5.63e+02 2.03e+02 1 \n",
|
||||
" 9 2.32e+02 3.74e+02 8.67e-02 3.94e+02 1.32e+02 0 \n",
|
||||
" 10 2.32e+02 2.67e+02 1.91e-01 3.11e+02 1.89e+02 1 \n",
|
||||
" 11 2.32e+02 1.29e+02 5.16e-01 2.49e+02 1.79e+02 0 Skip BFGS \n",
|
||||
" 12 2.91e+01 1.32e+02 3.87e-01 1.43e+02 1.57e+02 1 \n",
|
||||
" 13 2.91e+01 1.11e+02 6.45e-01 1.30e+02 1.91e+02 1 \n",
|
||||
" 14 2.91e+01 6.74e+01 9.75e-01 9.57e+01 1.25e+02 0 Skip BFGS \n",
|
||||
" 15 3.63e+00 5.28e+01 1.03e+00 5.66e+01 1.14e+01 0 Skip BFGS \n",
|
||||
" 16 3.63e+00 4.93e+01 1.04e+00 5.31e+01 3.75e+01 1 Skip BFGS \n",
|
||||
" 17 3.63e+00 4.65e+01 1.16e+00 5.07e+01 8.86e+01 0 \n",
|
||||
" 18 4.54e-01 4.36e+01 1.16e+00 4.42e+01 5.66e+01 0 \n",
|
||||
" 19 4.54e-01 4.18e+01 1.21e+00 4.23e+01 5.22e+01 1 Skip BFGS \n",
|
||||
" 20 4.54e-01 3.95e+01 1.24e+00 4.01e+01 1.23e+01 0 \n",
|
||||
" 21 5.68e-02 3.93e+01 1.26e+00 3.94e+01 1.03e+01 0 \n",
|
||||
" 22 5.68e-02 3.92e+01 1.29e+00 3.93e+01 9.85e+00 0 \n",
|
||||
" 23 5.68e-02 3.89e+01 1.44e+00 3.90e+01 1.92e+01 1 Skip BFGS \n",
|
||||
" 24 7.09e-03 3.87e+01 1.36e+00 3.87e+01 1.35e+01 0 \n",
|
||||
" 25 7.09e-03 3.86e+01 1.34e+00 3.86e+01 1.28e+01 0 Skip BFGS \n",
|
||||
" 26 7.09e-03 3.85e+01 1.36e+00 3.85e+01 1.29e+01 0 \n",
|
||||
" 27 8.87e-04 3.79e+01 1.32e+00 3.79e+01 1.64e+01 1 Skip BFGS \n",
|
||||
" 28 8.87e-04 3.79e+01 1.30e+00 3.79e+01 1.60e+01 0 \n",
|
||||
" 29 8.87e-04 3.79e+01 1.22e+00 3.79e+01 2.12e+01 0 Skip BFGS \n",
|
||||
" 30 1.11e-04 3.77e+01 1.26e+00 3.77e+01 1.51e+01 0 \n",
|
||||
" 31 1.11e-04 3.77e+01 1.24e+00 3.77e+01 1.43e+01 0 Skip BFGS \n",
|
||||
" 32 1.11e-04 3.76e+01 1.24e+00 3.76e+01 1.51e+01 0 \n",
|
||||
" 33 1.39e-05 3.75e+01 1.29e+00 3.75e+01 1.85e+01 2 Skip BFGS \n",
|
||||
" 34 1.39e-05 3.75e+01 1.24e+00 3.75e+01 1.58e+01 1 \n",
|
||||
" 35 1.39e-05 3.75e+01 1.17e+00 3.75e+01 1.76e+01 1 Skip BFGS \n",
|
||||
" 36 1.73e-06 3.75e+01 1.24e+00 3.75e+01 1.61e+01 1 \n",
|
||||
" 37 1.73e-06 3.74e+01 1.41e+00 3.74e+01 1.76e+01 3 Skip BFGS \n",
|
||||
" 38 1.73e-06 3.74e+01 1.37e+00 3.74e+01 1.76e+01 2 \n",
|
||||
" 39 2.17e-07 3.73e+01 1.45e+00 3.73e+01 2.92e+01 1 Skip BFGS \n",
|
||||
" 40 2.17e-07 3.71e+01 1.51e+00 3.71e+01 2.50e+01 1 \n",
|
||||
" 41 2.17e-07 3.70e+01 1.46e+00 3.70e+01 3.25e+01 0 \n",
|
||||
" 42 2.71e-08 3.67e+01 1.40e+00 3.67e+01 3.70e+01 2 Skip BFGS \n",
|
||||
" 43 2.71e-08 3.67e+01 1.39e+00 3.67e+01 3.64e+01 2 Skip BFGS \n",
|
||||
" 44 2.71e-08 3.66e+01 1.40e+00 3.66e+01 3.47e+01 2 \n",
|
||||
" 45 3.38e-09 3.62e+01 1.30e+00 3.62e+01 2.46e+01 1 Skip BFGS \n",
|
||||
" 46 3.38e-09 3.61e+01 1.24e+00 3.61e+01 1.97e+01 1 Skip BFGS \n",
|
||||
" 47 3.38e-09 3.61e+01 1.28e+00 3.61e+01 2.11e+01 1 \n",
|
||||
" 48 4.23e-10 3.60e+01 1.28e+00 3.60e+01 1.81e+01 0 Skip BFGS \n",
|
||||
" 49 4.23e-10 3.60e+01 1.26e+00 3.60e+01 1.81e+01 1 \n",
|
||||
" 50 4.23e-10 3.59e+01 1.27e+00 3.59e+01 1.80e+01 1 \n",
|
||||
"------------------------- STOP! -------------------------\n",
|
||||
"1 : |fc-fOld| = 1.8765e-02 <= tolF*(1+|f0|) = 2.1833e+04\n",
|
||||
"1 : |xc-x_last| = 4.3809e-01 <= tolX*(1+|x0|) = 5.1104e+00\n",
|
||||
"0 : |proj(x-g)-x| = 1.8033e+01 <= tolG = 1.0000e-01\n",
|
||||
"0 : |proj(x-g)-x| = 1.8033e+01 <= 1e3*eps = 1.0000e-02\n",
|
||||
"1 : maxIter = 50 <= iter = 50\n",
|
||||
"------------------------- DONE! -------------------------\n",
|
||||
"SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
|
||||
" ***Done using same solver as the problem***\n",
|
||||
"SimPEG.SaveModelEveryIteration will save your models as: '###-Inversion_TargMisEqnD_smoothTrue.npy'\n",
|
||||
"============================ Inexact Gauss Newton ============================\n",
|
||||
" # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n",
|
||||
"-----------------------------------------------------------------------------\n",
|
||||
" 0 2.04e+05 2.18e+05 0.00e+00 2.18e+05 4.01e+04 0 \n",
|
||||
" 1 2.04e+05 2.50e+04 2.21e-03 2.54e+04 5.65e+03 0 \n",
|
||||
" 2 2.04e+05 3.36e+03 4.81e-03 4.34e+03 9.87e+02 0 Skip BFGS \n",
|
||||
" 3 2.55e+04 1.70e+03 5.43e-03 1.84e+03 2.74e+02 0 Skip BFGS \n",
|
||||
" 4 2.55e+04 8.94e+02 2.07e-02 1.42e+03 2.45e+02 0 \n",
|
||||
" 5 2.55e+04 8.27e+02 1.76e-02 1.28e+03 1.27e+02 0 \n",
|
||||
" 6 3.19e+03 7.66e+02 1.96e-02 8.29e+02 1.52e+02 0 \n",
|
||||
" 7 3.19e+03 4.82e+02 7.40e-02 7.18e+02 2.52e+02 0 \n",
|
||||
" 8 3.19e+03 4.13e+02 6.65e-02 6.25e+02 1.58e+02 0 \n",
|
||||
" 9 3.98e+02 3.81e+02 7.32e-02 4.11e+02 1.43e+02 0 \n",
|
||||
" 10 3.98e+02 2.69e+02 1.28e-01 3.20e+02 1.49e+02 1 \n",
|
||||
" 11 3.98e+02 2.01e+02 2.10e-01 2.84e+02 1.84e+02 0 \n",
|
||||
" 12 4.98e+01 1.66e+02 2.25e-01 1.78e+02 1.25e+02 0 \n",
|
||||
" 13 4.98e+01 1.28e+02 3.65e-01 1.46e+02 9.89e+01 0 \n",
|
||||
" 14 4.98e+01 1.05e+02 4.50e-01 1.27e+02 1.64e+02 1 \n",
|
||||
" 15 6.23e+00 6.86e+01 5.39e-01 7.19e+01 1.64e+02 0 Skip BFGS \n",
|
||||
" 16 6.23e+00 4.97e+01 5.96e-01 5.34e+01 1.16e+01 0 Skip BFGS \n",
|
||||
" 17 6.23e+00 4.83e+01 6.74e-01 5.25e+01 1.77e+01 0 Skip BFGS \n",
|
||||
" 18 7.78e-01 4.76e+01 6.83e-01 4.81e+01 8.50e+00 0 \n",
|
||||
" 19 7.78e-01 4.67e+01 7.68e-01 4.73e+01 6.91e+00 0 Skip BFGS \n",
|
||||
" 20 7.78e-01 4.65e+01 7.23e-01 4.71e+01 3.93e+01 0 \n",
|
||||
" 21 9.73e-02 4.58e+01 8.12e-01 4.59e+01 5.65e+01 2 \n",
|
||||
" 22 9.73e-02 4.55e+01 7.62e-01 4.55e+01 3.81e+01 0 \n",
|
||||
" 23 9.73e-02 4.52e+01 6.89e-01 4.53e+01 3.60e+01 2 \n",
|
||||
" 24 1.22e-02 4.48e+01 7.67e-01 4.48e+01 3.68e+01 1 \n",
|
||||
" 25 1.22e-02 4.46e+01 7.74e-01 4.46e+01 3.70e+01 3 \n",
|
||||
" 26 1.22e-02 4.45e+01 7.45e-01 4.45e+01 4.29e+01 2 \n",
|
||||
" 27 1.52e-03 4.43e+01 7.79e-01 4.43e+01 4.13e+01 2 \n",
|
||||
" 28 1.52e-03 4.39e+01 7.69e-01 4.39e+01 4.83e+01 2 \n",
|
||||
