[1]:
%matplotlib inline
Credits: This is a modified example from SimPEG.xyz
Linear Least-Squares Inversion with Wavelet-Based-regularization
Here we demonstrate the basics of inverting data with SimPEG by considering a linear inverse problem. We formulate the inverse problem as a least-squares optimization problem. For this tutorial, we focus on the following:
- Defining the forward problem
- Defining the inverse problem (data misfit, regularization, optimization)
- Specifying directives for the inversion
- Recovering a set of model parameters which explains the observations
Import Modules
[2]:
import matplotlib.pyplot as plt
import numpy as np
from SimPEG import (
simulation,
maps,
data_misfit,
directives,
optimization,
inverse_problem,
inversion,
)
from discretize import TensorMesh
from wbi import wavelet_regularization as regularization
# sphinx_gallery_thumbnail_number = 3
[2]:
Defining the Model and Mapping
Here we generate a synthetic model and a mappig which goes from the model space to the row space of our linear operator.
[3]:
nParam = 100 # Number of model parameters
# A 1D mesh is used to define the row-space of the linear operator.
mesh = TensorMesh([nParam])
# Creating the true model
true_model = np.zeros(mesh.nC)
true_model[mesh.vectorCCx > 0.3] = 1.0
true_model[mesh.vectorCCx > 0.45] = -0.5
true_model[mesh.vectorCCx > 0.6] = 0
# Mapping from the model space to the row space of the linear operator
model_map = maps.IdentityMap(mesh)
# Plotting the true model
fig = plt.figure(figsize=(8, 5))
ax = fig.add_subplot(111)
ax.plot(mesh.vectorCCx, true_model, "b-")
ax.set_ylim([-2, 2])
plt.show()
Defining the Linear Operator
Here we define the linear operator with dimensions (nData, nParam). In practive, you may have a problem-specific linear operator which you would like to construct or load here.
[4]:
# Number of data observations (rows)
nData = 20
# Create the linear operator for the tutorial. The columns of the linear operator
# represents a set of decaying and oscillating functions.
jk = np.linspace(1.0, 60.0, nData)
p = -0.25
q = 0.25
def g(k):
return np.exp(p * jk[k] * mesh.vectorCCx) * np.cos(
np.pi * q * jk[k] * mesh.vectorCCx
)
G = np.empty((nData, nParam))
for i in range(nData):
G[i, :] = g(i)
# Plot the columns of G
fig = plt.figure(figsize=(8, 5))
ax = fig.add_subplot(111)
for i in range(G.shape[0]):
ax.plot(G[i, :])
ax.set_title("Columns of matrix G")
[4]:
Text(0.5, 1.0, 'Columns of matrix G')
Defining the Simulation
The simulation defines the relationship between the model parameters and predicted data.
[5]:
sim = simulation.LinearSimulation(mesh, G=G, model_map=model_map)
/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/SimPEG/simulation.py:546: UserWarning: G has not been implemented for the simulation
warnings.warn("G has not been implemented for the simulation")
Predict Synthetic Data
Here, we use the true model to create synthetic data which we will subsequently invert.
[6]:
# Standard deviation of Gaussian noise being added
std = 0.01
np.random.seed(1)
# Create a SimPEG data object
data_obj = sim.make_synthetic_data(true_model, relative_error=std, add_noise=True)
Define the Inverse Problem
The inverse problem is defined by 3 things:
1) Data Misfit: a measure of how well our recovered model explains the field data
2) Regularization: constraints placed on the recovered model and a priori information
3) Optimization: the numerical approach used to solve the inverse problem
[7]:
#
# Define the data misfit. Here the data misfit is the L2 norm of the weighted
# residual between the observed data and the data predicted for a given model.
# Within the data misfit, the residual between predicted and observed data are
# normalized by the data's standard deviation.
dmis = data_misfit.L2DataMisfit(simulation=sim, data=data_obj)
# Define the regularization (model objective function).
# Play here with the wav-parameter
# - db1 = blocky
# - db2, db3, db4 = rather sharp
# - db5+ = rather smooth
reg = regularization.WaveletRegularization1D(mesh, wav="db6")
# Define how the optimization problem is solved.
opt = optimization.InexactGaussNewton(maxIter=100, maxIterLS=20)
# Here we define the inverse problem that is to be solved
inv_prob = inverse_problem.BaseInvProblem(dmis, reg, opt)
# Here we define any directiveas that are carried out during the inversion. This
# includes the cooling schedule for the trade-off parameter (beta), stopping
# criteria for the inversion and saving inversion results at each iteration.
# Defining a starting value for the trade-off parameter (beta) between the data
# misfit and the regularization.
# Setting a stopping criteria for the inversion.
target_misfit = directives.TargetMisfit()
# The directives are defined as a list.
directives_list = [target_misfit]
Setting a Starting Model and Running the Inversion
To define the inversion object, we need to define the inversion problem and the set of directives. We can then run the inversion.
[8]:
inv = inversion.BaseInversion(inv_prob)
# Starting model
starting_model = np.random.rand(nParam)
starting_model = np.zeros(nParam)
# The inversion class is kinda broken
# Run inversion
recovered_model = inv.run(starting_model)
/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/pymatsolver/direct.py:23: PardisoTypeConversionWarning: Converting dia_matrix matrix to CSR format, will slow down.
self.solver = MKLPardisoSolver(
SimPEG.InvProblem will set Regularization.mref to m0.
SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.
***Done using same Solver and solverOpts as the problem***
model has any nan: 0
============================ Inexact Gauss Newton ============================
# beta phi_d phi_m f |proj(x-g)-x| LS Comment
-----------------------------------------------------------------------------
x0 has any nan: 0
0 1.00e+00 1.00e+05 9.58e-03 1.00e+05 1.26e+06 0
1 1.00e+00 6.29e+04 2.00e-02 6.29e+04 7.20e+04 0
2 1.00e+00 2.93e+04 7.57e-02 2.93e+04 3.31e+04 0
/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/pymatsolver/direct.py:73: PardisoTypeConversionWarning: Converting dia_matrix matrix to CSR format, will slow down.
self.solver.refactor(self.A)
3 1.00e+00 1.87e+04 8.19e-02 1.87e+04 1.58e+05 0
4 1.00e+00 1.59e+04 7.59e-02 1.59e+04 2.10e+04 0
5 1.00e+00 1.49e+04 7.74e-02 1.49e+04 9.11e+04 0
6 1.00e+00 1.44e+04 7.47e-02 1.44e+04 5.25e+04 0
7 1.00e+00 1.38e+04 7.31e-02 1.38e+04 1.01e+05 0
8 1.00e+00 6.57e+03 5.14e-02 6.57e+03 2.21e+04 0 Skip BFGS
9 1.00e+00 4.96e+03 5.28e-02 4.96e+03 2.22e+04 0
10 1.00e+00 4.36e+03 5.42e-02 4.36e+03 6.78e+04 0
11 1.00e+00 3.51e+03 5.26e-02 3.51e+03 4.76e+04 0
12 1.00e+00 3.07e+03 5.49e-02 3.07e+03 3.62e+04 0
13 1.00e+00 2.41e+03 5.43e-02 2.41e+03 2.48e+04 0
14 1.00e+00 1.55e+03 4.81e-02 1.55e+03 3.26e+04 0 Skip BFGS
15 1.00e+00 1.41e+03 4.74e-02 1.41e+03 1.96e+04 0
16 1.00e+00 9.10e+02 4.71e-02 9.10e+02 8.26e+04 0
17 1.00e+00 5.11e+02 6.27e-02 5.11e+02 2.58e+04 0 Skip BFGS
18 1.00e+00 4.93e+02 6.23e-02 4.93e+02 2.55e+04 0
19 1.00e+00 3.85e+02 6.87e-02 3.85e+02 4.29e+04 0 Skip BFGS
