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D Fournier fc3893c72f Improved gradient plot and sections.
2015-12-15 11:59:44 -08:00

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Parallelized with OpenMP. # of threads: 4
DCIP2D - Version 5 (BETA) 20110811: DCINV2D
Developed by University of British Columbia
Geophysical Inversion Facility (UBC-GIF)
(C) Copyright 1992 - 2011, UBC-GIF,
Department of Earth and Ocean Sciences, UBC
http://www.eos.ubc.ca/research/ubcgif/
Distributed by:
Mira Geoscience Ltd.
DCINV2D started on:12/14/2015 14:39:32
Reading input file: dcinv2d.inp
----------------------------------------------
OBS LOC_X FWR_3D_2_2D.dat
MESH FILE Mesh_2D.msh
CHIFACT 1 100.000000
TOPO DEFAULT %s
INIT_MOD DEFAULT
REF_MOD VALUE 1.000000e-03
ALPHA DEFAULT
WEIGHT DEFAULT
STORE_ALL_MODELS FALSE
INVMODE SVD
USE_MREF TRUE
----------------------------------------------
maximum # of iterations: 100
data were read from: FWR_3D_2_2D.dat
# of current locations: 11
# of data: 65
chifact: 1.00000E+00
target misfit: 6.50000E+01
mesh was read from: Mesh_2D.msh
# of cells: 58 x 30
total # of cells: 1740
# of active cells: 1740
# of unique data locations: 11
# of wave values: 13
2.5000E-04 4.9901E-04 9.9606E-04 1.9882E-03 3.9685E-03 7.9213E-03 1.5811E-02 3.1560E-02 6.2996E-02 1.2574E-01 2.5099E-01 5.0099E-01 1.0000E+00
reference conductivity model is set to a constant: 1.000000E-03
initial model is set to the reference model.
using default length scales (Lx, Lz): ( 8.00000E+01, 8.00000E+01)
corresponding alpha (a_s, a_x, a_z): ( 1.56250E-04, 1.0000E+00, 1.0000E+00)
Using basis vectors and SVD.
reference model will be used in the derivative terms.
number of basis vectors: 21 + 3 + 1 = 25
init cpu time: 0:00:00.05
initial misfit = 2.29963E+05
init. model norm = 0.00000E+00
norm comp Ws = 0.00000E+00
norm comp Wx = 0.00000E+00
norm comp Wz = 0.00000E+00
Iteration 1
beta vs. misfit:
beta misfit
2.76242E+04 4.25611E+04
5.52485E+04 5.73807E+04
1.38121E+05 9.14695E+04
2.16516E+05 1.14020E+05
2.20257E+05 1.14925E+05
2.20495E+05 1.14982E+05
5.51237E+05 1.62414E+05
chosen beta = 2.20496E+05
target misfit = 1.14982E+05
achieved misfit = 1.14982E+05
model norm = 2.23402E-01
misfit change = 5.00000E-01
model norm change = 0.00000E+00
norm comp Ws = 1.20709E-01
norm comp Wx = 5.36664E-02
norm comp Wz = 4.90259E-02
iter cpu time: 0:00:00.35
Iteration 2
beta vs. misfit:
beta misfit
5.51240E+04 5.38776E+04
1.10248E+05 8.30234E+04
chosen beta = 6.11687E+04
target misfit = 5.74909E+04
achieved misfit = 5.74953E+04
model norm = 8.13417E-01
misfit change = 4.99961E-01
model norm change = 2.64105E+00
norm comp Ws = 4.21387E-01
norm comp Wx = 2.22769E-01
norm comp Wz = 1.69261E-01
iter cpu time: 0:00:00.19
Iteration 3
beta vs. misfit:
beta misfit
1.52922E+04 2.43015E+04