" 29 1.52e-03 4.38e+01 7.82e-01 4.38e+01 6.21e+01 0 Skip BFGS \n",
|
||||
" 30 1.90e-04 4.36e+01 7.74e-01 4.36e+01 5.22e+01 1 \n",
|
||||
" 31 1.90e-04 4.36e+01 7.93e-01 4.36e+01 5.35e+01 2 \n",
|
||||
" 32 1.90e-04 4.31e+01 7.77e-01 4.31e+01 5.43e+01 1 \n",
|
||||
" 33 2.38e-05 4.11e+01 8.44e-01 4.11e+01 2.16e+01 0 \n",
|
||||
" 34 2.38e-05 4.05e+01 9.56e-01 4.05e+01 2.25e+01 0 \n",
|
||||
" 35 2.38e-05 4.03e+01 9.07e-01 4.03e+01 1.80e+01 1 \n",
|
||||
" 36 2.97e-06 4.03e+01 8.80e-01 4.03e+01 2.09e+01 3 \n",
|
||||
" 37 2.97e-06 4.03e+01 8.13e-01 4.03e+01 2.11e+01 2 Skip BFGS \n",
|
||||
" 38 2.97e-06 4.01e+01 8.53e-01 4.01e+01 2.04e+01 2 \n",
|
||||
" 39 3.71e-07 4.00e+01 8.76e-01 4.00e+01 1.56e+01 0 \n",
|
||||
" 40 3.71e-07 3.99e+01 9.15e-01 3.99e+01 1.25e+01 0 \n",
|
||||
" 41 3.71e-07 3.94e+01 9.05e-01 3.94e+01 1.02e+01 1 \n",
|
||||
" 42 4.64e-08 3.92e+01 9.48e-01 3.92e+01 8.52e+00 0 Skip BFGS \n",
|
||||
" 43 4.64e-08 3.91e+01 8.92e-01 3.91e+01 4.37e+00 0 \n",
|
||||
" 44 4.64e-08 3.89e+01 9.26e-01 3.89e+01 1.63e+01 1 \n",
|
||||
" 45 5.80e-09 3.88e+01 8.77e-01 3.88e+01 1.21e+01 0 \n",
|
||||
" 46 5.80e-09 3.88e+01 9.08e-01 3.88e+01 1.54e+01 0 \n",
|
||||
" 47 5.80e-09 3.83e+01 7.94e-01 3.83e+01 1.77e+01 0 \n",
|
||||
" 48 7.25e-10 3.81e+01 8.11e-01 3.81e+01 1.68e+01 1 \n",
|
||||
" 49 7.25e-10 3.81e+01 7.94e-01 3.81e+01 1.47e+01 0 \n",
|
||||
" 50 7.25e-10 3.80e+01 8.16e-01 3.80e+01 1.90e+01 3 Skip BFGS \n",
|
||||
"------------------------- STOP! -------------------------\n",
|
||||
"1 : |fc-fOld| = 1.5226e-01 <= tolF*(1+|f0|) = 2.1833e+04\n",
|
||||
"1 : |xc-x_last| = 1.1100e+00 <= tolX*(1+|x0|) = 5.1104e+00\n",
|
||||
"0 : |proj(x-g)-x| = 1.8975e+01 <= tolG = 1.0000e-01\n",
|
||||
"0 : |proj(x-g)-x| = 1.8975e+01 <= 1e3*eps = 1.0000e-02\n",
|
||||
"1 : maxIter = 50 <= iter = 50\n",
|
||||
"------------------------- DONE! -------------------------\n",
|
||||
"1 loops, best of 3: 20min 52s per loop\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%timeit\n",
|
||||
"%%timeit\n",
|
||||
"# Run the inversion, given the background model as a start.\n",
|
||||
"mopt = inv.run(m_0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAn0AAAIBCAYAAAA8kwl3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7f6941f051d0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
"ename": "NameError",
|
||||
"evalue": "name 'mopt' is not defined",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-7-915cfd00243c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mu'matplotlib inline'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msimpegmt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mUtils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataUtils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplotMT1DModelData\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mproblem\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mm_0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmopt\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msuptitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Target - smooth true'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mNameError\u001b[0m: name 'mopt' is not defined"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
@@ -244,7 +470,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -256,80 +482,19 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
|
||||
" ***Done using same solver as the problem***\n",
|
||||
"SimPEG.SaveModelEveryIteration will save your models as: '###-Inversion_TargMisEqnD_smoothTrue_Wxx.npy'\n",
|
||||
"============================ Inexact Gauss Newton ============================\n",
|
||||
" # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n",
|
||||
"-----------------------------------------------------------------------------\n",
|
||||
" 0 2.00e+05 2.18e+05 0.00e+00 2.18e+05 4.01e+04 0 \n",
|
||||
" 1 2.00e+05 2.52e+04 3.34e-05 2.52e+04 6.20e+03 0 \n",
|
||||
" 2 2.00e+05 3.46e+03 1.33e-04 3.49e+03 1.03e+03 0 Skip BFGS \n",
|
||||
" 3 2.49e+04 1.72e+03 8.63e-04 1.74e+03 2.61e+02 0 Skip BFGS \n",
|
||||
" 4 2.49e+04 1.02e+03 1.30e-02 1.35e+03 2.51e+02 0 Skip BFGS \n",
|
||||
" 5 2.49e+04 6.65e+02 1.68e-02 1.08e+03 1.37e+02 0 \n",
|
||||
" 6 3.12e+03 5.70e+02 1.88e-02 6.28e+02 1.17e+02 0 \n",
|
||||
" 7 3.12e+03 3.62e+02 4.82e-02 5.13e+02 1.38e+02 0 \n",
|
||||
" 8 3.12e+03 2.61e+02 5.68e-02 4.38e+02 1.98e+02 0 \n",
|
||||
" 9 3.90e+02 2.09e+02 6.09e-02 2.33e+02 6.57e+01 0 \n",
|
||||
" 10 3.90e+02 1.77e+02 9.89e-02 2.15e+02 1.73e+02 0 \n",
|
||||
" 11 3.90e+02 1.50e+02 1.15e-01 1.95e+02 1.29e+02 1 \n",
|
||||
" 12 4.87e+01 1.34e+02 1.32e-01 1.41e+02 7.79e+01 0 \n",
|
||||
" 13 4.87e+01 1.16e+02 1.58e-01 1.24e+02 7.38e+01 1 \n",
|
||||
" 14 4.87e+01 9.60e+01 2.56e-01 1.08e+02 1.22e+02 0 \n",
|
||||
" 15 6.09e+00 9.18e+01 2.69e-01 9.34e+01 8.58e+01 0 \n",
|
||||
" 16 6.09e+00 6.21e+01 3.72e-01 6.44e+01 6.72e+01 0 \n",
|
||||
"------------------------- STOP! -------------------------\n",
|
||||
"1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 2.1833e+04\n",
|
||||
"1 : |xc-x_last| = 5.7707e-01 <= tolX*(1+|x0|) = 5.1104e+00\n",
|
||||
"0 : |proj(x-g)-x| = 6.7181e+01 <= tolG = 1.0000e-01\n",
|
||||
"0 : |proj(x-g)-x| = 6.7181e+01 <= 1e3*eps = 1.0000e-02\n",
|
||||
"0 : maxIter = 50 <= iter = 17\n",
|
||||
"------------------------- DONE! -------------------------\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ -4.14711455, -8.48909373, -7.52827336, -3.49932909,\n",
|
||||
" -1.00331819, -2.70007482, -3.1711545 , -3.00405765,\n",
|
||||
" -2.7650382 , -2.66048875, -2.66349897, -2.71130228,\n",
|
||||
" -2.77717345, -2.7947959 , -2.66720877, -2.36129372,\n",
|
||||
" -1.99640404, -1.65920128, -1.57027047, -1.94765493,\n",
|
||||
" -2.75336364, -3.7261836 , -4.91018688, -6.25134639,\n",