20 1.00e+00 3.18e+02 6.20e-02 3.18e+02 2.62e+04 0
21 1.00e+00 2.45e+02 5.86e-02 2.46e+02 2.60e+04 0 Skip BFGS
22 1.00e+00 2.42e+02 5.89e-02 2.42e+02 2.57e+04 0
23 1.00e+00 2.42e+02 5.89e-02 2.42e+02 2.64e+04 0 Skip BFGS
24 1.00e+00 2.42e+02 5.87e-02 2.42e+02 2.59e+04 0
25 1.00e+00 2.39e+02 5.52e-02 2.39e+02 2.60e+04 0
26 1.00e+00 3.06e+01 6.15e-02 3.06e+01 1.58e+04 0 Skip BFGS
27 1.00e+00 3.03e+01 6.23e-02 3.04e+01 1.56e+04 0 Skip BFGS
28 1.00e+00 3.03e+01 6.23e-02 3.04e+01 1.49e+04 0
29 1.00e+00 3.03e+01 6.23e-02 3.03e+01 1.56e+04 0
30 1.00e+00 3.01e+01 6.31e-02 3.02e+01 1.72e+04 0 Skip BFGS
31 1.00e+00 2.99e+01 6.14e-02 3.00e+01 1.70e+04 0
32 1.00e+00 2.31e+01 6.14e-02 2.32e+01 7.55e+03 0
33 1.00e+00 1.38e+01 6.26e-02 1.38e+01 2.06e+04 0
34 1.00e+00 1.32e+01 6.31e-02 1.33e+01 1.25e+04 0 Skip BFGS
35 1.00e+00 1.29e+01 6.27e-02 1.30e+01 3.82e+03 0
36 1.00e+00 1.15e+01 6.29e-02 1.16e+01 1.83e+03 0
37 1.00e+00 1.12e+01 6.44e-02 1.12e+01 1.08e+03 0 Skip BFGS
38 1.00e+00 1.08e+01 6.31e-02 1.09e+01 1.51e+03 0
39 1.00e+00 8.37e+00 6.28e-02 8.43e+00 3.46e+03 0 Skip BFGS
40 1.00e+00 8.09e+00 6.32e-02 8.15e+00 3.17e+03 0
41 1.00e+00 7.97e+00 6.35e-02 8.04e+00 3.57e+03 0 Skip BFGS
42 1.00e+00 7.89e+00 6.33e-02 7.95e+00 3.54e+03 0
43 1.00e+00 7.86e+00 6.34e-02 7.92e+00 3.75e+03 0 Skip BFGS
44 1.00e+00 7.83e+00 6.34e-02 7.90e+00 3.66e+03 0
45 1.00e+00 7.66e+00 6.42e-02 7.72e+00 4.90e+03 0 Skip BFGS
46 1.00e+00 7.64e+00 6.39e-02 7.71e+00 4.72e+03 0
47 1.00e+00 7.64e+00 6.38e-02 7.71e+00 4.53e+03 0 Skip BFGS
48 1.00e+00 7.64e+00 6.39e-02 7.70e+00 4.57e+03 0
49 1.00e+00 7.34e+00 6.24e-02 7.40e+00 1.06e+04 0 Skip BFGS
50 1.00e+00 7.31e+00 6.21e-02 7.37e+00 1.11e+04 0 Skip BFGS
51 1.00e+00 7.28e+00 6.23e-02 7.34e+00 1.05e+04 0
52 1.00e+00 7.28e+00 6.21e-02 7.34e+00 1.04e+04 0 Skip BFGS
53 1.00e+00 7.27e+00 6.23e-02 7.34e+00 1.06e+04 0
54 1.00e+00 7.27e+00 6.24e-02 7.33e+00 1.04e+04 0
55 1.00e+00 7.10e+00 6.24e-02 7.16e+00 1.16e+03 0 Skip BFGS
56 1.00e+00 7.03e+00 6.27e-02 7.09e+00 2.54e+03 0
57 1.00e+00 6.99e+00 6.31e-02 7.05e+00 6.12e+02 0 Skip BFGS
58 1.00e+00 6.99e+00 6.31e-02 7.05e+00 5.35e+02 0 Skip BFGS
59 1.00e+00 6.99e+00 6.31e-02 7.05e+00 5.53e+02 0
60 1.00e+00 6.71e+00 6.45e-02 6.77e+00 1.35e+03 0
61 1.00e+00 6.67e+00 6.45e-02 6.74e+00 1.75e+03 0 Skip BFGS
62 1.00e+00 6.67e+00 6.45e-02 6.73e+00 2.39e+03 0
63 1.00e+00 6.64e+00 6.48e-02 6.71e+00 2.30e+03 0
64 1.00e+00 6.59e+00 6.47e-02 6.65e+00 1.80e+03 0 Skip BFGS
65 1.00e+00 6.59e+00 6.47e-02 6.65e+00 1.83e+03 0 Skip BFGS
66 1.00e+00 6.59e+00 6.47e-02 6.65e+00 1.81e+03 0
67 1.00e+00 6.59e+00 6.47e-02 6.65e+00 1.86e+03 0
68 1.00e+00 6.59e+00 6.48e-02 6.65e+00 2.00e+03 0 Skip BFGS
69 1.00e+00 6.58e+00 6.47e-02 6.65e+00 1.90e+03 0
70 1.00e+00 6.56e+00 6.46e-02 6.63e+00 2.18e+03 0
71 1.00e+00 6.56e+00 6.46e-02 6.62e+00 2.29e+03 0 Skip BFGS
72 1.00e+00 6.55e+00 6.46e-02 6.62e+00 2.30e+03 0
73 1.00e+00 6.54e+00 6.45e-02 6.61e+00 3.14e+03 0 Skip BFGS
74 1.00e+00 6.53e+00 6.40e-02 6.60e+00 2.23e+03 0
75 1.00e+00 6.51e+00 6.38e-02 6.57e+00 2.55e+03 0 Skip BFGS
76 1.00e+00 6.48e+00 6.34e-02 6.55e+00 1.57e+03 0
77 1.00e+00 6.31e+00 6.75e-02 6.38e+00 2.14e+03 0 Skip BFGS
78 1.00e+00 6.28e+00 6.83e-02 6.34e+00 1.04e+03 0
79 1.00e+00 6.27e+00 6.85e-02 6.34e+00 1.95e+03 0 Skip BFGS
80 1.00e+00 6.27e+00 6.82e-02 6.34e+00 1.25e+03 0
81 1.00e+00 6.27e+00 6.83e-02 6.33e+00 1.48e+03 0
82 1.00e+00 6.26e+00 6.83e-02 6.33e+00 2.06e+03 0
83 1.00e+00 6.24e+00 6.96e-02 6.31e+00 1.64e+03 0 Skip BFGS
84 1.00e+00 6.24e+00 6.96e-02 6.31e+00 1.54e+03 0 Skip BFGS
85 1.00e+00 6.24e+00 6.96e-02 6.31e+00 1.63e+03 0
86 1.00e+00 6.23e+00 6.87e-02 6.30e+00 1.93e+03 0 Skip BFGS
87 1.00e+00 6.22e+00 6.91e-02 6.29e+00 1.51e+03 0
88 1.00e+00 6.22e+00 6.92e-02 6.29e+00 1.42e+03 0 Skip BFGS
89 1.00e+00 6.21e+00 6.92e-02 6.28e+00 1.34e+03 0
90 1.00e+00 6.21e+00 6.93e-02 6.28e+00 1.44e+03 0 Skip BFGS
91 1.00e+00 6.21e+00 6.92e-02 6.28e+00 1.51e+03 0
92 1.00e+00 6.21e+00 6.92e-02 6.28e+00 1.55e+03 0 Skip BFGS
93 1.00e+00 6.21e+00 6.92e-02 6.28e+00 1.58e+03 0
94 1.00e+00 6.20e+00 6.91e-02 6.27e+00 2.13e+03 0 Skip BFGS
95 1.00e+00 6.20e+00 6.91e-02 6.27e+00 2.08e+03 0
96 1.00e+00 6.19e+00 6.92e-02 6.25e+00 1.56e+03 0
97 1.00e+00 6.19e+00 6.93e-02 6.25e+00 1.98e+03 0 Skip BFGS
98 1.00e+00 6.19e+00 6.93e-02 6.25e+00 1.58e+03 0
99 1.00e+00 6.17e+00 6.98e-02 6.24e+00 8.79e+02 0 Skip BFGS
100 1.00e+00 6.16e+00 6.90e-02 6.23e+00 4.08e+02 0
------------------------- STOP! -------------------------
1 : |fc-fOld| = 7.7596e-03 <= tolF*(1+|f0|) = 1.0000e+04
1 : |xc-x_last| = 1.8055e-02 <= tolX*(1+|x0|) = 1.0000e-01
0 : |proj(x-g)-x| = 4.0766e+02 <= tolG = 1.0000e-01
0 : |proj(x-g)-x| = 4.0766e+02 <= 1e3*eps = 1.0000e-02
1 : maxIter = 100 <= iter = 100
------------------------- DONE! -------------------------
[9]:
# Observed versus predicted data
fig, ax = plt.subplots(1, 2, figsize=(12 * 1.2, 4 * 1.2))
ax[0].plot(data_obj.dobs, "b-")
ax[0].plot(inv_prob.dpred, "r-")
ax[0].legend(("Observed Data", "Predicted Data"))
# True versus recovered model
ax[1].plot(mesh.vectorCCx, true_model, "b-")
ax[1].plot(mesh.vectorCCx, recovered_model, "r-")
ax[1].legend(("True Model", "Recovered Model"))
ax[1].set_ylim([-2, 2])
ax[1].set_title("Wavelet-type " + reg.wavelets.wav)
plt.show()
Generate ensemble of inversion results
[10]:
fig, ax_ls = plt.subplots(2,3, figsize=(12, 6))
wav_list = ['db1', 'db2', 'db3', 'db4', 'db5', 'db6']
betalist = [1e3, 1e4, 1e4, 1e4, 1e4, 1e4]
for idx, wav in enumerate(wav_list):
reg = regularization.WaveletRegularization1D(mesh, wav=wav)
inv_prob = inverse_problem.BaseInvProblem(dmis, reg, opt)
inv_prob.beta = betalist[idx]
inv = inversion.BaseInversion(inv_prob, directives_list)
recovered_model = inv.run(starting_model)
ax_ls[idx//3, idx%3].plot(mesh.vectorCCx, true_model, "b-")
ax_ls[idx//3, idx%3].plot(mesh.vectorCCx, recovered_model, "r-")
ax_ls[idx//3, idx%3].legend(("True Model", "Recovered Model"))
# ax[1].set_ylim([-2, 2])
ax_ls[idx//3, idx%3].set_title("Wavelet-type " + reg.wavelets.wav)
plt.tight_layout()
plt.show()
The callback on the InexactGaussNewton Optimization was replaced.
SimPEG.InvProblem will set Regularization.mref to m0.
SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.
***Done using same Solver and solverOpts as the problem***
model has any nan: 0
============================ Inexact Gauss Newton ============================
# beta phi_d phi_m f |proj(x-g)-x| LS Comment
-----------------------------------------------------------------------------
x0 has any nan: 0
0 1.00e+03 1.00e+05 8.85e-03 1.00e+05 1.26e+06 0
1 1.00e+03 6.29e+04 5.09e-02 6.30e+04 7.20e+04 0
2 1.00e+03 2.93e+04 2.27e-01 2.95e+04 3.22e+04 0
/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/pymatsolver/direct.py:23: PardisoTypeConversionWarning: Converting dia_matrix matrix to CSR format, will slow down.
self.solver = MKLPardisoSolver(
/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/pymatsolver/direct.py:73: PardisoTypeConversionWarning: Converting dia_matrix matrix to CSR format, will slow down.
