wassname/simpeg
1
0
Fork 0
mirror of https://github.com/wassname/simpeg.git synced 2026-07-14 11:18:18 +08:00
Code Issues Packages Projects Releases Wiki Activity
Files
3d52778274b37aec3e678674885a429f5f641889
simpeg/simpegDCIP/Dev/Inv2D/dcinv2d.log
T
D Fournier 3d52778274 Finalize "2click" forward + inversion demo
2015-12-10 15:27:50 -08:00

369 lines
8.0 KiB
Plaintext
Raw Blame History

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:12/10/2015 14:20:13
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: 10
# of data: 54
chifact: 1.00000E+00
target misfit: 5.40000E+01
mesh was read from: Mesh_2D.msh
# of cells: 50 x 30
total # of cells: 1500
# of active cells: 1500
# of unique data locations: 10
# 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: 19 + 3 + 1 = 23
init cpu time: 0:00:00.04
initial misfit = 2.87480E+04
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
1.05827E+04 7.91147E+03
2.11653E+04 9.97641E+03
5.29133E+04 1.42234E+04
5.43732E+04 1.43748E+04
chosen beta = 5.43657E+04
target misfit = 1.43740E+04
achieved misfit = 1.43740E+04
model norm = 1.00462E-01
misfit change = 5.00000E-01
model norm change = 0.00000E+00
norm comp Ws = 5.19270E-02
norm comp Wx = 2.47955E-02
norm comp Wz = 2.37393E-02
iter cpu time: 0:00:00.64
Iteration 2
beta vs. misfit:
beta misfit
8.89064E+03 7.03214E+03
1.35914E+04 8.23443E+03
2.71829E+04 1.09665E+04
chosen beta = 9.42699E+03
target misfit = 7.18700E+03
achieved misfit = 7.18162E+03
model norm = 4.48909E-01
misfit change = 5.00374E-01
model norm change = 3.46846E+00
norm comp Ws = 1.84955E-01
norm comp Wx = 1.65870E-01
norm comp Wz = 9.80840E-02
iter cpu time: 0:00:00.40
Iteration 3
beta vs. misfit:
beta misfit
1.30271E+03 3.38723E+03
2.35675E+03 4.32491E+03
4.71349E+03 5.60434E+03
chosen beta = 1.50085E+03
target misfit = 3.59081E+03
achieved misfit = 3.60067E+03
model norm = 1.47576E+00
misfit change = 4.98627E-01
model norm change = 2.28744E+00
norm comp Ws = 4.08324E-01
norm comp Wx = 8.20762E-01
norm comp Wz = 2.46674E-01
iter cpu time: 0:00:00.49
Iteration 4
beta vs. misfit:
beta misfit
3.75212E+02 1.34820E+03
7.50424E+02 2.29809E+03
chosen beta = 5.46411E+02
target misfit = 1.80034E+03
achieved misfit = 1.83379E+03
model norm = 3.31412E+00
misfit change = 4.90710E-01
model norm change = 1.24570E+00
norm comp Ws = 6.83124E-01
norm comp Wx = 2.07548E+00
norm comp Wz = 5.55519E-01
iter cpu time: 0:00:00.20
Iteration 5
beta vs. misfit:
beta misfit
1.36603E+02 5.05265E+02
2.73206E+02 9.48634E+02
chosen beta = 2.63165E+02
target misfit = 9.16894E+02
achieved misfit = 9.16736E+02
model norm = 5.54836E+00
misfit change = 5.00086E-01
model norm change = 6.74157E-01
norm comp Ws = 8.54281E-01
norm comp Wx = 3.70325E+00
norm comp Wz = 9.90823E-01
iter cpu time: 0:00:00.17
Iteration 6
beta vs. misfit:
beta misfit
6.57912E+01 2.23512E+02
1.31582E+02 4.05775E+02
1.51607E+02 4.68975E+02
chosen beta = 1.48251E+02
target misfit = 4.58368E+02
achieved misfit = 4.58273E+02
model norm = 7.68792E+00
misfit change = 5.00103E-01
model norm change = 3.85621E-01