|
||||
" -7.7042116 , -9.04768822, -10.22631443, -11.38316489,\n",
|
||||
" -12.47719857, -13.45601604, -14.16107916, -14.62352295,\n",
|
||||
" -14.8899775 , -14.90719522, -14.61381454, -14.01919889,\n",
|
||||
" -13.24084833, -12.17168104, -10.79110293, -9.09917198,\n",
|
||||
" -7.36420672, -5.75429753, -4.14109747, -2.68084574,\n",
|
||||
" -1.58271625, -1.1145424 , -1.14047311, -1.70470304,\n",
|
||||
" -2.80682095, -4.11322574, -5.17550422, -5.9471688 ,\n",
|
||||
" -6.43756507, -6.50919184, -6.12669826, -5.55246011,\n",
|
||||
" -5.10992856, -4.95756043, -5.27203799, -6.08576941,\n",
|
||||
" -7.12571201, -8.10996496, -9.02794122, -9.72407268, -10.05187723])"
|
||||
]
|
||||
},
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%timeit\n",
|
||||
"moptWxx = inv.run(m_0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
@@ -340,22 +505,11 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAn0AAAIBCAYAAAA8kwl3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7f69426eacd0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%matplotlib inline\n",
|
||||
"fig = simpegmt.Utils.dataUtils.plotMT1DModelData(problem,[m_0,moptWxx])\n",
|
||||
@@ -363,6 +517,17 @@
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"1/np.exp(moptWxx)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
@@ -378,18 +543,6 @@
|
||||
"display_name": "Python 2",
|
||||
"language": "python",
|
||||
"name": "python2"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 2
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython2",
|
||||
"version": "2.7.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@@ -156,6 +156,7 @@
|
||||
"beta = simpeg.Directives.BetaSchedule()\n",
|
||||
"betaest = simpeg.Directives.BetaEstimate_ByEig(beta0_ratio=0.75)\n",
|
||||
"targmis = simpeg.Directives.TargetMisfit()\n",
|
||||
"targmis.target = 1/2 * survey.nD\n",
|
||||
"saveModel = simpeg.Directives.SaveModelEveryIteration()\n",
|
||||
"saveModel.fileName = 'Inversion_TargMisEqnDregMesh_smoothFalse'\n",
|
||||
"# Create an inversion object\n",
|
||||
@@ -174,6 +175,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Mon, 06 Jul 2015 15:56:58 +0000\n",
|
||||
"SimPEG.InvProblem will set Regularization.mref to m0.\n",
|
||||
"SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
|
||||
" ***Done using same solver as the problem***\n",
|
||||
@@ -182,36 +184,52 @@
|
||||
"============================ Inexact Gauss Newton ============================\n",
|
||||
" # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n",
|
||||
"-----------------------------------------------------------------------------\n",
|
||||
" 0 1.40e+05 2.18e+05 0.00e+00 2.18e+05 4.01e+04 0 \n",
|
||||
" 1 1.40e+05 2.50e+04 2.22e-03 2.53e+04 5.66e+03 0 \n",
|
||||
" 2 1.40e+05 3.35e+03 4.89e-03 4.04e+03 9.84e+02 0 Skip BFGS \n",
|
||||
" 3 1.75e+04 1.58e+03 6.55e-03 1.69e+03 2.60e+02 0 Skip BFGS \n",
|
||||
" 4 1.75e+04 7.68e+02 2.89e-02 1.28e+03 2.85e+02 0 \n",
|
||||
" 5 1.75e+04 7.29e+02 2.02e-02 1.08e+03 1.38e+02 0 \n",
|
||||
" 6 2.19e+03 6.63e+02 2.30e-02 7.13e+02 1.47e+02 0 \n",
|
||||
" 7 2.19e+03 4.87e+02 7.57e-02 6.52e+02 2.24e+02 0 \n",
|
||||
" 8 2.19e+03 4.93e+02 7.29e-02 6.52e+02 2.84e+02 0 \n",
|
||||
" 9 2.74e+02 4.39e+02 6.85e-02 4.57e+02 2.08e+02 1 \n",
|
||||
" 10 2.74e+02 2.65e+02 3.60e-01 3.63e+02 3.83e+02 0 \n",
|
||||
" 11 2.74e+02 1.77e+02 4.10e-01 2.89e+02 2.70e+02 1 \n",
|
||||
" 12 3.42e+01 1.33e+02 4.04e-01 1.47e+02 8.75e+01 0 \n",
|
||||
" 13 3.42e+01 1.13e+02 4.75e-01 1.30e+02 1.15e+02 2 \n",
|
||||
" 14 3.42e+01 1.00e+02 5.68e-01 1.20e+02 4.64e+01 0 \n",
|
||||
" 15 4.27e+00 9.00e+01 6.16e-01 9.27e+01 1.41e+02 1 \n",
|
||||
" 16 4.27e+00 8.35e+01 8.42e-01 8.71e+01 1.65e+02 1 Skip BFGS \n",
|
||||
" 17 4.27e+00 7.62e+01 7.55e-01 7.94e+01 1.48e+02 2 \n",
|
||||
" 0 2.79e+05 2.18e+05 0.00e+00 2.18e+05 4.01e+04 0 \n",
|
||||
" 1 2.79e+05 2.50e+04 2.20e-03 2.56e+04 5.64e+03 0 \n",
|
||||
" 2 2.79e+05 3.37e+03 4.71e-03 4.68e+03 9.94e+02 0 Skip BFGS \n",
|
||||
" 3 3.48e+04 1.81e+03 4.72e-03 1.97e+03 2.88e+02 0 Skip BFGS \n",
|
||||
" 4 3.48e+04 1.03e+03 1.53e-02 1.57e+03 2.02e+02 0 Skip BFGS \n",
|
||||
" 5 3.48e+04 8.04e+02 1.70e-02 1.40e+03 1.02e+02 0 \n",
|
||||
" 6 4.35e+03 6.75e+02 1.98e-02 7.61e+02 1.50e+02 0 Skip BFGS \n",
|
||||
" 7 4.35e+03 3.41e+02 5.86e-02 5.96e+02 2.60e+02 0 \n",
|
||||
" 8 4.35e+03 4.04e+02 3.73e-02 5.67e+02 1.27e+02 0 \n",
|
||||
" 9 5.44e+02 3.85e+02 3.62e-02 4.05e+02 1.25e+02 1 \n",
|
||||
" 10 5.44e+02 2.92e+02 1.37e-01 3.66e+02 3.80e+02 0 \n",
|
||||
" 11 5.44e+02 2.08e+02 1.17e-01 2.71e+02 2.34e+02 1 \n",
|
||||
" 12 6.80e+01 8.59e+01 1.52e-01 9.63e+01 3.85e+01 0 Skip BFGS \n",
|
||||
" 13 6.80e+01 7.54e+01 1.76e-01 8.74e+01 7.22e+01 1 \n",
|
||||
" 14 6.80e+01 6.53e+01 2.09e-01 7.95e+01 4.35e+01 0 \n",
|
||||
" 15 8.50e+00 6.09e+01 2.13e-01 6.27e+01 4.35e+01 1 \n",
|
||||
" 16 8.50e+00 5.11e+01 3.14e-01 5.38e+01 6.93e+01 0 Skip BFGS \n",
|
||||
" 17 8.50e+00 4.66e+01 3.24e-01 4.93e+01 1.25e+01 0 \n",
|
||||
" 18 1.06e+00 4.55e+01 3.34e-01 4.59e+01 1.58e+01 0 \n",
|
||||
" 19 1.06e+00 4.26e+01 4.74e-01 4.31e+01 1.55e+01 0 \n",
|
||||
" 20 1.06e+00 4.25e+01 4.64e-01 4.30e+01 1.74e+01 0 \n",
|
||||
" 21 1.33e-01 4.22e+01 5.17e-01 4.22e+01 2.33e+01 2 \n",
|
||||
" 22 1.33e-01 4.20e+01 4.96e-01 4.21e+01 2.25e+01 1 \n",
|
||||
" 23 1.33e-01 4.18e+01 5.13e-01 4.19e+01 3.00e+01 0 \n",
|
||||
" 24 1.66e-02 4.13e+01 5.34e-01 4.13e+01 3.75e+01 1 Skip BFGS \n",
|
||||
" 25 1.66e-02 4.13e+01 4.91e-01 4.13e+01 2.33e+01 0 \n",
|
||||
" 26 1.66e-02 4.04e+01 3.75e-01 4.04e+01 2.85e+01 2 \n",
|
||||
" 27 2.08e-03 4.03e+01 3.92e-01 4.03e+01 2.55e+01 0 \n",
|
||||
" 28 2.08e-03 3.97e+01 3.50e-01 3.97e+01 3.04e+01 3 \n",
|
||||
" 29 2.08e-03 3.91e+01 3.46e-01 3.91e+01 3.72e+01 2 \n",