self.solver.refactor(self.A)
3 1.00e+03 1.90e+04 2.73e-01 1.92e+04 4.81e+04 0 Skip BFGS
4 1.00e+03 1.63e+04 2.71e-01 1.66e+04 2.01e+04 0
5 1.00e+03 1.45e+04 2.55e-01 1.48e+04 3.54e+04 0
6 1.00e+03 1.21e+04 2.69e-01 1.24e+04 2.86e+04 0
7 1.00e+03 8.02e+03 3.01e-01 8.32e+03 2.03e+04 0 Skip BFGS
8 1.00e+03 6.79e+03 2.99e-01 7.09e+03 2.38e+04 0
9 1.00e+03 5.47e+03 3.48e-01 5.81e+03 2.63e+04 0
10 1.00e+03 4.63e+03 3.25e-01 4.95e+03 2.82e+04 0
11 1.00e+03 4.33e+03 3.50e-01 4.68e+03 2.49e+04 0
12 1.00e+03 4.21e+03 3.46e-01 4.56e+03 7.22e+04 0
13 1.00e+03 4.14e+03 3.53e-01 4.49e+03 3.32e+04 0
14 1.00e+03 4.11e+03 3.53e-01 4.46e+03 4.80e+04 0 Skip BFGS
15 1.00e+03 4.10e+03 3.54e-01 4.45e+03 3.63e+04 0
16 1.00e+03 2.44e+03 3.15e-01 2.75e+03 3.15e+04 0 Skip BFGS
17 1.00e+03 1.91e+03 3.36e-01 2.25e+03 4.74e+04 0
18 1.00e+03 1.70e+03 3.24e-01 2.02e+03 1.58e+04 0
19 1.00e+03 1.36e+03 3.31e-01 1.69e+03 2.74e+04 0 Skip BFGS
20 1.00e+03 1.05e+03 3.42e-01 1.39e+03 2.94e+04 0
21 1.00e+03 7.49e+02 4.11e-01 1.16e+03 1.83e+04 0 Skip BFGS
22 1.00e+03 6.86e+02 4.36e-01 1.12e+03 2.09e+04 0 Skip BFGS
23 1.00e+03 6.96e+02 3.99e-01 1.10e+03 1.84e+04 0
24 1.00e+03 7.08e+02 3.79e-01 1.09e+03 3.19e+04 0 Skip BFGS
25 1.00e+03 6.97e+02 3.81e-01 1.08e+03 1.69e+04 0
26 1.00e+03 7.11e+02 3.63e-01 1.07e+03 1.34e+04 1
27 1.00e+03 6.88e+02 3.74e-01 1.06e+03 3.25e+04 1
28 1.00e+03 6.90e+02 3.59e-01 1.05e+03 3.88e+04 2
29 1.00e+03 6.81e+02 3.62e-01 1.04e+03 2.98e+04 1
30 1.00e+03 6.77e+02 3.49e-01 1.03e+03 3.31e+04 1
31 1.00e+03 6.60e+02 3.60e-01 1.02e+03 3.87e+04 0
32 1.00e+03 3.38e+02 3.59e-01 6.97e+02 1.89e+04 0
33 1.00e+03 2.05e+02 3.61e-01 5.66e+02 1.83e+04 0
34 1.00e+03 4.55e+01 3.72e-01 4.17e+02 4.30e+03 0 Skip BFGS
35 1.00e+03 4.43e+01 3.58e-01 4.02e+02 4.04e+03 0
36 1.00e+03 3.27e+01 3.20e-01 3.53e+02 2.49e+04 1 Skip BFGS
37 1.00e+03 2.52e+01 3.15e-01 3.41e+02 2.38e+03 0
38 1.00e+03 2.67e+01 3.11e-01 3.38e+02 4.74e+04 0
39 1.00e+03 2.75e+01 3.08e-01 3.36e+02 4.72e+03 0
40 1.00e+03 1.77e+01 3.11e-01 3.28e+02 3.13e+04 1
41 1.00e+03 2.72e+01 2.94e-01 3.21e+02 1.73e+04 0
42 1.00e+03 2.78e+01 2.90e-01 3.18e+02 2.26e+04 0
43 1.00e+03 2.02e+01 2.80e-01 3.00e+02 4.41e+04 1
44 1.00e+03 2.21e+01 2.67e-01 2.89e+02 3.18e+04 1
45 1.00e+03 2.30e+01 2.59e-01 2.82e+02 3.33e+04 0
46 1.00e+03 1.75e+01 2.48e-01 2.65e+02 5.72e+04 1
47 1.00e+03 1.48e+01 2.38e-01 2.53e+02 3.02e+04 1
48 1.00e+03 1.36e+01 2.34e-01 2.47e+02 2.46e+04 2 Skip BFGS
49 1.00e+03 1.31e+01 2.32e-01 2.45e+02 2.32e+04 2
50 1.00e+03 1.26e+01 2.31e-01 2.44e+02 2.01e+04 3 Skip BFGS
51 1.00e+03 1.27e+01 2.26e-01 2.38e+02 2.59e+04 3
52 1.00e+03 1.33e+01 2.24e-01 2.37e+02 1.64e+04 1
53 1.00e+03 1.33e+01 2.23e-01 2.36e+02 1.54e+04 3
54 1.00e+03 1.55e+01 2.20e-01 2.35e+02 3.77e+04 0
55 1.00e+03 1.50e+01 2.19e-01 2.34e+02 7.35e+03 0 Skip BFGS
56 1.00e+03 1.21e+01 2.14e-01 2.26e+02 7.93e+03 0
57 1.00e+03 1.43e+01 2.07e-01 2.22e+02 1.18e+04 1
58 1.00e+03 1.23e+01 2.08e-01 2.20e+02 2.84e+04 0
59 1.00e+03 1.29e+01 2.03e-01 2.16e+02 2.01e+04 3
60 1.00e+03 1.28e+01 1.98e-01 2.11e+02 1.59e+04 3
61 1.00e+03 1.27e+01 1.96e-01 2.09e+02 1.56e+04 1
62 1.00e+03 1.21e+01 1.94e-01 2.06e+02 1.86e+04 1
63 1.00e+03 1.11e+01 1.91e-01 2.02e+02 2.72e+04 1
64 1.00e+03 1.30e+01 1.85e-01 1.98e+02 3.61e+04 1
65 1.00e+03 1.45e+01 1.81e-01 1.95e+02 1.91e+04 0 Skip BFGS
66 1.00e+03 1.21e+01 1.80e-01 1.92e+02 1.64e+04 1
67 1.00e+03 1.29e+01 1.77e-01 1.90e+02 3.48e+04 2
68 1.00e+03 1.24e+01 1.76e-01 1.88e+02 3.13e+03 0
69 1.00e+03 1.25e+01 1.75e-01 1.87e+02 1.31e+04 1
70 1.00e+03 1.17e+01 1.73e-01 1.85e+02 7.62e+03 2
71 1.00e+03 1.14e+01 1.72e-01 1.83e+02 7.62e+03 3
72 1.00e+03 1.15e+01 1.71e-01 1.82e+02 4.92e+03 2 Skip BFGS
73 1.00e+03 1.12e+01 1.70e-01 1.81e+02 4.05e+03 2
74 1.00e+03 1.09e+01 1.68e-01 1.79e+02 9.17e+03 3 Skip BFGS
75 1.00e+03 1.09e+01 1.67e-01 1.78e+02 2.62e+03 3
76 1.00e+03 1.09e+01 1.66e-01 1.77e+02 6.58e+03 3
77 1.00e+03 1.08e+01 1.65e-01 1.76e+02 8.52e+03 3
78 1.00e+03 1.10e+01 1.64e-01 1.75e+02 5.20e+03 2
79 1.00e+03 1.12e+01 1.63e-01 1.74e+02 7.65e+03 2 Skip BFGS
80 1.00e+03 1.09e+01 1.63e-01 1.73e+02 4.48e+03 3
81 1.00e+03 1.12e+01 1.62e-01 1.73e+02 5.03e+03 1
82 1.00e+03 1.09e+01 1.61e-01 1.72e+02 3.72e+03 2
83 1.00e+03 1.09e+01 1.61e-01 1.72e+02 3.19e+03 4
84 1.00e+03 1.07e+01 1.60e-01 1.71e+02 2.79e+03 3
85 1.00e+03 1.06e+01 1.60e-01 1.70e+02 2.42e+03 4
86 1.00e+03 1.08e+01 1.59e-01 1.70e+02 2.95e+03 2 Skip BFGS
87 1.00e+03 1.05e+01 1.59e-01 1.69e+02 2.19e+03 2
88 1.00e+03 1.04e+01 1.59e-01 1.69e+02 2.37e+03 1 Skip BFGS
89 1.00e+03 1.04e+01 1.58e-01 1.68e+02 2.01e+03 4
90 1.00e+03 1.05e+01 1.56e-01 1.67e+02 1.90e+03 4
91 1.00e+03 1.06e+01 1.56e-01 1.66e+02 2.12e+03 4
92 1.00e+03 1.09e+01 1.54e-01 1.65e+02 1.73e+03 2
93 1.00e+03 1.18e+01 1.50e-01 1.62e+02 2.26e+03 0
94 1.00e+03 1.20e+01 1.48e-01 1.60e+02 2.77e+03 1
95 1.00e+03 1.24e+01 1.48e-01 1.60e+02 3.78e+03 1
96 1.00e+03 1.24e+01 1.48e-01 1.60e+02 4.96e+03 1
97 1.00e+03 1.18e+01 1.47e-01 1.59e+02 2.82e+03 1
98 1.00e+03 1.17e+01 1.47e-01 1.59e+02 2.50e+03 1
99 1.00e+03 1.16e+01 1.46e-01 1.57e+02 1.94e+03 4
100 1.00e+03 1.17e+01 1.45e-01 1.57e+02 2.35e+03 2
------------------------- STOP! -------------------------
1 : |fc-fOld| = 4.8734e-01 <= tolF*(1+|f0|) = 1.0001e+04
1 : |xc-x_last| = 7.7389e-03 <= tolX*(1+|x0|) = 1.0000e-01
0 : |proj(x-g)-x| = 2.3529e+03 <= tolG = 1.0000e-01
0 : |proj(x-g)-x| = 2.3529e+03 <= 1e3*eps = 1.0000e-02
1 : maxIter = 100 <= iter = 100
------------------------- DONE! -------------------------
The callback on the InexactGaussNewton Optimization was replaced.
SimPEG.InvProblem will set Regularization.mref to m0.
SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.
***Done using same Solver and solverOpts as the problem***
model has any nan: 0
============================ Inexact Gauss Newton ============================
# beta phi_d phi_m f |proj(x-g)-x| LS Comment
-----------------------------------------------------------------------------
x0 has any nan: 0
0 1.00e+04 1.00e+05 9.09e-03 1.00e+05 1.26e+06 0
1 1.00e+04 6.29e+04 3.09e-02 6.32e+04 7.24e+04 0
2 1.00e+04 3.07e+04 1.97e-01 3.26e+04 2.85e+04 0
/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/SimPEG/directives/directives.py:60: UserWarning: InversionDirective TargetMisfit has switched to a new inversion.
warnings.warn(
/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/pymatsolver/direct.py:23: PardisoTypeConversionWarning: Converting dia_matrix matrix to CSR format, will slow down.
self.solver = MKLPardisoSolver(
/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/pymatsolver/direct.py:73: PardisoTypeConversionWarning: Converting dia_matrix matrix to CSR format, will slow down.