3.05844E+04 3.74992E+04
chosen beta = 2.00018E+04
target misfit = 2.87477E+04
achieved misfit = 2.84776E+04
model norm = 1.74401E+00
misfit change = 5.04697E-01
model norm change = 1.14405E+00
norm comp Ws = 8.31643E-01
norm comp Wx = 5.75399E-01
norm comp Wz = 3.36966E-01
iter cpu time: 0:00:00.45
Iteration 4
beta vs. misfit:
beta misfit
5.00045E+03 1.28642E+04
1.00009E+04 1.86842E+04
chosen beta = 6.03799E+03
target misfit = 1.42388E+04
achieved misfit = 1.41388E+04
model norm = 3.13263E+00
misfit change = 5.03513E-01
model norm change = 7.96223E-01
norm comp Ws = 1.28958E+00
norm comp Wx = 1.28927E+00
norm comp Wz = 5.53778E-01
iter cpu time: 0:00:00.21
Iteration 5
beta vs. misfit:
beta misfit
1.50950E+03 5.99773E+03
3.01900E+03 9.21452E+03
chosen beta = 1.96825E+03
target misfit = 7.06938E+03
achieved misfit = 7.12029E+03
model norm = 5.18734E+00
misfit change = 4.96399E-01
model norm change = 6.55905E-01
norm comp Ws = 1.74210E+00
norm comp Wx = 2.47066E+00
norm comp Wz = 9.74574E-01
iter cpu time: 0:00:00.27
Iteration 6
beta vs. misfit:
beta misfit
4.92062E+02 2.05275E+03
9.84123E+02 3.92917E+03
chosen beta = 8.85768E+02
target misfit = 3.56015E+03
achieved misfit = 3.58474E+03
model norm = 7.76393E+00
misfit change = 4.96546E-01
model norm change = 4.96708E-01
norm comp Ws = 2.19885E+00
norm comp Wx = 3.77400E+00
norm comp Wz = 1.79107E+00
iter cpu time: 0:00:00.21
Iteration 7
beta vs. misfit:
beta misfit
2.21442E+02 8.33133E+02
4.42884E+02 1.70498E+03
4.64839E+02 1.79178E+03
4.64988E+02 1.79237E+03
1.16247E+03 4.25206E+03
chosen beta = 4.64988E+02
target misfit = 1.79237E+03
achieved misfit = 1.79237E+03
model norm = 1.04147E+01
misfit change = 5.00000E-01
model norm change = 3.41416E-01
norm comp Ws = 2.65166E+00
norm comp Wx = 4.83319E+00
norm comp Wz = 2.92980E+00
iter cpu time: 0:00:00.26
Iteration 8
beta vs. misfit:
beta misfit
1.16247E+02 4.08040E+02
2.32494E+02 7.73743E+02
2.72599E+02 9.16098E+02
chosen beta = 2.67012E+02
target misfit = 8.96186E+02
achieved misfit = 8.95980E+02
model norm = 1.28008E+01
misfit change = 5.00115E-01
model norm change = 2.29117E-01
norm comp Ws = 3.12662E+00
norm comp Wx = 5.80666E+00
norm comp Wz = 3.86755E+00
iter cpu time: 0:00:00.30
Iteration 9
beta vs. misfit:
beta misfit
6.67531E+01 2.47097E+02
1.33506E+02 4.19229E+02
1.45642E+02 4.54732E+02
chosen beta = 1.43332E+02
target misfit = 4.47990E+02
achieved misfit = 4.47897E+02
model norm = 1.50386E+01
misfit change = 5.00103E-01
model norm change = 1.74818E-01
norm comp Ws = 3.56241E+00
norm comp Wx = 6.64092E+00
norm comp Wz = 4.83532E+00
iter cpu time: 0:00:00.21
Iteration 10
beta vs. misfit:
beta misfit
3.58330E+01 1.94330E+02
7.16660E+01 2.56470E+02
chosen beta = 5.10735E+01
target misfit = 2.23949E+02
achieved misfit = 2.17974E+02