|
||||
" 30 2.59e-04 3.86e+01 4.32e-01 3.86e+01 3.71e+01 0 Skip BFGS \n",
|
||||
"------------------------- STOP! -------------------------\n",
|
||||
"1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 2.1833e+04\n",
|
||||
"1 : |xc-x_last| = 4.2045e+00 <= tolX*(1+|x0|) = 5.1104e+00\n",
|
||||
"0 : |proj(x-g)-x| = 1.4824e+02 <= tolG = 1.0000e-01\n",
|
||||
"0 : |proj(x-g)-x| = 1.4824e+02 <= 1e3*eps = 1.0000e-02\n",
|
||||
"0 : maxIter = 30 <= iter = 18\n",
|
||||
"------------------------- DONE! -------------------------\n"
|
||||
"1 : |fc-fOld| = 4.3953e-01 <= tolF*(1+|f0|) = 2.1833e+04\n",
|
||||
"1 : |xc-x_last| = 1.9611e+00 <= tolX*(1+|x0|) = 5.1104e+00\n",
|
||||
"0 : |proj(x-g)-x| = 3.7072e+01 <= tolG = 1.0000e-01\n",
|
||||
"0 : |proj(x-g)-x| = 3.7072e+01 <= 1e3*eps = 1.0000e-02\n",
|
||||
"1 : maxIter = 30 <= iter = 30\n",
|
||||
"------------------------- DONE! -------------------------\n",
|
||||
"CPU times: user 11min 29s, sys: 960 ms, total: 11min 30s\n",
|
||||
"Wall time: 11min 30s\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Run the inversion, given the background model as a start.\n",
|
||||
"%%time\n",
|
||||
"simpegmt.Utils.dataUtils.printTime()\n",
|
||||
"mopt = inv.run(m_0)"
|
||||
]
|
||||
},
|
||||
@@ -244,9 +262,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAn0AAAIBCAYAAAA8kwl3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAn0AAAIBCAYAAAA8kwl3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl8VNX9+P/XyUYCYQk7AhKQTdkVQRYlLfBRqeBaqfgp\nTf1YF0qpVtv+vrUfT04X+6m1tqJVW4ugVBSruO8CgcgiICAFEQIkIPsmS0BClvfvjzsJWSbJJJnM\nTJL38/GYBzP3njn3PSE39z3nnsWICEoppZRSqmGLCncASimllFKq7mnSp5RSSinVCGjSp5RSSinV\nCGjSp5RSSinVCGjSp5RSSinVCGjSp5RSSinVCGjSp5RSSinVCGjSp5RSSinVCGjSp1QDY4xJMsb8\nwRjzM2NME2PM08aY/xhjZhtjWoc7PqWUUuGhSZ9SDc9sIA7oAywCjgPfA7KAmWGMSymlVBgZXYZN\nqYbFGLNBRAYaY6KA/UBHESn07ftcRAaFN0KllFLhoC19SjU8hQC+RG9NUcKnlFKqcYsJdwBKNWbG\nmKY1fOs3UnEz/XFjTHMROSkiE0ocqwOQW8PjKaWUqiXnXByAtfZsOI6vt3eVCiNjTE1a4QS4VETW\nVvNYrYAkEcmqwTGVUkrVkHMuHrgcuA84Acy31r4a6jg06VMqjHxJ3++AHQG+JQr4JzC0ukmfUkqp\n0HPOJQG3AlcCC4BMYBYwyVq7JZSx6O1dpcLvbRFZFUhBY0wMXtJXbcaY5sAYvFG9Sb7NXwNfAktE\nJKcm9SqllPLPdzt3CjAIeNham+HbvhsI+RRamvQpFV49gL2BFhaRfGNMD2BPoO/xjeJ1wM+ABOA0\nXrIHXvLXFDhtjHkUsJX0FVRKKVU9o4CJwEPW2gznXDRwPd7f/TWhDkZH7yoVRiKSLSLV6tDre09e\nNd5igXuBNCBZRBJFpKuIdAW6AV/gzeX3a7xBIOuMMWt9rYpBY4xpaYy5u5Z1TDTG/LKKMi8aYz43\nxtxjjHHGmLG+7fcYYxJqc/xgMMaMMcaMKPF6jjHmxgDeV+D7vyl6nF9JWW21VSrMnHMxwJ3AAmvt\nUt/r0cBwvISv0DkX0jxMW/qUikC+hCuu7HYROV2D6m4H7hORv/up7wjQ33fMN4ExIjIkgPiiRaSg\nmnEkAdOAp6r5vmIi8hbwViVxdcTr79jLz+6fAnOBb2p6/CD5FnASWOF7HWjL6ulA/m+qWadSqu4I\ncAYo+mI/GRjsez3HWlvqb6hzrrW19mhdBqQtfUpFCGNMK2PMU8aY/XhTq+SUeZysYdWtgG0BlDsK\nJBhjbjfGrDLGrDfGvFLUOuZrkXraGLMS+KMx5gJjzEpjzAZjzO+MMcXxGWN+7qvjc2NMmm/z/wEX\n+Fqp/ljmsycbY770LRW3xRjzgjHmv4wxy4wxW40xl/rKpRpjHvc9/65vebn1xph0X1UfAp19xxhd\n1IpmjPkJcB6w2BizsOwHN8b0M8Z86nvf577PFmhMrY0xr/vet8IYM6Ci7caYZLxv/vf6WlNH+0K4\nwlfv9kBa/Xz1NzPGfGyM+cz3fzDJT5lOxpilvs/1n6Lj+T7Hct97XzbGNAvkmEqpwPmSupnAz51z\n6cB38Abt/clae9zX8odz7jbn3F+AN5xzV9ZlTPV29K4xpn4GrhocETHBqMcYswBIAZ4BtnPu22HJ\nY82pQb0LgQLghooGaxhjEoH1eN9Mh4vIUd/23wIHROQJY8wcvI7H1+KbAFoppVTl1wHnXEegJZBt\nrS03V6pz7vfAIWAX8Efgf6y1S+siznrd0iciFT6stZVu8/fc379FDz2WHsvfsYJsLDBdRH4pIv8Q\nkTllHzWs9yd4t3B3GmPmGWMeNMbM8D3+1xgzD9gJtMebTmCAMSbDGLMBb5qBi3z1CPBv8X1TbNOm\nDQ8++CAiwvHjx4mLi0NEuO+++2jVqhWDBw9m8ODB9OrVi0mTJpGVlUX//v39/h9kZWXRunXr4tdT\np05l3rx5iAjbt2+nY8eOiAizZ89m+vTpiAh33XUX48eP55lnnuHIkSOICD/96U+LjyEipKam8uqr\nryIiJCcnF5cr+5g3bx79+vVj3LhxZGZmYq0lKyuLXr16Fcc6depUbrjhhuKYBg8ejLWWIUOGkJWV\nVfy5unbtyokTJ4q3F72/aHtaWhqPPPJIcfnU1NTizyoiNG/e3O/vZ2JiYqltZ8+e5cc//jEDBw5k\n8ODBNG3alAMHDiAixWWXLl1Kz549GTNmDOvXr8day1tvvUXbtm3p2LEjgwcP5qKLLmLIkCGVniNV\nnTPV2VeTclW9v6rzVz+Xfq66/FxVsdbu903Ncp1zrrg/r3NuhnPuZ8A4YLm1dgHe7AwDavi3vkr1\nOumrTEpKSqXb/D3396+/evRYeqxAj1VNe/BG1gaViHwB9AMeAboAP/Y9fwSYDnQG/gT8DTgIzAam\nichAvFG/JQc/lIpvzJgxxc+jo6OLn992222sW7eOdevWsXXrVu69995SMfn72bVs2bL4eVRUFHFx\nccXPExLKj7946qmn+N3vfsdXX33FJZdcwtGjR7nssssq/2H4vP766wwZMoQhQ4awdu1abrnlFt56\n6y1iYmKYMGFCcSxNmjQpjjUqKoqBAwcWP8/Pzy/eV/SHv+znEpFKf0+K9hV91pJ1+StX0gsvvMDh\nw4dZu3Yt69ato3379pw5c6ZUmcsvv5yMjAxatGhBamoqZ896jcfjx4/nxRdfZN26dWzatIlHH320\n0uNVdc5UZ19NylWnnoqeB/K6qpj0cwVWrjr1NKTPFYCl+KZpcc6lARfjdd35CPjIOXcX8AeqMaND\ntdUmuw3nwws9NKy1eiw9ll++38Ng/U5PAtYC3YJVZzWPb/Fmiz8ItANifX+MnvXtnw3c6Hsu3/nO\nd2T+/PkiIvL3v/9dEhMTRUTkww8/lOHDh0tOTo6IiOzevVsOHjwohw8flm7duvn9OWZlZUn//v2L\nX6empsorr7xSbt/s2bNl+vTpIiKybdu24vKXXnqpfP75537refXVV0VEZMCAAZKVleX3+Dt27BAR\n73fn/vvvl8cee0yys7MDimnGjBny29/+VkREFi9eLBdffHGl2//85z+X+h0tWa+IFP8cyyq7/bHH\nHpOf/OQnIiKyaNEiMcbIzp07S5XduXOn5Ofni7VWnnjiCbn33nvl0KFDcv755xf//HJycmTr1q1+\njxnpQnmuh5J+rvqlJteBtLS0P6elpU1KS0uL9b2ek5aWdktaWtp3qltXdR46ejcAIfwWoMeqZ8cK\nJhF50xhzNbDNGJMFHAMM3m1V4xWRYXUYQgzeoI8HgU/x+ph8CiSWDLPoSY8ePUhLS+Ohhx7iyiuv\nLG4dGz9+PJs3b2bECO8uRmJiIi+88ALdu3dn1KhRDBgwgAkTJvDHP5Yay4ExpsLXRc+NMcXPf/GL\nX5CZmYmIMG7cOAYOHEh2dna5eorccccdXHXVVXTu3JmFC0uP5Xj55ZeZO3cuubm59OrViwceeIBj\nx44FFFNaWhq33XYbgwYNolmzZjz33HOVbp84cSI33XQTb775JjNnzqyw3rLKbr/11luZOHEiAwcO\nZOjQoVx44YXlyi5evJhHHnmE3NxcOnXqxPPPP0/btm2ZM2cOt9xyC7m5Xvei3//+9/Tq5W/Ac2Sr\nr+d6VfRzNVzOOYN396QPsN1am+ec6wd8G3jcWvtZXR6/Xg/kqK+xq4bDGIMEbyDHn/Hm01uN/4Ec\nIiI/DMaxKjj+TcB8EYkOoKycPn26+LbrSy+9xPz583nttdfqKjyllIpINbkOOOcuAl4D3gBuBP5u\nrX24LuIrSZM+pWohyEnfMeBhEXkoGPXV4Pg3AS+LSJV9fY0xkpGRUTyoIikpiWeffZYePXqEIFKl\nlKq+06dPk5WVRW5ubvHjzJkzNG/enFGjRpUrv2/fPt59911iY2OJjY0lJiaG2NhY2rdvz8iRI4vL\n1fQ64Jzridev75S19p3afLZAadKnVC0EOenbB/xARD4MRn0l6l1MYJP1tgcuDLSlT88/pVR9cvjw\nYRYtWkSTJk2KH/Hx8SQlJdG3b99y5XNzczl48CB5eXmlHk2bNqVPnz7F5YJ5HahrmvQpVQtBTvr+\nP2Ao8N1g/nIbYwqALXjLrVWmMzBMkz6llApcfUr6dCCHUpGjDd6ajFt8K0wcK1tARH5Rg3o3AZtF\nZHJlhYpu7wZaaVpaWl1NXaOUUtUm4s2huWLFCkaNGqXdTfzQlj6laiHILX3ZlBipW3Y33kCO7jWo\n9+/A1SJyfhXlqtWnT88/pVRdOH36NMYY4uPjKxzNXlJBQQEbN25k+fLlAIwcOZL+/fuXmju0LtWn\nlj5N+pSqhfpwshtjeuKtqvFWZSeN8dbY7SAi2QHUqeefUirocnJyePLJJxERcnNziY+PJyEhgQ4d\nOnDzzTeXK79//37mzZtHu3btGDFiBBdccEFAiWIw1YfrQBFN+pSqhfp0sgeTnn9KqbqSn59PTEwM\nhYWFnDlzhtOnT5Ofn0/Hjh3Llc3Ly+Pw4cN06tQpDJF66tN1QJM+pWqhPp3swaTnn1JKeerTdaDB\nrr2rlFJKqchVUFAQ7hAaHU36lFI1kpaWRnp6erjDUErVQ3v27OHJJ59k//794Q6lUdHbu0rVQn1q\n1g8mY4z8YMwYAFolJ/PXOXPCG5BSql4oLCxk+fLlrFixggkTJtCvX79wh1Rr9ek6oPP0KaVqpPuS\nJQBkhTkOpVT9cOLECV577TVEhDvuuIOWLVuGO6SQc87FAVhry66tHhKa9CkVQYwxw4AbgPOA+JK7\n8ObpKz9nQZjlnzmDFBZios71FrknNZVj2dnlypZtFQy0nFKqfhMRXn75ZXr37s3o0Line truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7ff3ef333810>"