self.solver.refactor(self.A)
3 1.00e+04 1.85e+04 2.18e-01 2.07e+04 2.51e+04 0 Skip BFGS
4 1.00e+04 1.41e+04 2.21e-01 1.63e+04 3.87e+04 0
5 1.00e+04 1.11e+04 1.81e-01 1.29e+04 2.42e+04 0
6 1.00e+04 8.39e+03 2.60e-01 1.10e+04 3.43e+04 0
7 1.00e+04 6.17e+03 2.92e-01 9.09e+03 3.48e+04 0 Skip BFGS
8 1.00e+04 4.79e+03 2.63e-01 7.41e+03 2.99e+04 0 Skip BFGS
9 1.00e+04 5.11e+03 1.77e-01 6.88e+03 3.95e+04 0
10 1.00e+04 4.42e+03 2.05e-01 6.47e+03 2.98e+04 0
11 1.00e+04 4.49e+03 1.94e-01 6.43e+03 3.33e+04 1
12 1.00e+04 4.46e+03 1.61e-01 6.07e+03 4.65e+04 0
13 1.00e+04 4.11e+03 1.91e-01 6.02e+03 4.05e+04 0
14 1.00e+04 3.68e+03 2.15e-01 5.83e+03 4.46e+04 0
15 1.00e+04 3.58e+03 1.99e-01 5.57e+03 4.49e+04 0
16 1.00e+04 2.89e+03 2.58e-01 5.48e+03 2.29e+04 0
17 1.00e+04 2.45e+03 2.51e-01 4.96e+03 1.86e+04 1
18 1.00e+04 2.26e+03 2.69e-01 4.95e+03 1.89e+04 1
19 1.00e+04 2.22e+03 2.41e-01 4.63e+03 2.01e+04 2
20 1.00e+04 2.13e+03 2.34e-01 4.47e+03 2.03e+04 3
21 1.00e+04 2.04e+03 2.27e-01 4.31e+03 2.00e+04 3
22 1.00e+04 1.86e+03 2.14e-01 4.00e+03 2.37e+04 1
23 1.00e+04 1.81e+03 1.99e-01 3.80e+03 2.42e+04 3
24 1.00e+04 1.73e+03 2.01e-01 3.74e+03 2.37e+04 2
25 1.00e+04 1.50e+03 2.04e-01 3.54e+03 1.91e+04 2
26 1.00e+04 1.40e+03 2.09e-01 3.48e+03 1.36e+04 1
27 1.00e+04 1.25e+03 2.00e-01 3.25e+03 1.28e+04 2
28 1.00e+04 1.19e+03 1.95e-01 3.14e+03 1.26e+04 3
29 1.00e+04 1.12e+03 1.89e-01 3.01e+03 1.19e+04 2
30 1.00e+04 1.02e+03 1.89e-01 2.91e+03 1.16e+04 1
31 1.00e+04 1.03e+03 1.84e-01 2.87e+03 1.10e+04 4
32 1.00e+04 8.57e+02 1.93e-01 2.79e+03 1.04e+04 1
33 1.00e+04 8.37e+02 1.92e-01 2.76e+03 1.02e+04 3
34 1.00e+04 7.80e+02 1.85e-01 2.63e+03 9.99e+03 2
35 1.00e+04 7.51e+02 1.87e-01 2.62e+03 1.32e+04 2
36 1.00e+04 6.98e+02 1.87e-01 2.57e+03 1.15e+04 3
37 1.00e+04 6.96e+02 1.80e-01 2.50e+03 1.14e+04 4
38 1.00e+04 6.80e+02 1.76e-01 2.44e+03 1.07e+04 3
39 1.00e+04 6.55e+02 1.75e-01 2.40e+03 1.05e+04 3
40 1.00e+04 6.23e+02 1.77e-01 2.39e+03 1.02e+04 3
41 1.00e+04 6.13e+02 1.72e-01 2.33e+03 9.84e+03 4
42 1.00e+04 6.11e+02 1.68e-01 2.29e+03 9.12e+03 4
43 1.00e+04 6.06e+02 1.66e-01 2.27e+03 9.11e+03 5
44 1.00e+04 5.57e+02 1.65e-01 2.21e+03 8.40e+03 2
45 1.00e+04 5.39e+02 1.63e-01 2.17e+03 8.52e+03 4
46 1.00e+04 5.01e+02 1.63e-01 2.13e+03 9.35e+03 2
47 1.00e+04 4.86e+02 1.60e-01 2.09e+03 9.03e+03 4
48 1.00e+04 4.74e+02 1.58e-01 2.05e+03 8.97e+03 4
49 1.00e+04 4.66e+02 1.57e-01 2.03e+03 9.29e+03 4
50 1.00e+04 4.57e+02 1.55e-01 2.01e+03 8.60e+03 4
51 1.00e+04 4.54e+02 1.52e-01 1.97e+03 8.46e+03 4
52 1.00e+04 4.50e+02 1.51e-01 1.96e+03 8.55e+03 5
53 1.00e+04 4.00e+02 1.51e-01 1.91e+03 8.41e+03 2
54 1.00e+04 3.98e+02 1.47e-01 1.87e+03 7.88e+03 5
55 1.00e+04 3.71e+02 1.49e-01 1.87e+03 8.24e+03 3
56 1.00e+04 3.71e+02 1.47e-01 1.84e+03 7.98e+03 4
57 1.00e+04 3.46e+02 1.48e-01 1.82e+03 8.22e+03 2
58 1.00e+04 3.44e+02 1.45e-01 1.79e+03 7.69e+03 5
59 1.00e+04 3.38e+02 1.44e-01 1.78e+03 7.24e+03 4
60 1.00e+04 3.33e+02 1.44e-01 1.77e+03 7.13e+03 4
61 1.00e+04 3.26e+02 1.43e-01 1.76e+03 6.98e+03 4
62 1.00e+04 3.18e+02 1.44e-01 1.75e+03 7.03e+03 3
63 1.00e+04 3.11e+02 1.42e-01 1.73e+03 6.55e+03 4
64 1.00e+04 3.08e+02 1.42e-01 1.73e+03 6.82e+03 5
65 1.00e+04 3.06e+02 1.41e-01 1.72e+03 6.53e+03 5
66 1.00e+04 3.03e+02 1.41e-01 1.71e+03 6.44e+03 5
67 1.00e+04 2.95e+02 1.42e-01 1.71e+03 6.86e+03 4
68 1.00e+04 2.92e+02 1.41e-01 1.70e+03 6.84e+03 5
69 1.00e+04 2.85e+02 1.41e-01 1.69e+03 6.87e+03 3
70 1.00e+04 2.82e+02 1.40e-01 1.68e+03 6.51e+03 5
71 1.00e+04 2.71e+02 1.40e-01 1.67e+03 6.11e+03 3
72 1.00e+04 2.69e+02 1.39e-01 1.66e+03 6.14e+03 5
73 1.00e+04 2.67e+02 1.39e-01 1.66e+03 6.06e+03 5
74 1.00e+04 2.65e+02 1.39e-01 1.66e+03 6.32e+03 5
75 1.00e+04 2.64e+02 1.39e-01 1.66e+03 6.16e+03 5
76 1.00e+04 2.62e+02 1.39e-01 1.65e+03 6.21e+03 5
77 1.00e+04 2.60e+02 1.39e-01 1.65e+03 6.04e+03 5
78 1.00e+04 2.58e+02 1.39e-01 1.64e+03 5.98e+03 5
79 1.00e+04 2.52e+02 1.39e-01 1.64e+03 6.06e+03 3
80 1.00e+04 2.49e+02 1.39e-01 1.64e+03 5.94e+03 5
81 1.00e+04 2.44e+02 1.38e-01 1.63e+03 5.55e+03 4
82 1.00e+04 2.42e+02 1.39e-01 1.63e+03 5.71e+03 5
83 1.00e+04 2.36e+02 1.39e-01 1.62e+03 6.09e+03 3
84 1.00e+04 2.34e+02 1.38e-01 1.62e+03 5.49e+03 5
85 1.00e+04 2.32e+02 1.38e-01 1.62e+03 5.59e+03 5
86 1.00e+04 2.30e+02 1.39e-01 1.62e+03 4.95e+03 4
87 1.00e+04 2.28e+02 1.38e-01 1.61e+03 5.29e+03 5
88 1.00e+04 2.26e+02 1.38e-01 1.61e+03 4.95e+03 5 Skip BFGS
89 1.00e+04 2.26e+02 1.38e-01 1.60e+03 4.91e+03 6
90 1.00e+04 2.24e+02 1.38e-01 1.60e+03 4.94e+03 5
91 1.00e+04 2.23e+02 1.38e-01 1.60e+03 4.86e+03 5
92 1.00e+04 2.22e+02 1.38e-01 1.60e+03 4.57e+03 6
93 1.00e+04 2.15e+02 1.38e-01 1.59e+03 4.67e+03 3
94 1.00e+04 2.14e+02 1.38e-01 1.59e+03 4.89e+03 5
95 1.00e+04 2.10e+02 1.38e-01 1.59e+03 4.60e+03 4
96 1.00e+04 2.08e+02 1.38e-01 1.59e+03 4.75e+03 4
97 1.00e+04 2.06e+02 1.38e-01 1.58e+03 4.52e+03 5
98 1.00e+04 2.05e+02 1.37e-01 1.58e+03 4.46e+03 5 Skip BFGS
99 1.00e+04 2.04e+02 1.37e-01 1.58e+03 4.23e+03 6
100 1.00e+04 2.03e+02 1.37e-01 1.57e+03 4.08e+03 5 Skip BFGS
------------------------- STOP! -------------------------
1 : |fc-fOld| = 3.9421e+00 <= tolF*(1+|f0|) = 1.0009e+04
1 : |xc-x_last| = 4.3781e-03 <= tolX*(1+|x0|) = 1.0000e-01
0 : |proj(x-g)-x| = 4.0834e+03 <= tolG = 1.0000e-01
0 : |proj(x-g)-x| = 4.0834e+03 <= 1e3*eps = 1.0000e-02
1 : maxIter = 100 <= iter = 100
------------------------- DONE! -------------------------
The callback on the InexactGaussNewton Optimization was replaced.
SimPEG.InvProblem will set Regularization.mref to m0.
SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.
***Done using same Solver and solverOpts as the problem***
model has any nan: 0
============================ Inexact Gauss Newton ============================
# beta phi_d phi_m f |proj(x-g)-x| LS Comment
-----------------------------------------------------------------------------
x0 has any nan: 0
0 1.00e+04 1.00e+05 9.19e-03 1.00e+05 1.26e+06 0 Skip BFGS
1 1.00e+04 6.29e+04 2.88e-02 6.32e+04 7.21e+04 0 Skip BFGS
2 1.00e+04 3.05e+04 1.37e-01 3.19e+04 2.84e+04 0
/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/SimPEG/directives/directives.py:60: UserWarning: InversionDirective TargetMisfit has switched to a new inversion.
warnings.warn(
/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/pymatsolver/direct.py:23: PardisoTypeConversionWarning: Converting dia_matrix matrix to CSR format, will slow down.
self.solver = MKLPardisoSolver(
/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/pymatsolver/direct.py:73: PardisoTypeConversionWarning: Converting dia_matrix matrix to CSR format, will slow down.
