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D Fournier b3ceccf303 Finish script to extract 2D model from 3D mesh and write DCIP files 
Compare SimPEG vs DCIP2D vs DCIP3D on Mt Isa synthetic model
Invert Mt Isa synthetic 2D line. Dipole-Dipole sucks... need another setup/
2015-12-08 17:31:19 -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:12/08/2015 17:11:12
Reading input file: dcinv2d.inp
----------------------------------------------
OBS LOC_X FWR_3D_2_2D.dat
MESH FILE Mesh_2D.msh
CHIFACT 10
TOPO DEFAULT
INIT_MOD DEFAULT
REF_MOD VALUE 1e-1
ALPHA DEFAULT
WEIGHT DEFAULT
STORE_ALL_MODELS TRUE
INVMODE SVD
USE_MREF TRUE
----------------------------------------------
maximum # of iterations: 100
data were read from: FWR_3D_2_2D.dat
# of current locations: 29
# of data: 182
chifact: 1.00000E+01
target misfit: 1.82000E+03
mesh was read from: Mesh_2D.msh
# of cells: 91 x 45
total # of cells: 4095
# of active cells: 4095
# of unique data locations: 29
# 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-01
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: 57 + 3 + 1 = 61
init cpu time: 0:00:00.19
initial misfit = 1.20080E+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
5.39457E+02 5.41076E+04
6.78941E+02 6.29718E+04
1.49367E+03 8.32498E+04
2.98734E+03 9.37811E+04
chosen beta = 6.31603E+02
target misfit = 6.00398E+04
achieved misfit = 6.03730E+04
model norm = 1.88364E+01
misfit change = 4.97226E-01
model norm change = 0.00000E+00
norm comp Ws = 8.27046E+00
norm comp Wx = 5.77044E+00
norm comp Wz = 4.79552E+00
iter cpu time: 0:00:01.81
Iteration 2
beta vs. misfit:
beta misfit
1.57901E+02 1.71638E+05
3.15802E+02 2.71289E+04
6.31603E+02 2.38219E+04
7.86032E+02 3.17368E+04
1.26321E+03 5.04487E+04
chosen beta = 7.56581E+02
target misfit = 3.01865E+04
achieved misfit = 3.02637E+04
model norm = 3.81202E+01
misfit change = 4.98721E-01
model norm change = 1.02375E+00
norm comp Ws = 1.70820E+01
norm comp Wx = 1.13965E+01
norm comp Wz = 9.64173E+00
iter cpu time: 0:00:01.86
Iteration 3
beta vs. misfit:
beta misfit
1.89145E+02 4.58243E+03
3.78290E+02 1.03135E+04
5.24901E+02 1.46009E+04
5.42867E+02 1.51069E+04
5.43753E+02 1.51317E+04
1.35938E+03 3.42157E+04
chosen beta = 5.43758E+02
target misfit = 1.51319E+04
achieved misfit = 1.51319E+04
model norm = 5.40288E+01
misfit change = 4.99999E-01
model norm change = 4.17327E-01
norm comp Ws = 2.41470E+01
norm comp Wx = 1.73570E+01
norm comp Wz = 1.25248E+01
iter cpu time: 0:00:02.34
Iteration 4
beta vs. misfit:
beta misfit
1.35940E+02 2.80648E+03
2.71879E+02 6.12611E+03
3.27929E+02 7.46284E+03
3.32230E+02 7.56392E+03
3.32316E+02 7.56593E+03
8.30790E+02 1.78551E+04
chosen beta = 3.32316E+02
target misfit = 7.56594E+03
achieved misfit = 7.56594E+03
model norm = 6.83143E+01
misfit change = 5.00000E-01
model norm change = 2.64406E-01
norm comp Ws = 2.94508E+01
norm comp Wx = 2.36755E+01
norm comp Wz = 1.51880E+01
iter cpu time: 0:00:02.17
Iteration 5
beta vs. misfit:
beta misfit
8.30790E+01 1.98945E+03
1.66158E+02 3.60086E+03
1.76017E+02 3.78526E+03
chosen beta = 1.75894E+02
target misfit = 3.78297E+03
achieved misfit = 3.78297E+03
model norm = 8.19887E+01
misfit change = 5.00000E-01
model norm change = 2.00169E-01
norm comp Ws = 3.30274E+01
norm comp Wx = 3.06964E+01
norm comp Wz = 1.82648E+01
iter cpu time: 0:00:01.77
Iteration 6
beta vs. misfit:
beta misfit
4.39734E+01 8.68659E+02
8.79468E+01 1.63366E+03
1.03290E+02 1.90175E+03
chosen beta = 1.02701E+02
target misfit = 1.89148E+03
achieved misfit = 1.89146E+03
model norm = 9.38829E+01
misfit change = 5.00006E-01
model norm change = 1.45071E-01
norm comp Ws = 3.64720E+01
norm comp Wx = 3.54646E+01
norm comp Wz = 2.19464E+01
iter cpu time: 0:00:01.96
Iteration 7
beta vs. misfit:
beta misfit
9.50870E+01 1.65955E+03
9.88206E+01 1.72560E+03
1.04154E+02 1.81997E+03
2.60385E+02 4.50983E+03
chosen beta = 1.04156E+02
target misfit = 1.82000E+03
achieved misfit = 1.82000E+03
model norm = 9.41343E+01
misfit change = 3.77808E-02
model norm change = 2.67721E-03
norm comp Ws = 3.58106E+01
norm comp Wx = 3.57249E+01
norm comp Wz = 2.25988E+01
iter cpu time: 0:00:02.14
Target misfit achieved. Minimizing model norm.
Iteration 8
beta vs. misfit:
beta misfit
1.04156E+02 1.70980E+03
1.04156E+02 1.70981E+03
1.10515E+02 1.81990E+03
1.10521E+02 1.82000E+03
2.76302E+02 4.63990E+03
chosen beta = 1.10521E+02
target misfit = 1.82000E+03
achieved misfit = 1.82000E+03
model norm = 9.39651E+01
misfit change = 3.30759E-07
model norm change = -1.79735E-03
norm comp Ws = 3.63303E+01
norm comp Wx = 3.53890E+01
norm comp Wz = 2.22458E+01
iter cpu time: 0:00:02.03
Exit at convergence.
Iterations performed: 8
total cpu time: 0:00:16.31
DCINV2D ended on:12/08/2015 17:11:28
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