|
||||
"<matplotlib.figure.Figure at 0x7fab14b26910>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -283,38 +301,59 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Mon, 06 Jul 2015 16:08:31 +0000\n",
|
||||
"SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
|
||||
" ***Done using same solver as the problem***\n",
|
||||
"SimPEG.SaveModelEveryIteration will save your models as: '###-Inversion_TargMisEqnDregMesh_smoothFalseWxx.npy'\n",
|
||||
"============================ Inexact Gauss Newton ============================\n",
|
||||
" # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n",
|
||||
"-----------------------------------------------------------------------------\n",
|
||||
" 0 2.39e+05 2.18e+05 0.00e+00 2.18e+05 4.01e+04 0 \n",
|
||||
" 1 2.39e+05 2.50e+04 2.21e-03 2.55e+04 5.64e+03 0 \n",
|
||||
" 2 2.39e+05 3.36e+03 4.76e-03 4.50e+03 9.90e+02 0 Skip BFGS \n",
|
||||
" 3 2.99e+04 1.75e+03 5.04e-03 1.91e+03 2.80e+02 0 Skip BFGS \n",
|
||||
" 4 2.99e+04 9.62e+02 1.78e-02 1.49e+03 2.24e+02 0 Skip BFGS \n",
|
||||
" 5 2.99e+04 7.14e+02 1.93e-02 1.29e+03 1.09e+02 0 \n",
|
||||
" 6 3.74e+03 5.87e+02 2.24e-02 6.71e+02 1.34e+02 0 Skip BFGS \n",
|
||||
" 7 3.74e+03 3.16e+02 5.95e-02 5.38e+02 2.60e+02 0 \n",
|
||||
" 8 3.74e+03 3.28e+02 4.17e-02 4.84e+02 1.11e+02 0 \n",
|
||||
" 9 4.67e+02 2.99e+02 4.41e-02 3.19e+02 1.06e+02 1 \n",
|
||||
" 10 4.67e+02 2.23e+02 1.46e-01 2.91e+02 3.71e+02 0 \n",
|
||||
" 11 4.67e+02 2.34e+02 1.10e-01 2.85e+02 1.88e+02 0 \n",
|
||||
" 12 5.84e+01 1.71e+02 1.24e-01 1.78e+02 1.36e+02 0 \n",
|
||||
" 13 5.84e+01 7.84e+01 1.68e-01 8.82e+01 8.21e+01 0 \n",
|
||||
" 14 5.84e+01 6.37e+01 2.01e-01 7.54e+01 6.83e+01 1 \n",
|
||||
" 0 2.41e+05 2.18e+05 0.00e+00 2.18e+05 4.01e+04 0 Skip BFGS \n",
|
||||
" 1 2.41e+05 2.50e+04 2.21e-03 2.55e+04 5.64e+03 0 Skip BFGS \n",
|
||||
" 2 2.41e+05 3.36e+03 4.76e-03 4.51e+03 9.90e+02 0 Skip BFGS \n",
|
||||
" 3 3.01e+04 1.76e+03 5.03e-03 1.91e+03 2.81e+02 0 Skip BFGS \n",
|
||||
" 4 3.01e+04 9.65e+02 1.77e-02 1.50e+03 2.23e+02 0 Skip BFGS \n",
|
||||
" 5 3.01e+04 7.17e+02 1.92e-02 1.29e+03 1.08e+02 0 \n",
|
||||
" 6 3.76e+03 5.91e+02 2.23e-02 6.75e+02 1.34e+02 0 Skip BFGS \n",
|
||||
" 7 3.76e+03 3.17e+02 5.95e-02 5.41e+02 2.60e+02 0 \n",
|
||||
" 8 3.76e+03 3.31e+02 4.15e-02 4.87e+02 1.12e+02 0 \n",
|
||||
" 9 4.70e+02 3.02e+02 4.38e-02 3.22e+02 1.07e+02 1 \n",
|
||||
" 10 4.70e+02 2.25e+02 1.46e-01 2.94e+02 3.72e+02 0 \n",
|
||||
" 11 4.70e+02 1.55e+02 1.23e-01 2.13e+02 2.31e+02 1 \n",
|
||||
" 12 5.88e+01 8.26e+01 1.35e-01 9.05e+01 5.12e+01 0 Skip BFGS \n",
|
||||
" 13 5.88e+01 7.00e+01 1.74e-01 8.02e+01 7.64e+01 0 Skip BFGS \n",
|
||||
" 14 5.88e+01 6.72e+01 1.77e-01 7.76e+01 3.88e+01 0 \n",
|
||||
" 15 7.35e+00 6.55e+01 1.88e-01 6.69e+01 3.28e+01 0 \n",
|
||||
" 16 7.35e+00 5.88e+01 2.19e-01 6.04e+01 4.50e+01 1 \n",
|
||||
" 17 7.35e+00 5.59e+01 2.43e-01 5.77e+01 4.95e+01 2 Skip BFGS \n",
|
||||
" 18 9.19e-01 5.33e+01 3.02e-01 5.36e+01 7.11e+01 0 Skip BFGS \n",
|
||||
" 19 9.19e-01 4.81e+01 3.05e-01 4.83e+01 1.18e+01 0 \n",
|
||||
" 20 9.19e-01 4.77e+01 3.34e-01 4.80e+01 1.56e+01 0 \n",
|
||||
" 21 1.15e-01 4.76e+01 3.47e-01 4.76e+01 1.33e+01 0 \n",
|
||||
" 22 1.15e-01 4.74e+01 3.49e-01 4.74e+01 9.70e+00 0 \n",
|
||||
" 23 1.15e-01 4.72e+01 3.60e-01 4.73e+01 1.33e+01 2 Skip BFGS \n",
|
||||
" 24 1.44e-02 4.66e+01 3.81e-01 4.66e+01 2.08e+01 0 \n",
|
||||
" 25 1.44e-02 4.65e+01 3.70e-01 4.65e+01 1.70e+01 0 \n",
|
||||
" 26 1.44e-02 4.52e+01 2.77e-01 4.52e+01 3.75e+01 2 Skip BFGS \n",
|
||||
" 27 1.79e-03 4.45e+01 2.81e-01 4.45e+01 1.81e+01 0 \n",
|
||||
" 28 1.79e-03 4.43e+01 2.78e-01 4.43e+01 1.40e+01 1 Skip BFGS \n",
|
||||
" 29 1.79e-03 4.42e+01 2.68e-01 4.42e+01 1.68e+01 1 \n",
|
||||
" 30 2.24e-04 4.31e+01 2.77e-01 4.31e+01 2.79e+01 1 Skip BFGS \n",
|
||||
"------------------------- STOP! -------------------------\n",
|
||||
"1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 2.1833e+04\n",
|
||||
"1 : |xc-x_last| = 3.0163e+00 <= tolX*(1+|x0|) = 5.1104e+00\n",
|
||||
"0 : |proj(x-g)-x| = 6.8281e+01 <= tolG = 1.0000e-01\n",
|
||||
"0 : |proj(x-g)-x| = 6.8281e+01 <= 1e3*eps = 1.0000e-02\n",
|
||||
"0 : maxIter = 30 <= iter = 15\n",
|
||||
"------------------------- DONE! -------------------------\n"
|
||||
"1 : |fc-fOld| = 1.1021e+00 <= tolF*(1+|f0|) = 2.1833e+04\n",
|
||||
"1 : |xc-x_last| = 1.0668e+00 <= tolX*(1+|x0|) = 5.1104e+00\n",
|
||||
"0 : |proj(x-g)-x| = 2.7855e+01 <= tolG = 1.0000e-01\n",
|
||||
"0 : |proj(x-g)-x| = 2.7855e+01 <= 1e3*eps = 1.0000e-02\n",
|
||||
"1 : maxIter = 30 <= iter = 30\n",
|
||||
"------------------------- DONE! -------------------------\n",
|
||||
"CPU times: user 11min 31s, sys: 484 ms, total: 11min 31s\n",
|
||||
"Wall time: 11min 31s\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"simpegmt.Utils.dataUtils.printTime()\n",
|
||||
"moptWxx = inv.run(m_0)"
|
||||
]
|
||||
},
|
||||