self.solver.refactor(self.A)
3 1.00e+04 1.83e+04 1.43e-01 1.97e+04 2.21e+04 0 Skip BFGS
4 1.00e+04 1.40e+04 1.62e-01 1.57e+04 2.86e+04 0
5 1.00e+04 1.15e+04 1.17e-01 1.27e+04 2.55e+04 0
6 1.00e+04 8.69e+03 1.70e-01 1.04e+04 1.89e+04 0
7 1.00e+04 6.10e+03 1.82e-01 7.92e+03 2.05e+04 0 Skip BFGS
8 1.00e+04 5.00e+03 1.66e-01 6.66e+03 1.55e+04 0 Skip BFGS
9 1.00e+04 4.53e+03 1.44e-01 5.96e+03 2.16e+04 1
10 1.00e+04 4.48e+03 1.04e-01 5.52e+03 1.59e+04 0
11 1.00e+04 3.81e+03 1.11e-01 4.92e+03 1.42e+04 0
12 1.00e+04 3.23e+03 1.42e-01 4.64e+03 1.73e+04 1
13 1.00e+04 2.90e+03 1.32e-01 4.22e+03 1.62e+04 0
14 1.00e+04 2.31e+03 1.61e-01 3.93e+03 2.00e+04 1
15 1.00e+04 1.98e+03 1.58e-01 3.56e+03 4.49e+04 0
16 1.00e+04 1.90e+03 1.32e-01 3.22e+03 2.63e+04 1
17 1.00e+04 1.62e+03 1.42e-01 3.05e+03 3.03e+04 1
18 1.00e+04 1.37e+03 1.37e-01 2.74e+03 1.97e+04 2
19 1.00e+04 1.25e+03 1.22e-01 2.47e+03 1.06e+04 1
20 1.00e+04 1.00e+03 1.34e-01 2.35e+03 1.83e+04 1
21 1.00e+04 9.88e+02 1.19e-01 2.18e+03 1.78e+04 3
22 1.00e+04 9.85e+02 1.15e-01 2.13e+03 1.72e+04 3
23 1.00e+04 9.74e+02 1.11e-01 2.08e+03 1.72e+04 4
24 1.00e+04 9.68e+02 1.09e-01 2.06e+03 1.78e+04 4
25 1.00e+04 9.55e+02 1.06e-01 2.02e+03 1.74e+04 3
26 1.00e+04 9.59e+02 1.03e-01 1.99e+03 1.76e+04 4
27 1.00e+04 8.64e+02 1.04e-01 1.90e+03 1.78e+04 2
28 1.00e+04 8.10e+02 1.07e-01 1.88e+03 1.43e+04 1
29 1.00e+04 6.64e+02 1.03e-01 1.70e+03 1.25e+04 2
30 1.00e+04 6.47e+02 9.94e-02 1.64e+03 1.11e+04 4
31 1.00e+04 6.37e+02 9.97e-02 1.63e+03 1.17e+04 4
32 1.00e+04 6.08e+02 9.85e-02 1.59e+03 1.17e+04 2
33 1.00e+04 6.20e+02 9.53e-02 1.57e+03 1.20e+04 3
34 1.00e+04 5.37e+02 1.01e-01 1.55e+03 1.10e+04 2
35 1.00e+04 5.38e+02 9.69e-02 1.51e+03 1.20e+04 3
36 1.00e+04 5.16e+02 9.54e-02 1.47e+03 1.14e+04 3
37 1.00e+04 4.74e+02 9.94e-02 1.47e+03 1.05e+04 2
38 1.00e+04 4.23e+02 1.03e-01 1.46e+03 1.01e+04 2
39 1.00e+04 4.21e+02 8.92e-02 1.31e+03 9.34e+03 4
40 1.00e+04 3.98e+02 8.70e-02 1.27e+03 8.85e+03 3
41 1.00e+04 3.93e+02 8.69e-02 1.26e+03 8.84e+03 4
42 1.00e+04 3.89e+02 8.70e-02 1.26e+03 9.19e+03 3
43 1.00e+04 3.81e+02 8.65e-02 1.25e+03 9.52e+03 4
44 1.00e+04 3.82e+02 8.23e-02 1.20e+03 9.08e+03 4
45 1.00e+04 3.80e+02 8.08e-02 1.19e+03 8.55e+03 5
46 1.00e+04 3.74e+02 7.87e-02 1.16e+03 8.90e+03 5
47 1.00e+04 3.40e+02 7.94e-02 1.13e+03 9.07e+03 2
48 1.00e+04 3.28e+02 8.00e-02 1.13e+03 8.44e+03 4
49 1.00e+04 3.13e+02 7.50e-02 1.06e+03 8.42e+03 3
50 1.00e+04 3.10e+02 7.13e-02 1.02e+03 8.88e+03 5
51 1.00e+04 3.09e+02 6.78e-02 9.87e+02 7.50e+03 5
52 1.00e+04 3.05e+02 6.68e-02 9.73e+02 8.11e+03 5
53 1.00e+04 3.02e+02 6.70e-02 9.72e+02 7.99e+03 3
54 1.00e+04 2.87e+02 6.59e-02 9.46e+02 8.57e+03 4
55 1.00e+04 2.80e+02 6.51e-02 9.30e+02 8.40e+03 3
56 1.00e+04 2.59e+02 6.64e-02 9.23e+02 8.13e+03 3
57 1.00e+04 2.58e+02 6.64e-02 9.21e+02 8.05e+03 4
58 1.00e+04 2.54e+02 6.53e-02 9.06e+02 8.46e+03 5
59 1.00e+04 2.51e+02 6.29e-02 8.80e+02 7.38e+03 5
60 1.00e+04 2.49e+02 6.16e-02 8.65e+02 7.62e+03 5
61 1.00e+04 2.47e+02 6.13e-02 8.61e+02 7.33e+03 6
62 1.00e+04 2.43e+02 6.14e-02 8.57e+02 6.91e+03 4
63 1.00e+04 2.39e+02 6.16e-02 8.55e+02 7.55e+03 4
64 1.00e+04 2.35e+02 6.14e-02 8.49e+02 7.91e+03 4
65 1.00e+04 2.34e+02 6.08e-02 8.42e+02 7.35e+03 5
66 1.00e+04 2.30e+02 6.10e-02 8.40e+02 7.84e+03 4
67 1.00e+04 2.26e+02 5.97e-02 8.22e+02 6.47e+03 5
68 1.00e+04 2.07e+02 6.14e-02 8.21e+02 7.89e+03 2
69 1.00e+04 2.02e+02 5.83e-02 7.85e+02 6.95e+03 5
70 1.00e+04 1.90e+02 5.75e-02 7.65e+02 6.48e+03 3
71 1.00e+04 1.89e+02 5.76e-02 7.65e+02 7.16e+03 5
72 1.00e+04 1.88e+02 5.71e-02 7.59e+02 6.72e+03 5
73 1.00e+04 1.86e+02 5.71e-02 7.56e+02 6.74e+03 5
74 1.00e+04 1.82e+02 5.64e-02 7.46e+02 6.43e+03 4
75 1.00e+04 1.78e+02 5.62e-02 7.39e+02 5.63e+03 4
76 1.00e+04 1.76e+02 5.61e-02 7.37e+02 6.75e+03 5
77 1.00e+04 1.75e+02 5.61e-02 7.35e+02 6.24e+03 5
78 1.00e+04 1.72e+02 5.53e-02 7.25e+02 6.03e+03 5
79 1.00e+04 1.71e+02 5.49e-02 7.20e+02 5.20e+03 5
80 1.00e+04 1.69e+02 5.51e-02 7.20e+02 6.38e+03 5
81 1.00e+04 1.66e+02 5.52e-02 7.18e+02 6.42e+03 4 Skip BFGS
82 1.00e+04 1.65e+02 5.49e-02 7.14e+02 6.41e+03 5
83 1.00e+04 1.63e+02 5.48e-02 7.10e+02 6.10e+03 4
84 1.00e+04 1.62e+02 5.42e-02 7.03e+02 5.65e+03 6
85 1.00e+04 1.61e+02 5.39e-02 7.00e+02 4.18e+03 6 Skip BFGS
86 1.00e+04 1.61e+02 5.38e-02 6.99e+02 4.89e+03 6
87 1.00e+04 1.59e+02 5.38e-02 6.97e+02 4.66e+03 5 Skip BFGS
88 1.00e+04 1.58e+02 5.38e-02 6.96e+02 5.31e+03 5
89 1.00e+04 1.57e+02 5.38e-02 6.95e+02 4.47e+03 5
90 1.00e+04 1.56e+02 5.39e-02 6.94e+02 5.37e+03 5
91 1.00e+04 1.51e+02 5.40e-02 6.91e+02 5.74e+03 3
92 1.00e+04 1.50e+02 5.37e-02 6.87e+02 5.35e+03 6
93 1.00e+04 1.13e+02 5.42e-02 6.55e+02 6.11e+03 0 Skip BFGS
94 1.00e+04 1.13e+02 5.32e-02 6.45e+02 5.12e+03 5
95 1.00e+04 1.13e+02 5.30e-02 6.43e+02 5.26e+03 3
96 1.00e+04 1.11e+02 5.30e-02 6.42e+02 5.39e+03 5
97 1.00e+04 1.11e+02 5.28e-02 6.39e+02 5.42e+03 6
98 1.00e+04 1.10e+02 5.28e-02 6.38e+02 5.13e+03 5 Skip BFGS
99 1.00e+04 1.10e+02 5.26e-02 6.36e+02 4.88e+03 6
100 1.00e+04 1.07e+02 5.28e-02 6.35e+02 6.00e+03 3
------------------------- STOP! -------------------------
1 : |fc-fOld| = 1.5744e+00 <= tolF*(1+|f0|) = 1.0009e+04
1 : |xc-x_last| = 7.4404e-03 <= tolX*(1+|x0|) = 1.0000e-01
0 : |proj(x-g)-x| = 5.9960e+03 <= tolG = 1.0000e-01
0 : |proj(x-g)-x| = 5.9960e+03 <= 1e3*eps = 1.0000e-02
1 : maxIter = 100 <= iter = 100
------------------------- DONE! -------------------------
The callback on the InexactGaussNewton Optimization was replaced.
SimPEG.InvProblem will set Regularization.mref to m0.
SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.