model norm = 1.75231E+01
misfit change = 5.13339E-01
model norm change = 1.65205E-01
norm comp Ws = 4.09954E+00
norm comp Wx = 7.65264E+00
norm comp Wz = 5.77092E+00
iter cpu time: 0:00:00.26
Iteration 11
beta vs. misfit:
beta misfit
2.63809E+00 1.40839E+02
5.80380E+00 1.40006E+02
1.27684E+01 1.44718E+02
2.55367E+01 1.58122E+02
chosen beta = 5.80380E+00
target misfit = 1.08987E+02
achieved misfit = 1.40006E+02
model norm = 2.01006E+01
misfit change = 3.57693E-01
model norm change = 1.47090E-01
norm comp Ws = 5.01489E+00
norm comp Wx = 8.20406E+00
norm comp Wz = 6.88163E+00
iter cpu time: 0:00:00.22
Iteration 12
beta vs. misfit:
beta misfit
1.45095E+00 7.61897E+01
2.90190E+00 7.59815E+01
5.80380E+00 7.71909E+01
chosen beta = 2.90190E+00
target misfit = 7.00031E+01
achieved misfit = 7.59815E+01
model norm = 2.32752E+01
misfit change = 4.57299E-01
model norm change = 1.57934E-01
norm comp Ws = 5.70037E+00
norm comp Wx = 9.53924E+00
norm comp Wz = 8.03554E+00
iter cpu time: 0:00:00.23
Iteration 13
beta vs. misfit:
beta misfit
9.65320E-01 6.59663E+01
2.12370E+00 6.63626E+01
2.48250E+00 6.65339E+01
chosen beta = 2.12370E+00
target misfit = 6.50000E+01
achieved misfit = 6.63626E+01
model norm = 2.39048E+01
misfit change = 1.26594E-01
model norm change = 2.70511E-02
norm comp Ws = 5.80622E+00
norm comp Wx = 9.69831E+00
norm comp Wz = 8.40024E+00
iter cpu time: 0:00:00.26
Iteration 14
beta vs. misfit:
beta misfit
2.03739E+00 4.61300E+01
2.08010E+00 4.61580E+01
5.20024E+00 5.05536E+01
1.30006E+01 6.80030E+01
chosen beta = 1.13071E+01
target misfit = 6.50000E+01
achieved misfit = 6.38895E+01
model norm = 2.30277E+01
misfit change = 3.72669E-02
model norm change = -3.66901E-02
norm comp Ws = 5.35300E+00
norm comp Wx = 9.06543E+00
norm comp Wz = 8.60927E+00
iter cpu time: 0:00:00.30
Iteration 15
beta vs. misfit:
beta misfit
1.00060E+01 6.37285E+01
1.15036E+01 6.57179E+01
1.17036E+01 6.60002E+01
chosen beta = 1.09443E+01
target misfit = 6.50000E+01
achieved misfit = 6.49491E+01
model norm = 2.25675E+01
misfit change = -1.65848E-02
model norm change = -1.99856E-02
norm comp Ws = 5.25353E+00
norm comp Wx = 9.12332E+00
norm comp Wz = 8.19063E+00
iter cpu time: 0:00:00.45
Target misfit achieved. Minimizing model norm.
Iteration 16
beta vs. misfit:
beta misfit
1.09529E+01 6.22414E+01
1.09615E+01 6.22535E+01
1.30478E+01 6.53929E+01
chosen beta = 1.27723E+01
target misfit = 6.50000E+01
achieved misfit = 6.49555E+01
model norm = 2.25114E+01
misfit change = -9.87955E-05
model norm change = -2.48393E-03
norm comp Ws = 5.19089E+00
norm comp Wx = 9.13767E+00
norm comp Wz = 8.18286E+00
iter cpu time: 0:00:00.28
Exit at convergence.
Iterations performed: 16
total cpu time: 0:00:04.60
DCINV2D ended on:12/14/2015 14:39:36
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