@@ -327,9 +366,9 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAoEAAAIBCAYAAAA242VgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd4VFX6wPHvSZ0EkBCQJiX0jjQVBCGKCCugoK6Krisq\nYkesq67uyd1V1wZrL2uLq4s/UWyorA0QRBCpIgQQCSC9hFRInff3x52ElEkyyUymJOfzPPOQuXPu\nuWeGTOadU96jRATDMAzDMAyjYQkLdAMMwzAMwzAM/zNBoGEYhmEYRgNkgkDDMAzDMIwGyASBhmEY\nhmEYDZAJAg3DMAzDMBogEwQahmEYhmE0QCYINAzDMAzDaIBMEGgYhmEYhtEAmSDQMOoZpVQzpdQ/\nlVJ3KqWilVIvK6U2KKXeVErFB7p9hmEYRnAwQaBh1D9vAlFAD2AhkAFcDqQCzwawXYZhGA2aZVlR\nlmVFBbodxZTZNs4w6hel1M8i0l8pFQbsB1qLiNP12HoROTWwLTQMw2hYLMtyAGcBdwGZwHta63mB\nbZXpCTSM+sgJ4Ar8VhUHgIZhGIb/WZbVDJgGzADewx6RedSyrB4BbRgQEegGGEZDppSKreWpx6Xy\nbvwMpVQTEckSkfNLXasVkFfL6xmGYRg15Br6vQI4FXhCa73UdXw3EPA52iYINIzAyq7FOQKcBqxx\n+6DIqErOywMuq8X1DMMwjNoZDkwEHtVaL7UsKxyYDOwFVgW0ZZg5gYYRUEopJ/AwsN3DU8KA14Ah\nIuI2CDQMwzACz7KsCOAdYKHW+t+u+8OBCcBu4Hlc03e01gEJxkxPoGEE3mcistKTgkqpCOwgsMaU\nUk2AUdirhpu5Dh8FNgPfiUhteiUNwzAM9wTIBfJd9y8DBrjuJ2uti0oXtizLobXO9WcDTU+gYQSQ\nUioB2Csi+dUULX/OHhEp8LB8GGABdwIxwDHs4A/sYDDWdWw2oKuYa2gYhmHUgGVZg4C3gUPYQ8BL\ngXe11umlypwP9AN6A3O01l/6q30mCDQMP1NKNQe+cd1tDRRh/4EQ4HQRKfThtZoCc7B7AC3gPRHZ\nVa5Me+xvqBqYLSK63OMTgd4i8ngV13kX+w/Ym9iB5RIR+VYpNRN4RUSO++o51YZSahSQLyLLXfeT\ngfkiUmWKBqVUEfBzqUMXln/9SpXNFpHGXrQxDtgmIi1c94cBy4B2IrLX9X+5XUSa1/Yarnpfxf5/\nTqnFuTV6jkqpROAuEZlYg3MWu85ZXdP2GUYwsiyrNdAU2KG1ziv32JNAY+AI9t+a54CJWmuPRoe8\nZVLEGIaficgRERkoIgOBl7E/kAeKyKDiAFApFaGUii13q02A0Qw4D/tD9Ul3AYyI/C4iT2Hnr5rm\n5vH51QSArbHnKJ4qIk+LiBaRb10P347d0xhoZwNnlrrv6bffY8X/V66b2wCwhnW6P1kkHdinlOrl\nOnQm9uKf4a77Q4EfvbmG6zrX1yYALD7d2+t7eA3TO2HUG1rr/VrrLcAky7KGFh+3LOsJoDn258Dj\nWuu5wBvYyf79wgSBhhF4Sik1TSm1Wil1WCmVi72SNxvIKXXLVEp1UUqtUEr9rJR6WCmVVaqSe5RS\nK5VS65VSSa7Dj2HP/b1bKfV4uYsmKKU2u7aT2wJcCjRXSi1TSm1VSp3mKjdVKfWc6+c/uragW+fq\nsQH4CjhFKbVWKTVCKZWslLpYKXUb0BZYpJT6lnKUUn2UUj+6zlvvem5l2qSU+q9S6jw3bYpXSn3s\nOm+5UqpfZcddw+c3AHcopdYopUa4mjDSVe9vSqmLPfyPaqSU+sb1f/WzUuoCN2XaKKWWuJ7XhuLr\nuZ7HD65z5yqlGrm5xA+cCFaHAU+Xun8msEwpFe76fx7lqvefSql/KKVOcr123V3H31VKXeemfYuV\nUoNcP2e7fo/WuV6vlq7jrZRSH7mOr1NKDS1XR6JSan6p+88rpa52/TxOKZWilFqNvQqy9Gv3huv/\nfE3xa6eUilFK/Z9SapNS6kPsKQuquv8LwwhBS7CDPizLOgdogp0zcKPWOsuyrIHYuzspV5k6j9FC\ndjhYKRWaDTfqHRGp9QeWUkpjB3tvYi/4SARSgJ3YwdU0oBHwjOuUS4C3ReQ9pdQNwFMi0kQpdR5w\nsYjcoOw5gJ8ATwC7gI3YwcVFpRd/uIKjX7EnKu8E9gA5ItLW9QF9jYhMVkpNBQaJyAyl1M/AWOy5\nLYZhGAY1/xywLGsm0BF4SGudbVlWH+y/819orWdbltUIeB94Tmu9wPcttoV0T6CIVHrTWld5zN3P\n7v4tvplrmWu5u5YP9QMuwA4I2wDpIpKMHaTNFpFk1/2h2H8YAN4tdf55wHlKqbXAauwVwF2xv1Hu\nBfoCO5VSc5RSf1NKzQCmYu8r/FfsIDASmOWq7xcgoVT9xX/glgFvARw5cgStNampqfTt27fktZk6\ndSqXXnopIkJCQkJJufKv95w5c+jTpw+PP/44X3zxRUld3bp1Kyn35z//mTlz5qC15rfffmPAgAGI\nCAMHDiQ1NbWkXPv27cnMzKR169ZujyclJfHUU0+VHJ86dSpz5swpud+kSRO3/8+NGzcu0/YHH3yQ\nW265hf79+zNgwAAiIyM5cOBAmbJLliyha9euJCUlsW7dOrTWzJ8/nxYtWjBgwAAGDBhA7969mTZt\nWoXX5ddff6Vnz56kpqYyefJkRIThw4dz//33Ex8fT05OTknZRx55BIfDwbp168rUcf3119O8eXP2\n7Nnj9vc4MTGR1atXo7UmOjq65Ph7773HtGnTEBFiY2PJz8+vcH7jxo0RERYtWkT37t1LHr/11ltJ\nTk5m7dq1jBw5sqT8p59+yoQJExARBg8eTN++fUteg44dO3LLLbcwadIkFi1aVFLXoEGDWL16dZXv\n4cren1X97Mn9yo558lhtypnnVT+eV01YlqUsy4oEugO/uwLAwdhzAb8uFQD+D4gG/mFZ1tgaXaQG\nQjoIrEpiYmKVx9z97O5fd/WYa5lreXqtGngTOxCbgb2AI6bUY8c8rOOfcmLuWncRedN1PA/oAzwF\ntANucf38APZk5VOAJ4GPgB2uc5y4SSElIjcBDwIMHjyYQYMGlTxW+vXo06dPmfMSExP5+OOPGThw\nIAMHDqRt27ZMmTKF+fPnExMTw4033khWlj2yHR0dXXJeWFgYUVFRJCYmEhYWRmHhiTUz7v74Fgdz\nnoiKOjHtpvw5lf0+FBQUcPjwYdasWcPatWtp3rw5ubm5ZcqeddZZLF26lFNOOYWpU6eSn28v/B4z\nZgxr165l7dq1bNy4kVdffZVWrVqVvCafffYZXbt2JT09nfnz53PmmfYo8ODBg8nOziYhIYHY2BPT\nKzds2ECzZs04cOBAyTGn00lKSgqNGjUiLS2twnMu/7wiIyNLfi79+kZFRZV5Tcr/rkdERNCsWbOS\n+8WvgVKqTPnyr+uHH35Y8hrs2LGDSy65xG25mqru/evp/cqOefJYbcrVpB7zvIL/eXlCay1a6wLg\nBeBuy7JeBOYC84DnXMmkPwP2aq1HY2d1eNUVKPqeN9FvIG920/1Da22uZa7lluv30JvfY429IOMg\n8CfshQBLgTdcj7+JPcxbXP4z4FLXz9OBLNfPY4AVQCPX/VOAk7Hnn+yo5NoJwIZS90uuVfox7B7D\n51w/d3H9K23btpXXXntNUlNTpW/fviWvydSpU2XevHkiItKvXz9JTU11+9pt37695OfLLrtMbrnl\nFtmxY0eFuj744AMRkTLXmTFjhvzjH/8QEZFFixbJoEGDqjw+a9asMr8XpesVEWncuLGIVPzdKT5e\n7JlnnpHbbrtNREQWLlwoSinZuXNnmbI7d+6UwsJCERF5/vnn5Y477pBDhw5Jhw4dZNu2bSIikp2d\nLVu3bnX7ukyaNEm6dOki33//vYiIvPvuu9K5c2eZMWNGSZl58+bJuHHjZOvWrdK9e3dJT08XEZGn\nnnpKbrjhBlm6dKkMGTJECgoKKjyvxMREWb16dYXn9/7778vUqVNFROTyyy+Xp59+WkRECgsLJSMj\no0z5Xbt2SUJCguTl5cnRo0elU6dO8tZbb0lubq506NBBfvvtt5J6JkyYICIiDzzwgNx6660l11uz\nZo2IiMyePVumTZsmIiIbNmyQiIiIkvZVx5/vdX8yzyu01PZzICkpKSEpKWlgUlLSqaWOhSclJf01\nKSnp+6SkpFNcx/rVpn5PbvW2J9CX/PktwVwrtK7lQ38D/o4dvJ0JXKyUWomdWf4xpdRPrvszgTuV\nUuuALtjDuYjI19ipYJa75u29DzQWkSPYiwk2lF8Y4lK6CyYc16Tlco+VXq35hKt+Lr30Uq67zl53\nUNwDVN706dMZN24co0ePrvDY3Llz6du3LwMHDiQ9PZ0HH3wQEalQV+n7xT8nJSWxevVqTj31VB54\n4AHeeuutKo9PnDiRjz76iEGDBvH9999XWm/5353ybbnyyitZtWoV/fv35+2336ZXr14Vyi5atIgB\nAwYwaNAg5s6dy+23306LFi1ITk5mypQpnHrqqZx55pls2bLF7Ws2fPhwdu/ezZAhQwAYOnQoqamp\nJT2Dhw8f5v777+e1116jW7du3Hrrrdx+++1s3bqV119/nVmzZjFixAhGjhzJww8/7PZ5VfbaFt9/\n5plnWLRoEf3792fIkCGkpKSUKd++fXsuvfRS+vbty2WXXVbSIxwdHc2///1vxo8fz+DBg2nVqlXJ\nOQ899BAFBQX079+/ZPoAwE033UR2dja9e/dGa13yvD0Rou/1apnn1TBorXdorddqrddblnWK67Bo\nrR/BHpm5xbKsSK31BrCHkn3dhpBeGBKqbTfqD6UU4sXCkHJ1zQLuAH4CfuNElvliALine truncated
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAoEAAAIBCAYAAAA242VgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7ff3e63afe50>"
|
||||
"<matplotlib.figure.Figure at 0x7fab1024b7d0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -353,27 +392,28 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 1.56364893e+00, 1.62641903e+00, 9.96327507e-01,\n",
|
||||
" 1.44424654e+00, 6.30578509e-01, 1.23401217e+00,\n",
|
||||
" 3.24085433e-01, 8.22697458e-01, 1.76735724e-01,\n",
|
||||
" 6.03074990e-01, 9.58662944e-02, 4.08215049e-01,\n",
|
||||
" 5.98116441e-02, 2.51972974e-01, 3.54653904e-02,\n",
|
||||
" 1.75450200e-01, 2.56228377e-02, 1.08572300e-01,\n",
|
||||
" 2.02929041e-02, 7.36028696e-02, 1.64620388e-02,\n",
|
||||
" 4.35752349e-02, 1.80228828e-02, 3.00269993e-02,\n",
|
||||
" 1.63486779e-02, 2.07561568e-02, 1.48651221e-02,\n",
|
||||
" 1.54284013e-02, 1.44530319e-02, 1.33959072e-02,\n",
|
||||
" 1.19683391e-02, 9.92609378e-03, 7.65168460e-03,\n",
