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simpeg/simpegDCIP/Dev/Inv2D/dcinv2d.log
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D Fournier 2dfacdc904 Appended functions to BaseDC
2016-01-12 17:46:58 -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: 1/12/2016 16:42:18
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-02
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: 9
# of data: 45
chifact: 1.00000E+00
target misfit: 4.50000E+01
mesh was read from: Mesh_2D.msh
# of cells: 81 x 45
total # of cells: 3645
# of active cells: 3645
# of unique data locations: 9
# 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-02
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: 17 + 3 + 1 = 21
init cpu time: 0:00:00.18
initial misfit = 2.57080E+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
1.11493E+03 4.74485E+04
2.22985E+03 5.68364E+04
5.57463E+03 8.02516E+04
1.39366E+04 1.21526E+05
1.57750E+04 1.28279E+05
1.58488E+04 1.28536E+05
1.58499E+04 1.28540E+05
3.96247E+04 1.79392E+05
chosen beta = 1.58499E+04
target misfit = 1.28540E+05
achieved misfit = 1.28540E+05
model norm = 3.63297E+00
misfit change = 5.00000E-01
model norm change = 0.00000E+00
norm comp Ws = 2.88514E+00
norm comp Wx = 3.70226E-01
norm comp Wz = 3.77603E-01
iter cpu time: 0:00:01.20
Iteration 2
beta vs. misfit:
beta misfit
3.96247E+03 6.15053E+04
7.92494E+03 9.46707E+04
chosen beta = 4.25263E+03
target misfit = 6.42701E+04
achieved misfit = 6.42774E+04
model norm = 1.37169E+01
misfit change = 4.99943E-01
model norm change = 2.77567E+00
norm comp Ws = 1.06979E+01
norm comp Wx = 1.61088E+00
norm comp Wz = 1.40814E+00
iter cpu time: 0:00:00.78
Iteration 3
beta vs. misfit:
beta misfit
1.06316E+03 2.49833E+04
2.12631E+03 4.10044E+04
chosen beta = 1.51222E+03
target misfit = 3.21387E+04
achieved misfit = 3.17674E+04
model norm = 2.91306E+01
misfit change = 5.05777E-01
model norm change = 1.12370E+00
norm comp Ws = 2.19928E+01
norm comp Wx = 4.11507E+00
norm comp Wz = 3.02271E+00
iter cpu time: 0:00:01.00
Iteration 4
beta vs. misfit:
beta misfit
3.78054E+02 1.14051E+04
7.56108E+02 1.87488E+04
chosen beta = 6.00000E+02
target misfit = 1.58837E+04
achieved misfit = 1.56569E+04
model norm = 4.84312E+01
misfit change = 5.07137E-01
model norm change = 6.62554E-01
norm comp Ws = 3.49213E+01
norm comp Wx = 8.32898E+00
norm comp Wz = 5.18098E+00
iter cpu time: 0:00:00.86
Iteration 5
beta vs. misfit:
beta misfit
1.50000E+02 5.05151E+03
3.00000E+02 8.76679E+03
chosen beta = 2.60199E+02
target misfit = 7.82847E+03
achieved misfit = 7.79602E+03
model norm = 6.97577E+01
misfit change = 5.02073E-01
model norm change = 4.40344E-01
norm comp Ws = 4.74916E+01
norm comp Wx = 1.43993E+01
norm comp Wz = 7.86671E+00
iter cpu time: 0:00:00.88
Iteration 6
beta vs. misfit:
beta misfit
6.50498E+01 1.83324E+03
1.30100E+02 3.68412E+03
1.37599E+02 3.90387E+03
chosen beta = 1.37400E+02
target misfit = 3.89801E+03
achieved misfit = 3.89800E+03
model norm = 8.95751E+01
misfit change = 5.00001E-01
model norm change = 2.84090E-01
norm comp Ws = 5.75580E+01
norm comp Wx = 2.11440E+01
norm comp Wz = 1.08731E+01
iter cpu time: 0:00:00.81
Iteration 7
beta vs. misfit:
beta misfit
3.43499E+01 8.99667E+02
6.86999E+01 1.65819E+03
8.25106E+01 2.00613E+03
chosen beta = 8.02499E+01
target misfit = 1.94900E+03
achieved misfit = 1.94758E+03
model norm = 1.06566E+02
misfit change = 5.00364E-01