norm comp Ws = 1.03639E+00
norm comp Wx = 5.25876E+00
norm comp Wz = 1.39277E+00
iter cpu time: 0:00:00.29
Iteration 7
beta vs. misfit:
beta misfit
3.70626E+01 1.66504E+02
7.41253E+01 2.51726E+02
chosen beta = 6.33124E+01
target misfit = 2.29137E+02
achieved misfit = 2.25244E+02
model norm = 1.00149E+01
misfit change = 5.08495E-01
model norm change = 3.02681E-01
norm comp Ws = 1.04755E+00
norm comp Wx = 6.91957E+00
norm comp Wz = 2.04778E+00
iter cpu time: 0:00:00.34
Iteration 8
beta vs. misfit:
beta misfit
1.58281E+01 1.02052E+02
3.16562E+01 1.24062E+02
chosen beta = 2.24560E+01
target misfit = 1.12622E+02
achieved misfit = 1.09408E+02
model norm = 1.25018E+01
misfit change = 5.14267E-01
model norm change = 2.48315E-01
norm comp Ws = 1.21846E+00
norm comp Wx = 9.04398E+00
norm comp Wz = 2.23932E+00
iter cpu time: 0:00:00.17
Iteration 9
beta vs. misfit:
beta misfit
1.15992E+00 7.28154E+01
2.55182E+00 7.30977E+01
5.61400E+00 7.44520E+01
1.12280E+01 7.91248E+01
chosen beta = 2.55182E+00
target misfit = 5.47041E+01
achieved misfit = 7.30977E+01
model norm = 1.46799E+01
misfit change = 3.31881E-01
model norm change = 1.74228E-01
norm comp Ws = 1.27978E+00
norm comp Wx = 1.03955E+01
norm comp Wz = 3.00464E+00
iter cpu time: 0:00:00.17
Iteration 10
beta vs. misfit:
beta misfit
1.39261E+00 4.80711E+01
1.88512E+00 4.83598E+01
4.71280E+00 5.04589E+01
1.17820E+01 5.88251E+01
chosen beta = 7.06669E+00
target misfit = 5.40000E+01
achieved misfit = 5.27458E+01
model norm = 1.48886E+01
misfit change = 2.78420E-01
model norm change = 1.42172E-02
norm comp Ws = 1.42273E+00
norm comp Wx = 1.05086E+01
norm comp Wz = 2.95727E+00
iter cpu time: 0:00:00.39
Iteration 11
beta vs. misfit:
beta misfit
7.23472E+00 4.12130E+01
7.40675E+00 4.14256E+01
1.85169E+01 5.95740E+01
chosen beta = 1.44535E+01
target misfit = 5.40000E+01
achieved misfit = 5.20734E+01
model norm = 1.45035E+01
misfit change = 1.27476E-02
model norm change = -2.58673E-02
norm comp Ws = 1.38232E+00
norm comp Wx = 1.04192E+01
norm comp Wz = 2.70198E+00
iter cpu time: 0:00:00.26
Iteration 12
beta vs. misfit:
beta misfit
1.49882E+01 5.14497E+01
1.55427E+01 5.22662E+01
1.67582E+01 5.41032E+01
chosen beta = 1.66886E+01
target misfit = 5.40000E+01
achieved misfit = 5.39963E+01
model norm = 1.42758E+01
misfit change = -3.69261E-02
model norm change = -1.56990E-02
norm comp Ws = 1.40497E+00
norm comp Wx = 1.00935E+01
norm comp Wz = 2.77734E+00
iter cpu time: 0:00:00.17
Target misfit achieved. Minimizing model norm.
Iteration 13
beta vs. misfit:
beta misfit
1.66898E+01 5.35819E+01
1.66909E+01 5.35835E+01
1.69863E+01 5.40047E+01
chosen beta = 1.69830E+01
target misfit = 5.40000E+01
achieved misfit = 5.39999E+01
model norm = 1.42353E+01
misfit change = -6.75492E-05
model norm change = -2.83385E-03
norm comp Ws = 1.36827E+00
norm comp Wx = 1.00376E+01
norm comp Wz = 2.82945E+00
iter cpu time: 0:00:00.18
Exit at convergence.
Iterations performed: 13
total cpu time: 0:00:03.96
DCINV2D ended on:12/10/2015 14:20:17
Reference in New Issue View Git Blame Copy Permalink