***Done using same Solver and solverOpts as the problem***
model has any nan: 0
============================ Inexact Gauss Newton ============================
# beta phi_d phi_m f |proj(x-g)-x| LS Comment
-----------------------------------------------------------------------------
x0 has any nan: 0
0 1.00e+04 1.00e+05 9.24e-03 1.00e+05 1.26e+06 0
/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/SimPEG/directives/directives.py:60: UserWarning: InversionDirective TargetMisfit has switched to a new inversion.
warnings.warn(
/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/pymatsolver/direct.py:23: PardisoTypeConversionWarning: Converting dia_matrix matrix to CSR format, will slow down.
self.solver = MKLPardisoSolver(
/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/pymatsolver/direct.py:73: PardisoTypeConversionWarning: Converting dia_matrix matrix to CSR format, will slow down.
self.solver.refactor(self.A)
1 1.00e+04 6.29e+04 2.07e-02 6.31e+04 7.16e+04 0
2 1.00e+04 3.06e+04 1.09e-01 3.17e+04 2.80e+04 0
3 1.00e+04 1.84e+04 1.22e-01 1.96e+04 2.15e+04 0 Skip BFGS
4 1.00e+04 1.41e+04 1.45e-01 1.55e+04 2.93e+04 0
5 1.00e+04 1.14e+04 1.01e-01 1.24e+04 1.87e+04 0
6 1.00e+04 8.49e+03 1.47e-01 9.95e+03 1.84e+04 0
7 1.00e+04 5.99e+03 1.53e-01 7.53e+03 1.71e+04 0 Skip BFGS
8 1.00e+04 4.92e+03 1.41e-01 6.33e+03 1.60e+04 0 Skip BFGS
9 1.00e+04 4.54e+03 1.07e-01 5.62e+03 1.70e+04 1
10 1.00e+04 4.13e+03 1.28e-01 5.41e+03 2.27e+04 0
11 1.00e+04 3.52e+03 1.13e-01 4.64e+03 1.59e+04 0
12 1.00e+04 3.31e+03 1.00e-01 4.31e+03 2.03e+04 0
13 1.00e+04 3.10e+03 1.09e-01 4.19e+03 1.50e+04 0
14 1.00e+04 2.50e+03 1.50e-01 4.00e+03 1.66e+04 1
15 1.00e+04 1.92e+03 1.90e-01 3.82e+03 2.53e+04 0
16 1.00e+04 2.12e+03 1.29e-01 3.42e+03 2.60e+04 0
17 1.00e+04 1.60e+03 1.73e-01 3.32e+03 1.67e+04 1
18 1.00e+04 1.54e+03 1.60e-01 3.13e+03 1.28e+04 2
19 1.00e+04 1.60e+03 1.41e-01 3.01e+03 1.63e+04 0
20 1.00e+04 1.40e+03 1.37e-01 2.77e+03 1.29e+04 1
21 1.00e+04 1.41e+03 1.28e-01 2.69e+03 1.34e+04 2
22 1.00e+04 1.31e+03 1.36e-01 2.68e+03 1.13e+04 2
23 1.00e+04 1.27e+03 1.31e-01 2.58e+03 1.13e+04 2
24 1.00e+04 1.19e+03 1.31e-01 2.50e+03 1.07e+04 2
25 1.00e+04 1.15e+03 1.29e-01 2.44e+03 1.30e+04 2
26 1.00e+04 8.66e+02 1.51e-01 2.38e+03 1.27e+04 1
27 1.00e+04 7.35e+02 1.38e-01 2.12e+03 1.09e+04 1
28 1.00e+04 6.47e+02 1.38e-01 2.03e+03 1.48e+04 1
29 1.00e+04 5.91e+02 1.24e-01 1.83e+03 1.37e+04 2
30 1.00e+04 5.73e+02 1.12e-01 1.70e+03 1.48e+04 2
31 1.00e+04 5.53e+02 1.11e-01 1.66e+03 1.37e+04 3
32 1.00e+04 4.90e+02 1.05e-01 1.54e+03 1.08e+04 2
33 1.00e+04 4.80e+02 1.02e-01 1.50e+03 1.07e+04 4
34 1.00e+04 4.49e+02 1.01e-01 1.46e+03 1.08e+04 3
35 1.00e+04 4.46e+02 9.85e-02 1.43e+03 1.11e+04 4
36 1.00e+04 4.07e+02 9.82e-02 1.39e+03 1.08e+04 2
37 1.00e+04 3.94e+02 9.13e-02 1.31e+03 1.02e+04 4
38 1.00e+04 3.83e+02 8.70e-02 1.25e+03 1.02e+04 4
39 1.00e+04 3.70e+02 8.27e-02 1.20e+03 9.43e+03 3
40 1.00e+04 3.55e+02 7.50e-02 1.10e+03 9.01e+03 3
41 1.00e+04 3.53e+02 7.00e-02 1.05e+03 9.89e+03 4
42 1.00e+04 3.09e+02 6.55e-02 9.64e+02 9.01e+03 2
43 1.00e+04 2.95e+02 6.23e-02 9.18e+02 9.05e+03 4
44 1.00e+04 2.94e+02 5.98e-02 8.92e+02 8.98e+03 3
45 1.00e+04 2.82e+02 5.36e-02 8.18e+02 8.34e+03 4
46 1.00e+04 2.59e+02 5.03e-02 7.63e+02 9.01e+03 2
47 1.00e+04 2.49e+02 4.68e-02 7.17e+02 8.76e+03 4
48 1.00e+04 2.48e+02 4.49e-02 6.97e+02 8.17e+03 4
49 1.00e+04 2.35e+02 4.38e-02 6.72e+02 8.15e+03 3
50 1.00e+04 2.32e+02 4.27e-02 6.58e+02 8.01e+03 2
51 1.00e+04 2.13e+02 4.38e-02 6.51e+02 8.01e+03 3
52 1.00e+04 2.11e+02 4.35e-02 6.46e+02 8.55e+03 4
53 1.00e+04 2.15e+02 4.24e-02 6.39e+02 8.12e+03 4
54 1.00e+04 2.06e+02 4.25e-02 6.31e+02 8.28e+03 4
55 1.00e+04 2.00e+02 4.19e-02 6.18e+02 7.96e+03 4
56 1.00e+04 1.98e+02 4.07e-02 6.05e+02 7.50e+03 5
57 1.00e+04 1.97e+02 3.99e-02 5.96e+02 7.05e+03 5
58 1.00e+04 1.95e+02 3.92e-02 5.87e+02 7.04e+03 5
59 1.00e+04 1.93e+02 3.89e-02 5.82e+02 6.43e+03 4
60 1.00e+04 1.90e+02 3.91e-02 5.81e+02 6.84e+03 4
61 1.00e+04 1.83e+02 3.90e-02 5.74e+02 8.08e+03 4
62 1.00e+04 1.81e+02 3.79e-02 5.59e+02 7.32e+03 5
63 1.00e+04 1.76e+02 3.74e-02 5.50e+02 7.18e+03 3
64 1.00e+04 1.73e+02 3.67e-02 5.40e+02 7.03e+03 5
65 1.00e+04 1.69e+02 3.57e-02 5.26e+02 6.94e+03 3
66 1.00e+04 1.67e+02 3.52e-02 5.19e+02 6.49e+03 5
67 1.00e+04 1.38e+02 3.63e-02 5.01e+02 6.07e+03 1 Skip BFGS
68 1.00e+04 1.35e+02 3.55e-02 4.90e+02 6.11e+03 4
69 1.00e+04 1.35e+02 3.50e-02 4.86e+02 5.74e+03 5
70 1.00e+04 1.33e+02 3.47e-02 4.80e+02 5.46e+03 5
71 1.00e+04 1.34e+02 3.45e-02 4.79e+02 5.84e+03 4
72 1.00e+04 1.32e+02 3.37e-02 4.69e+02 5.59e+03 5 Skip BFGS
73 1.00e+04 1.30e+02 3.35e-02 4.66e+02 5.63e+03 5
74 1.00e+04 1.29e+02 3.34e-02 4.63e+02 5.66e+03 5
75 1.00e+04 1.27e+02 3.34e-02 4.61e+02 5.58e+03 4
76 1.00e+04 1.18e+02 3.36e-02 4.54e+02 6.20e+03 2
77 1.00e+04 1.20e+02 3.34e-02 4.54e+02 6.38e+03 4
78 1.00e+04 1.16e+02 3.33e-02 4.49e+02 6.07e+03 4
79 1.00e+04 1.13e+02 3.35e-02 4.47e+02 6.08e+03 4
80 1.00e+04 1.07e+02 3.31e-02 4.38e+02 5.39e+03 3
81 1.00e+04 1.07e+02 3.27e-02 4.34e+02 5.09e+03 6
82 1.00e+04 1.04e+02 3.28e-02 4.33e+02 4.86e+03 4 Skip BFGS
83 1.00e+04 1.04e+02 3.27e-02 4.31e+02 5.21e+03 5
84 1.00e+04 1.01e+02 3.27e-02 4.28e+02 5.10e+03 3
85 1.00e+04 1.00e+02 3.28e-02 4.28e+02 5.35e+03 5
86 1.00e+04 9.97e+01 3.26e-02 4.26e+02 5.11e+03 5
87 1.00e+04 9.98e+01 3.25e-02 4.25e+02 5.05e+03 5
88 1.00e+04 9.60e+01 3.29e-02 4.25e+02 5.06e+03 2 Skip BFGS
89 1.00e+04 9.58e+01 3.23e-02 4.19e+02 4.65e+03 6
90 1.00e+04 9.65e+01 3.20e-02 4.17e+02 4.48e+03 4 Skip BFGS
91 1.00e+04 9.51e+01 3.17e-02 4.12e+02 4.16e+03 5
92 1.00e+04 9.49e+01 3.15e-02 4.10e+02 3.98e+03 6
93 1.00e+04 9.45e+01 3.15e-02 4.09e+02 3.83e+03 6
94 1.00e+04 9.44e+01 3.13e-02 4.08e+02 3.56e+03 6 Skip BFGS
95 1.00e+04 9.41e+01 3.13e-02 4.07e+02 3.52e+03 5
96 1.00e+04 9.23e+01 3.14e-02 4.06e+02 3.84e+03 4
97 1.00e+04 9.18e+01 3.13e-02 4.05e+02 3.66e+03 6
98 1.00e+04 9.00e+01 3.10e-02 4.01e+02 3.27e+03 4 Skip BFGS
99 1.00e+04 8.97e+01 3.09e-02 3.99e+02 3.02e+03 6
100 1.00e+04 8.92e+01 3.09e-02 3.98e+02 3.03e+03 5
------------------------- STOP! -------------------------
1 : |fc-fOld| = 6.4978e-01 <= tolF*(1+|f0|) = 1.0009e+04
1 : |xc-x_last| = 1.7763e-03 <= tolX*(1+|x0|) = 1.0000e-01
0 : |proj(x-g)-x| = 3.0323e+03 <= tolG = 1.0000e-01
0 : |proj(x-g)-x| = 3.0323e+03 <= 1e3*eps = 1.0000e-02
1 : maxIter = 100 <= iter = 100
------------------------- DONE! -------------------------
The callback on the InexactGaussNewton Optimization was replaced.
SimPEG.InvProblem will set Regularization.mref to m0.
SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.