model norm change = 1.89686E-01
norm comp Ws = 6.57720E+01
norm comp Wx = 2.69488E+01
norm comp Wz = 1.38454E+01
iter cpu time: 0:00:00.89
Iteration 8
beta vs. misfit:
beta misfit
2.00625E+01 5.63946E+02
4.01250E+01 9.43655E+02
4.18598E+01 9.78372E+02
chosen beta = 4.16303E+01
target misfit = 9.73791E+02
achieved misfit = 9.73755E+02
model norm = 1.22904E+02
misfit change = 5.00019E-01
model norm change = 1.53314E-01
norm comp Ws = 7.39851E+01
norm comp Wx = 3.20341E+01
norm comp Wz = 1.68852E+01
iter cpu time: 0:00:00.91
Iteration 9
beta vs. misfit:
beta misfit
1.04076E+01 3.54740E+02
2.08152E+01 5.47348E+02
chosen beta = 1.72632E+01
target misfit = 4.86877E+02
achieved misfit = 4.81217E+02
model norm = 1.41185E+02
misfit change = 5.05813E-01
model norm change = 1.48742E-01
norm comp Ws = 8.27069E+01
norm comp Wx = 3.78831E+01
norm comp Wz = 2.05954E+01
iter cpu time: 0:00:00.78
Iteration 10
beta vs. misfit:
beta misfit
4.31579E+00 2.09965E+02
8.63158E+00 2.93092E+02
chosen beta = 5.72809E+00
target misfit = 2.40608E+02
achieved misfit = 2.34998E+02
model norm = 1.66836E+02
misfit change = 5.11659E-01
model norm change = 1.81682E-01
norm comp Ws = 9.43748E+01
norm comp Wx = 4.67077E+01
norm comp Wz = 2.57538E+01
iter cpu time: 0:00:00.66
Iteration 11
beta vs. misfit:
beta misfit
1.43202E+00 1.08765E+02
2.86405E+00 1.48776E+02
chosen beta = 1.69894E+00
target misfit = 1.17499E+02
achieved misfit = 1.13855E+02
model norm = 2.07214E+02
misfit change = 5.15505E-01
model norm change = 2.42021E-01
norm comp Ws = 1.10709E+02
norm comp Wx = 6.50107E+01
norm comp Wz = 3.14941E+01
iter cpu time: 0:00:00.81
Iteration 12
beta vs. misfit:
beta misfit
4.24735E-01 4.69288E+01
8.49470E-01 5.82083E+01
chosen beta = 7.90778E-01
target misfit = 5.69276E+01
achieved misfit = 5.61534E+01
model norm = 2.39426E+02
misfit change = 5.06799E-01
model norm change = 1.55454E-01
norm comp Ws = 1.22208E+02
norm comp Wx = 7.90861E+01
norm comp Wz = 3.81319E+01
iter cpu time: 0:00:00.72
Iteration 13
beta vs. misfit:
beta misfit
5.07840E-01 2.85576E+01
6.33710E-01 3.23824E+01
1.13145E+00 5.17204E+01
chosen beta = 9.52357E-01
target misfit = 4.50000E+01
achieved misfit = 4.40177E+01
model norm = 2.37754E+02
misfit change = 2.16117E-01
model norm change = -6.98309E-03
norm comp Ws = 1.26074E+02
norm comp Wx = 7.63269E+01
norm comp Wz = 3.53536E+01
iter cpu time: 0:00:01.02
Iteration 14
beta vs. misfit:
beta misfit
9.73609E-01 3.64390E+01
9.95336E-01 3.71506E+01
1.23871E+00 4.57613E+01
chosen beta = 1.21709E+00
target misfit = 4.50000E+01
achieved misfit = 4.49504E+01
model norm = 2.30537E+02
misfit change = -2.11895E-02
model norm change = -3.03571E-02
norm comp Ws = 1.26466E+02
norm comp Wx = 7.21243E+01
norm comp Wz = 3.19461E+01
iter cpu time: 0:00:01.21
Target misfit achieved. Minimizing model norm.
Iteration 15
beta vs. misfit:
beta misfit
1.21843E+00 3.86348E+01
1.21978E+00 3.86736E+01
1.44018E+00 4.53382E+01
chosen beta = 1.42896E+00
target misfit = 4.50000E+01
achieved misfit = 4.49850E+01
model norm = 2.28950E+02
misfit change = -7.69307E-04
model norm change = -6.88525E-03
norm comp Ws = 1.25759E+02
norm comp Wx = 6.90749E+01
norm comp Wz = 3.41159E+01
iter cpu time: 0:00:00.76
Exit at convergence.
Iterations performed: 15
total cpu time: 0:00:13.54
DCINV2D ended on: 1/12/2016 16:42:31
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