|
||||
" 1.02732372e-02, 6.00068131e-03, 7.50916027e-03,\n",
|
||||
" 4.18767716e-03, 5.42181442e-03, 3.25257531e-03,\n",
|
||||
" 4.05629002e-03, 2.68596740e-03, 3.42401859e-03,\n",
|
||||
" 1.90915942e-03, 2.59005940e-03, 1.43069528e-03,\n",
|
||||
" 1.94836445e-03, 1.18442707e-03, 1.55686635e-03,\n",
|
||||
" 9.44647001e-04, 1.12579572e-03, 6.95558685e-04,\n",
|
||||
" 8.43305256e-04, 4.93088025e-04, 5.98123170e-04,\n",
|
||||
" 3.83781524e-04, 5.17427666e-04, 3.07601634e-04,\n",
|
||||
" 3.64609780e-04, 2.55739400e-04, 2.83916829e-04,\n",
|
||||
" 2.10645694e-04, 2.29281320e-04])"
|
||||
"array([ 3.62176541e+02, 1.93066605e+03, 5.43119942e+02,\n",
|
||||
" 1.23983980e+02, 9.62568969e+01, 2.26952779e+02,\n",
|
||||
" 4.16015718e+02, 3.36033260e+02, 1.49361976e+02,\n",
|
||||
" 5.94809582e+01, 2.68597003e+01, 1.31073753e+01,\n",
|
||||
" 6.60576408e+00, 4.10892000e+00, 4.03067488e+00,\n",
|
||||
" 5.25251605e+00, 6.56267752e+00, 8.01989052e+00,\n",
|
||||
" 1.02889234e+01, 1.50359567e+01, 2.35198494e+01,\n",
|
||||
" 3.60094535e+01, 5.45566016e+01, 7.86143059e+01,\n",
|
||||
" 1.05154338e+02, 1.27026062e+02, 1.43644507e+02,\n",
|
||||
" 1.56945792e+02, 1.66362651e+02, 1.71043208e+02,\n",
|
||||
" 1.70082239e+02, 1.65514714e+02, 1.57395317e+02,\n",
|
||||
" 1.46351336e+02, 1.33299014e+02, 1.20754766e+02,\n",
|
||||
" 1.09753808e+02, 9.89302713e+01, 8.85290515e+01,\n",
|
||||
" 7.84946496e+01, 6.89501897e+01, 5.84210778e+01,\n",
|
||||
" 4.29656441e+01, 2.24842307e+01, 6.30396936e+00,\n",
|
||||
" 1.95241628e+00, 3.16204318e+00, 2.29981760e+01,\n",
|
||||
" 6.19914391e+01, 1.26262913e+02, 2.26157313e+02,\n",
|
||||
" 3.37249660e+02, 4.25572770e+02, 4.26317144e+02,\n",
|
||||
" 3.41068698e+02, 2.39971365e+02, 1.64269182e+02,\n",
|
||||
" 1.37876397e+02, 2.56982660e+02, 1.06313526e+03,\n",
|
||||
" 4.35525995e+03, 1.32851564e+04, 3.27770877e+04,\n",
|
||||
" 6.09617713e+04, 8.03476028e+04])"
|
||||
]
|
||||
},
|
||||
"execution_count": 11,
|
||||
@@ -385,42 +425,11 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ -4.30174521e-02, -2.82476125e-02, -1.12416190e-02,\n",
|
||||
" 1.16416209e-01, -6.26268459e-02, -1.72987726e-02,\n",
|
||||
" 3.33927883e-03, -1.28190065e-01, -1.99208335e-02,\n",
|
||||
" 5.43972252e-03, 7.85202615e-03, 6.20311032e-03,\n",
|
||||
" 2.04136058e-03, 9.56190481e-03, -1.09499039e-04,\n",
|
||||
" -1.03789061e-02, 9.53663363e-04, -4.19335702e-05,\n",
|
||||
" -1.64251310e-04, -2.59815192e-03, -6.75066243e-04,\n",
|
||||
" -1.49936092e-03, -3.33456950e-04, 2.44076361e-03,\n",
|
||||
" -7.33153707e-04, -9.61669258e-04, 5.97912054e-04,\n",
|
||||
" -6.83894797e-04, 7.26002326e-04, -3.60787807e-04,\n",
|
||||
" 1.68289077e-04, 3.31670038e-04, 9.45753316e-05,\n",
|
||||
" 3.30271734e-04, -2.26943406e-04, 2.11135875e-04,\n",
|
||||
" 6.80321836e-05, 1.62860800e-04, -1.84990894e-04,\n",
|
||||
" -5.71275809e-05, 1.37379585e-04, -1.99667211e-04,\n",
|
||||
" 7.01025135e-05, -1.01077045e-04, -2.60300325e-05,\n",
|
||||
" 9.37896779e-05, 2.50865054e-05, 2.79492481e-05,\n",
|
||||
" -1.02976226e-05, -4.30700509e-05, 1.40579045e-06,\n",
|
||||
" 7.82098221e-05, 1.95325111e-06, -1.45440569e-05,\n",
|
||||
" -2.50424982e-07, 1.63473288e-05, 6.79494048e-06,\n",
|
||||
" -8.75565310e-06, -1.09645580e-05, 1.46704211e-05,\n",
|
||||
" -1.28231535e-05, 1.07621173e-05])"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
|
||||
@@ -11,6 +11,175 @@
|
||||
"## Forward model a data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import SimPEG as simpeg\n",
|
||||
"import simpegMT as simpegmt\n",
|
||||
"import cPickle as pickle"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"## Setup the forward modeling\n",
|
||||
"# Read the model\n",
|
||||
"modelname = \"simpegTDmodel.con\"\n",
|
||||
"sigma = np.loadtxt(modelname)\n",
|
||||
"# Make the mesh.\n",
|
||||
"mTensor = simpeg.Utils.meshTensor\n",
|
||||
"cSize = [50,20]\n",
|
||||
"# Cells constant size mesh\n",
|
||||
"hx = mTensor([(cSize[0],50)])\n",
|
||||
"hy = mTensor([(cSize[0],50)])\n",
|
||||
"hz = mTensor([(cSize[1],48)])\n",
|
||||
"x0 = np.array([-1250,-1250,- 30*20])\n",
|
||||
"mesh3dCons = simpeg.Mesh.TensorMesh([hx,hy,hz],x0)\n",
|
||||
"# With padding\n",
|
||||
"hPad = mTensor([(cSize[0],5,1.5)])\n",
|
||||
"aPad = mTensor([(cSize[1],13,1.3)])\n",
|
||||
"bPad = mTensor([(cSize[1],5,-1.5)])\n",
|
||||
"hxPad = np.hstack((hPad[::-1],mTensor([(cSize[0],40)]),hPad))\n",
|
||||
"hyPad = np.hstack((hPad[::-1],mTensor([(cSize[0],40)]),hPad))\n",
|
||||
"hzPad = np.hstack((bPad,mTensor([(cSize[1],30)]),aPad))\n",
|
||||
"x0Pad = np.array([-(np.sum(hPad)+1000),-(np.sum(hPad)+1000),-(np.sum(bPad)+600)])\n",
|
||||
"mesh3d = simpeg.Mesh.TensorMesh([hxPad,hyPad,hzPad],x0Pad)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Load the model to the uniform cell mesh\n",
|
||||
"modelUniCell = simpeg.Utils.meshutils.readUBCTensorModel(modelname,mesh3dCons)\n",
|
||||
"# Save as a vtk file\n",
|
||||
"simpeg.Utils.meshutils.writeVTRFile('modelTDuniMesh.vtr',mesh3dCons,{'S/m':modelUniCell})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Load the model to the mesh with padding cells\n",
|
||||
"modelTD = simpeg.Utils.meshutils.readUBCTensorModel(modelname,mesh3d)\n",
|
||||
"# Save as a vtk file\n",
|
||||
"simpeg.Utils.meshutils.writeVTRFile('modelTDpaddedMesh.vtr',mesh3d,{'S/m':modelTD})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Define the data locations\n",
|
||||
"xG,yG = np.meshgrid(np.linspace(-700,700,8),np.linspace(-700,700,8))\n",
|
||||
"zG = np.zeros_like(xG)\n",
|
||||
"locs = np.hstack((simpeg.mkvc(xG.ravel(),2),simpeg.mkvc(yG.ravel(),2),simpeg.mkvc(zG.ravel(),2)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "SolverException",
|
||||