***Done using same Solver and solverOpts as the problem***
model has any nan: 0
============================ Inexact Gauss Newton ============================
# beta phi_d phi_m f |proj(x-g)-x| LS Comment
-----------------------------------------------------------------------------
x0 has any nan: 0
/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/SimPEG/directives/directives.py:60: UserWarning: InversionDirective TargetMisfit has switched to a new inversion.
warnings.warn(
/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/pymatsolver/direct.py:23: PardisoTypeConversionWarning: Converting dia_matrix matrix to CSR format, will slow down.
self.solver = MKLPardisoSolver(
/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/pymatsolver/direct.py:73: PardisoTypeConversionWarning: Converting dia_matrix matrix to CSR format, will slow down.
self.solver.refactor(self.A)
0 1.00e+04 1.00e+05 9.39e-03 1.00e+05 1.26e+06 0
1 1.00e+04 6.29e+04 2.35e-02 6.32e+04 7.21e+04 0
2 1.00e+04 3.06e+04 7.37e-02 3.13e+04 2.79e+04 0
3 1.00e+04 1.84e+04 8.77e-02 1.93e+04 2.06e+04 0 Skip BFGS
4 1.00e+04 1.39e+04 1.07e-01 1.50e+04 2.82e+04 0
5 1.00e+04 1.15e+04 7.35e-02 1.22e+04 1.94e+04 0
6 1.00e+04 8.65e+03 1.12e-01 9.77e+03 1.70e+04 0
7 1.00e+04 6.15e+03 1.32e-01 7.46e+03 1.38e+04 0 Skip BFGS
8 1.00e+04 4.97e+03 1.24e-01 6.21e+03 1.47e+04 0 Skip BFGS
9 1.00e+04 4.78e+03 1.16e-01 5.93e+03 1.97e+04 0
10 1.00e+04 3.96e+03 8.94e-02 4.85e+03 1.60e+04 0
11 1.00e+04 3.59e+03 8.02e-02 4.39e+03 1.26e+04 1
12 1.00e+04 3.25e+03 1.03e-01 4.28e+03 1.42e+04 0
13 1.00e+04 2.93e+03 8.58e-02 3.79e+03 1.53e+04 0
14 1.00e+04 2.71e+03 8.59e-02 3.57e+03 1.45e+04 0
15 1.00e+04 1.73e+03 1.55e-01 3.28e+03 1.86e+04 0
16 1.00e+04 1.50e+03 1.37e-01 2.87e+03 1.47e+04 2
17 1.00e+04 1.44e+03 1.13e-01 2.57e+03 1.36e+04 3
18 1.00e+04 1.37e+03 1.14e-01 2.51e+03 1.28e+04 3
19 1.00e+04 1.27e+03 1.07e-01 2.34e+03 1.18e+04 1
20 1.00e+04 1.08e+03 1.11e-01 2.19e+03 1.25e+04 1
21 1.00e+04 9.80e+02 1.08e-01 2.06e+03 1.16e+04 2
22 1.00e+04 9.41e+02 1.03e-01 1.97e+03 1.02e+04 2
23 1.00e+04 9.30e+02 9.48e-02 1.88e+03 1.07e+04 2
24 1.00e+04 8.91e+02 8.98e-02 1.79e+03 1.05e+04 3
25 1.00e+04 8.78e+02 8.45e-02 1.72e+03 1.14e+04 3
26 1.00e+04 7.35e+02 8.09e-02 1.54e+03 1.03e+04 1
27 1.00e+04 5.95e+02 8.33e-02 1.43e+03 1.35e+04 1
28 1.00e+04 5.47e+02 8.20e-02 1.37e+03 1.26e+04 3
29 1.00e+04 5.33e+02 6.43e-02 1.18e+03 1.16e+04 3
30 1.00e+04 4.85e+02 6.28e-02 1.11e+03 9.95e+03 2
31 1.00e+04 4.57e+02 6.41e-02 1.10e+03 1.02e+04 3
32 1.00e+04 4.26e+02 5.81e-02 1.01e+03 1.10e+04 1
33 1.00e+04 4.20e+02 5.33e-02 9.53e+02 1.08e+04 4
34 1.00e+04 4.32e+02 4.77e-02 9.09e+02 1.08e+04 4
35 1.00e+04 3.76e+02 5.18e-02 8.94e+02 1.03e+04 2
36 1.00e+04 3.36e+02 5.47e-02 8.83e+02 9.95e+03 3
37 1.00e+04 3.35e+02 5.04e-02 8.38e+02 9.60e+03 4
38 1.00e+04 3.04e+02 5.14e-02 8.18e+02 9.76e+03 3
39 1.00e+04 3.00e+02 4.73e-02 7.73e+02 9.10e+03 4
40 1.00e+04 2.96e+02 4.47e-02 7.43e+02 9.09e+03 5
41 1.00e+04 2.89e+02 4.32e-02 7.21e+02 9.00e+03 3
42 1.00e+04 2.77e+02 4.25e-02 7.02e+02 8.85e+03 4
43 1.00e+04 2.68e+02 4.10e-02 6.78e+02 9.09e+03 4
44 1.00e+04 2.65e+02 3.98e-02 6.63e+02 8.61e+03 5
45 1.00e+04 2.51e+02 4.04e-02 6.54e+02 8.29e+03 3
46 1.00e+04 2.44e+02 3.95e-02 6.39e+02 8.27e+03 4
47 1.00e+04 2.26e+02 3.83e-02 6.09e+02 8.02e+03 2
48 1.00e+04 2.01e+02 3.55e-02 5.56e+02 8.71e+03 2
49 1.00e+04 1.93e+02 3.57e-02 5.50e+02 8.65e+03 4
50 1.00e+04 1.87e+02 3.42e-02 5.29e+02 7.83e+03 4
51 1.00e+04 1.84e+02 3.39e-02 5.23e+02 8.04e+03 4
52 1.00e+04 1.84e+02 3.37e-02 5.20e+02 8.40e+03 3
53 1.00e+04 1.74e+02 3.21e-02 4.95e+02 7.79e+03 4
54 1.00e+04 1.32e+02 3.60e-02 4.92e+02 8.65e+03 1
55 1.00e+04 1.21e+02 2.98e-02 4.19e+02 7.22e+03 3
56 1.00e+04 1.17e+02 2.93e-02 4.10e+02 7.61e+03 4
57 1.00e+04 1.16e+02 2.81e-02 3.97e+02 7.12e+03 5
58 1.00e+04 1.16e+02 2.75e-02 3.91e+02 6.90e+03 5
59 1.00e+04 1.09e+02 2.78e-02 3.87e+02 7.01e+03 3
60 1.00e+04 9.97e+01 2.75e-02 3.75e+02 7.02e+03 3
61 1.00e+04 9.86e+01 2.42e-02 3.40e+02 6.54e+03 4
62 1.00e+04 9.58e+01 2.29e-02 3.25e+02 9.40e+03 0 Skip BFGS
63 1.00e+04 9.58e+01 2.26e-02 3.22e+02 9.24e+03 6
64 1.00e+04 9.31e+01 2.21e-02 3.14e+02 8.85e+03 5
65 1.00e+04 8.90e+01 2.25e-02 3.14e+02 9.64e+03 3 Skip BFGS
66 1.00e+04 8.08e+01 2.28e-02 3.09e+02 7.88e+03 2
67 1.00e+04 8.15e+01 2.26e-02 3.08e+02 7.66e+03 5
68 1.00e+04 7.91e+01 2.21e-02 3.00e+02 7.23e+03 5
69 1.00e+04 7.43e+01 2.23e-02 2.97e+02 7.78e+03 3
70 1.00e+04 7.52e+01 2.14e-02 2.89e+02 8.75e+03 3
71 1.00e+04 7.07e+01 2.13e-02 2.84e+02 7.90e+03 4
72 1.00e+04 7.00e+01 2.11e-02 2.81e+02 7.84e+03 5
73 1.00e+04 6.94e+01 2.11e-02 2.80e+02 8.15e+03 4
74 1.00e+04 6.70e+01 2.10e-02 2.77e+02 7.96e+03 5
75 1.00e+04 6.51e+01 2.01e-02 2.66e+02 7.09e+03 3
76 1.00e+04 6.36e+01 2.01e-02 2.64e+02 7.20e+03 5
77 1.00e+04 5.46e+01 2.07e-02 2.61e+02 7.72e+03 2
78 1.00e+04 5.20e+01 2.07e-02 2.59e+02 7.43e+03 4
79 1.00e+04 5.15e+01 2.03e-02 2.54e+02 6.69e+03 5
80 1.00e+04 5.12e+01 2.00e-02 2.51e+02 6.79e+03 6
81 1.00e+04 5.09e+01 1.97e-02 2.48e+02 6.47e+03 5 Skip BFGS
82 1.00e+04 5.03e+01 1.95e-02 2.46e+02 6.51e+03 5
83 1.00e+04 4.99e+01 1.93e-02 2.43e+02 6.14e+03 6
84 1.00e+04 4.95e+01 1.93e-02 2.42e+02 6.06e+03 6
85 1.00e+04 4.88e+01 1.86e-02 2.35e+02 5.90e+03 5 Skip BFGS
86 1.00e+04 4.84e+01 1.82e-02 2.30e+02 5.63e+03 6
87 1.00e+04 4.70e+01 1.80e-02 2.27e+02 5.76e+03 4 Skip BFGS
88 1.00e+04 4.63e+01 1.79e-02 2.26e+02 5.56e+03 5
89 1.00e+04 4.48e+01 1.80e-02 2.25e+02 5.59e+03 4
90 1.00e+04 4.42e+01 1.80e-02 2.24e+02 5.69e+03 5
91 1.00e+04 4.14e+01 1.76e-02 2.18e+02 5.39e+03 3
92 1.00e+04 4.08e+01 1.75e-02 2.16e+02 5.64e+03 5
93 1.00e+04 4.04e+01 1.67e-02 2.08e+02 4.63e+03 6
94 1.00e+04 3.94e+01 1.62e-02 2.01e+02 3.99e+03 5 Skip BFGS
95 1.00e+04 3.92e+01 1.60e-02 1.99e+02 3.65e+03 6
96 1.00e+04 3.88e+01 1.60e-02 1.99e+02 3.85e+03 5 Skip BFGS
97 1.00e+04 3.85e+01 1.59e-02 1.98e+02 3.54e+03 6
98 1.00e+04 3.84e+01 1.59e-02 1.97e+02 3.41e+03 6
99 1.00e+04 3.79e+01 1.59e-02 1.97e+02 3.50e+03 5
100 1.00e+04 3.75e+01 1.58e-02 1.96e+02 3.53e+03 5 Skip BFGS
------------------------- STOP! -------------------------
1 : |fc-fOld| = 7.2861e-01 <= tolF*(1+|f0|) = 1.0009e+04
1 : |xc-x_last| = 2.0508e-03 <= tolX*(1+|x0|) = 1.0000e-01
0 : |proj(x-g)-x| = 3.5288e+03 <= tolG = 1.0000e-01
0 : |proj(x-g)-x| = 3.5288e+03 <= 1e3*eps = 1.0000e-02
1 : maxIter = 100 <= iter = 100
------------------------- DONE! -------------------------
/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/SimPEG/directives/directives.py:60: UserWarning: InversionDirective TargetMisfit has switched to a new inversion.
warnings.warn(
/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/pymatsolver/direct.py:23: PardisoTypeConversionWarning: Converting dia_matrix matrix to CSR format, will slow down.
self.solver = MKLPardisoSolver(
/Users/u0102388/miniconda3/envs/rosetta/lib/python3.8/site-packages/pymatsolver/direct.py:73: PardisoTypeConversionWarning: Converting dia_matrix matrix to CSR format, will slow down.
self.solver.refactor(self.A)
The callback on the InexactGaussNewton Optimization was replaced.