"evalue": "Mumps Exception [-13] - An error occurred in a Fortran ALLOCATE statement. The size that the package requested is available in INFO(2). If INFO(2) is negative, then the size that the package requested is obtained by multiplying the absolute value of INFO(2) by 1 million.",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mSolverException\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-7-10aaec78a1f3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 21\u001b[0m \u001b[1;31m# Forward model the data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 22\u001b[1;33m \u001b[0mfields\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mproblem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfields\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodelTD\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 23\u001b[0m \u001b[0mmtData\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msurvey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprojectFields\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfields\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/simpegmt/simpegMT/ProblemMT3D/Problems.pyc\u001b[0m in \u001b[0;36mfields\u001b[1;34m(self, m)\u001b[0m\n\u001b[0;32m 100\u001b[0m \u001b[0mA\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetA\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 101\u001b[0m \u001b[0mrhs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetRHS\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 102\u001b[1;33m \u001b[0mAinv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSolver\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mA\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msolverOpts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 103\u001b[0m \u001b[0me_s\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAinv\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mrhs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 104\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/pymatsolver/pymatsolver/Mumps/__init__.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, A, symmetric, fromPointer)\u001b[0m\n\u001b[0;32m 96\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 97\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mfromPointer\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 98\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfactor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 99\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfromPointer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_Pointer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 100\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpointer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfromPointer\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m/media/gudni/ExtraDrive1/Codes/python/pymatsolver/pymatsolver/Mumps/__init__.pyc\u001b[0m in \u001b[0;36mfactor\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 132\u001b[0m self.A.indptr+1)\n\u001b[0;32m 133\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mierr\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 134\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mSolverException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Mumps Exception [%d] - %s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mierr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_mumpsErrors\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mierr\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 135\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mierr\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 136\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m\"Mumps Warning [%d] - %s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mierr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_mumpsErrors\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mierr\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mSolverException\u001b[0m: Mumps Exception [-13] - An error occurred in a Fortran ALLOCATE statement. The size that the package requested is available in INFO(2). If INFO(2) is negative, then the size that the package requested is obtained by multiplying the absolute value of INFO(2) by 1 million."
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Make the receiver list\n",
|
||||
"rxList = []\n",
|
||||
"for rxType in ['zxxr','zxxi','zxyr','zxyi','zyxr','zyxi','zyyr','zyyi']:\n",
|
||||
" rxList.append(simpegmt.SurveyMT.RxMT(locs,rxType)) \n",
|
||||
"# Source list\n",
|
||||
"srcList =[]\n",
|
||||
"freqs = np.logspace(3,0,13)\n",
|
||||
"for freq in freqs:\n",
|
||||
" srcList.append(simpegmt.SurveyMT.srcMT_polxy_1Dprimary(rxList,freq))\n",
|
||||
"# Survey MT\n",
|
||||
"survey = simpegmt.SurveyMT.SurveyMT(srcList)\n",
|
||||
"\n",
|
||||
"# Setup the problem object\n",
|
||||
"sigma1d = mesh3d.r(modelTD,'CC','CC','M')[0,0,:] # Use the edge column as a background model\n",
|
||||
"problem = simpegmt.ProblemMT3D.eForm_ps(mesh3d,sigmaPrimary = sigma1d)\n",
|
||||
"problem.verbose = False\n",
|
||||
"from pymatsolver import MumpsSolver\n",
|
||||
"problem.Solver = MumpsSolver\n",
|
||||
"problem.pair(survey)\n",
|
||||
"\n",
|
||||
"# Forward model the data\n",
|
||||
"fields = problem.fields(modelTD)\n",
|
||||
"mtData = survey.projectFields(fields)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
" 50*50*48"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"np.sum(modTD<1e-7)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"45000/50**2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
@@ -18,9 +187,7 @@
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Define the model\n"
|
||||
]
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
||||
Reference in new issue
Block a user