SimPEG.InvProblem will set Regularization.mref to m0.
SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.
***Done using same Solver and solverOpts as the problem***
model has any nan: 0
============================ Inexact Gauss Newton ============================
# beta phi_d phi_m f |proj(x-g)-x| LS Comment
-----------------------------------------------------------------------------
x0 has any nan: 0
0 1.00e+04 1.00e+05 9.58e-03 1.00e+05 1.26e+06 0 Skip BFGS
1 1.00e+04 6.29e+04 1.99e-02 6.31e+04 7.15e+04 0 Skip BFGS
2 1.00e+04 3.06e+04 7.97e-02 3.14e+04 2.78e+04 0
3 1.00e+04 1.85e+04 1.09e-01 1.96e+04 2.10e+04 0 Skip BFGS
4 1.00e+04 1.42e+04 1.26e-01 1.55e+04 2.84e+04 0
5 1.00e+04 1.17e+04 9.14e-02 1.26e+04 2.00e+04 0
6 1.00e+04 8.82e+03 1.25e-01 1.01e+04 1.74e+04 0
7 1.00e+04 6.17e+03 1.37e-01 7.55e+03 1.46e+04 0 Skip BFGS
8 1.00e+04 4.98e+03 1.16e-01 6.14e+03 1.61e+04 0 Skip BFGS
9 1.00e+04 4.68e+03 1.08e-01 5.76e+03 2.03e+04 0
10 1.00e+04 3.97e+03 7.60e-02 4.73e+03 1.74e+04 0
11 1.00e+04 3.65e+03 6.43e-02 4.29e+03 1.47e+04 0
12 1.00e+04 3.32e+03 8.02e-02 4.12e+03 1.44e+04 0
13 1.00e+04 3.12e+03 6.44e-02 3.76e+03 1.23e+04 0
14 1.00e+04 2.54e+03 1.16e-01 3.71e+03 1.73e+04 0
15 1.00e+04 2.01e+03 1.23e-01 3.25e+03 1.30e+04 0
16 1.00e+04 1.98e+03 9.39e-02 2.91e+03 1.36e+04 1
17 1.00e+04 1.55e+03 1.36e-01 2.91e+03 1.49e+04 0
18 1.00e+04 1.31e+03 1.21e-01 2.52e+03 1.03e+04 1
19 1.00e+04 1.31e+03 1.11e-01 2.42e+03 1.11e+04 1
20 1.00e+04 1.26e+03 1.15e-01 2.41e+03 1.14e+04 2
21 1.00e+04 1.22e+03 1.09e-01 2.31e+03 1.12e+04 2
22 1.00e+04 1.08e+03 1.15e-01 2.24e+03 9.90e+03 1
23 1.00e+04 1.02e+03 1.16e-01 2.19e+03 1.17e+04 1
24 1.00e+04 9.68e+02 1.10e-01 2.07e+03 1.02e+04 1
25 1.00e+04 9.83e+02 1.07e-01 2.05e+03 1.04e+04 3
26 1.00e+04 8.10e+02 1.02e-01 1.83e+03 9.95e+03 2
27 1.00e+04 7.07e+02 9.70e-02 1.68e+03 1.19e+04 1
28 1.00e+04 5.73e+02 9.91e-02 1.56e+03 1.13e+04 1
29 1.00e+04 5.12e+02 1.01e-01 1.53e+03 1.02e+04 2
30 1.00e+04 4.98e+02 8.85e-02 1.38e+03 9.62e+03 3
31 1.00e+04 4.96e+02 8.14e-02 1.31e+03 9.53e+03 3
32 1.00e+04 4.96e+02 7.59e-02 1.26e+03 9.37e+03 3
33 1.00e+04 4.78e+02 6.52e-02 1.13e+03 9.57e+03 2
34 1.00e+04 4.42e+02 6.05e-02 1.05e+03 8.61e+03 3
35 1.00e+04 4.36e+02 5.77e-02 1.01e+03 8.00e+03 5
36 1.00e+04 4.05e+02 5.92e-02 9.96e+02 9.00e+03 2
37 1.00e+04 3.90e+02 5.56e-02 9.46e+02 8.70e+03 4
38 1.00e+04 3.55e+02 5.84e-02 9.38e+02 8.81e+03 2
39 1.00e+04 3.46e+02 5.81e-02 9.26e+02 8.56e+03 4
40 1.00e+04 3.43e+02 5.43e-02 8.86e+02 8.39e+03 4
41 1.00e+04 3.50e+02 5.26e-02 8.76e+02 8.98e+03 3
42 1.00e+04 3.17e+02 5.17e-02 8.34e+02 8.68e+03 3
43 1.00e+04 3.13e+02 4.85e-02 7.98e+02 8.33e+03 5
44 1.00e+04 3.12e+02 4.79e-02 7.91e+02 8.78e+03 3
45 1.00e+04 3.01e+02 4.49e-02 7.50e+02 8.18e+03 4
46 1.00e+04 2.56e+02 4.18e-02 6.73e+02 7.66e+03 2
47 1.00e+04 2.53e+02 4.07e-02 6.60e+02 7.74e+03 5
48 1.00e+04 2.37e+02 3.96e-02 6.33e+02 7.22e+03 3
49 1.00e+04 2.36e+02 3.85e-02 6.21e+02 6.89e+03 5
50 1.00e+04 2.35e+02 3.86e-02 6.20e+02 7.25e+03 5
51 1.00e+04 2.29e+02 3.83e-02 6.12e+02 7.41e+03 4
52 1.00e+04 2.26e+02 3.70e-02 5.96e+02 6.95e+03 4
53 1.00e+04 2.18e+02 3.55e-02 5.72e+02 6.89e+03 4
54 1.00e+04 2.14e+02 3.50e-02 5.64e+02 6.45e+03 4
55 1.00e+04 2.14e+02 3.39e-02 5.53e+02 6.17e+03 4
56 1.00e+04 1.97e+02 3.49e-02 5.45e+02 6.39e+03 2
57 1.00e+04 1.89e+02 3.35e-02 5.23e+02 6.34e+03 2
58 1.00e+04 1.53e+02 3.39e-02 4.92e+02 7.54e+03 0
59 1.00e+04 1.21e+02 3.42e-02 4.63e+02 9.42e+03 0
60 1.00e+04 1.01e+02 3.43e-02 4.45e+02 6.85e+03 2
61 1.00e+04 1.01e+02 3.36e-02 4.37e+02 6.11e+03 4
62 1.00e+04 1.01e+02 3.32e-02 4.33e+02 5.79e+03 5
63 1.00e+04 9.93e+01 3.30e-02 4.29e+02 6.07e+03 4
64 1.00e+04 9.71e+01 3.30e-02 4.27e+02 5.95e+03 3
65 1.00e+04 9.46e+01 3.23e-02 4.18e+02 6.66e+03 2
66 1.00e+04 8.67e+01 3.29e-02 4.16e+02 6.11e+03 2
67 1.00e+04 8.55e+01 3.26e-02 4.12e+02 5.83e+03 5
68 1.00e+04 8.54e+01 3.24e-02 4.10e+02 6.05e+03 4
69 1.00e+04 8.56e+01 3.22e-02 4.08e+02 5.54e+03 5
70 1.00e+04 8.34e+01 3.21e-02 4.05e+02 6.26e+03 4
71 1.00e+04 8.23e+01 3.22e-02 4.04e+02 6.08e+03 4 Skip BFGS
72 1.00e+04 8.13e+01 3.19e-02 4.00e+02 6.22e+03 5
73 1.00e+04 8.10e+01 3.18e-02 3.99e+02 5.90e+03 5
74 1.00e+04 8.07e+01 3.12e-02 3.92e+02 5.51e+03 6
75 1.00e+04 8.03e+01 3.11e-02 3.91e+02 5.21e+03 5
76 1.00e+04 8.00e+01 3.10e-02 3.90e+02 5.22e+03 6
77 1.00e+04 7.91e+01 3.09e-02 3.88e+02 5.23e+03 4
78 1.00e+04 7.81e+01 3.09e-02 3.87e+02 5.10e+03 5
79 1.00e+04 7.80e+01 3.07e-02 3.85e+02 5.13e+03 5
80 1.00e+04 7.68e+01 3.06e-02 3.83e+02 4.88e+03 5
81 1.00e+04 7.67e+01 3.05e-02 3.81e+02 4.42e+03 5
82 1.00e+04 7.55e+01 3.05e-02 3.80e+02 4.76e+03 5
83 1.00e+04 7.51e+01 3.03e-02 3.79e+02 4.72e+03 5 Skip BFGS
84 1.00e+04 7.46e+01 3.02e-02 3.77e+02 4.53e+03 6
85 1.00e+04 7.42e+01 2.99e-02 3.73e+02 4.33e+03 5 Skip BFGS
86 1.00e+04 7.40e+01 2.99e-02 3.73e+02 4.49e+03 5
87 1.00e+04 7.36e+01 2.96e-02 3.70e+02 4.01e+03 6
88 1.00e+04 7.35e+01 2.95e-02 3.69e+02 3.92e+03 6
89 1.00e+04 7.33e+01 2.95e-02 3.69e+02 3.94e+03 5 Skip BFGS
90 1.00e+04 7.32e+01 2.94e-02 3.67e+02 3.89e+03 6
91 1.00e+04 7.28e+01 2.94e-02 3.67e+02 4.24e+03 4
92 1.00e+04 7.23e+01 2.92e-02 3.65e+02 3.91e+03 6
93 1.00e+04 7.19e+01 2.93e-02 3.64e+02 4.09e+03 4
94 1.00e+04 7.07e+01 2.92e-02 3.63e+02 4.09e+03 5
95 1.00e+04 7.00e+01 2.92e-02 3.62e+02 4.74e+03 3
96 1.00e+04 6.89e+01 2.89e-02 3.58e+02 4.36e+03 5
97 1.00e+04 6.88e+01 2.88e-02 3.57e+02 4.17e+03 6
98 1.00e+04 6.85e+01 2.88e-02 3.57e+02 4.08e+03 5 Skip BFGS
99 1.00e+04 6.84e+01 2.87e-02 3.56e+02 4.01e+03 6
100 1.00e+04 6.82e+01 2.87e-02 3.55e+02 3.99e+03 4 Skip BFGS
------------------------- STOP! -------------------------
1 : |fc-fOld| = 6.9116e-01 <= tolF*(1+|f0|) = 1.0010e+04
1 : |xc-x_last| = 3.9399e-03 <= tolX*(1+|x0|) = 1.0000e-01
0 : |proj(x-g)-x| = 3.9878e+03 <= tolG = 1.0000e-01
0 : |proj(x-g)-x| = 3.9878e+03 <= 1e3*eps = 1.0000e-02
1 : maxIter = 100 <= iter = 100
------------------------- DONE! -------------------------
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