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Appended functions to BaseDC

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D Fournier
2016-01-12 17:46:58 -08:00
parent 9d861267e1
commit 2dfacdc904
23 changed files with 5164 additions and 2377 deletions
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simpegDCIP/Dev/Inv2D/dcinv2d.log
+209 -236
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@@ -14,7 +14,7 @@ Distributed by:
Mira Geoscience Ltd.
DCINV2D started on:12/14/2015 14:39:32
DCINV2D started on: 1/12/2016 16:42:18
Reading input file: dcinv2d.inp
@@ -25,7 +25,7 @@ MESH FILE Mesh_2D.msh
CHIFACT 1 100.000000
TOPO DEFAULT %s
INIT_MOD DEFAULT
REF_MOD VALUE 1.000000e-03
REF_MOD VALUE 1.000000e-02
ALPHA DEFAULT
WEIGHT DEFAULT
STORE_ALL_MODELS FALSE
@@ -35,21 +35,21 @@ USE_MREF TRUE
maximum # of iterations: 100
data were read from: FWR_3D_2_2D.dat
# of current locations: 11
# of data: 65
# of current locations: 9
# of data: 45
chifact: 1.00000E+00
target misfit: 6.50000E+01
target misfit: 4.50000E+01
mesh was read from: Mesh_2D.msh
# of cells: 58 x 30
total # of cells: 1740
# of active cells: 1740
# of cells: 81 x 45
total # of cells: 3645
# of active cells: 3645
# of unique data locations: 11
# 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-03
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)
@@ -57,12 +57,12 @@ 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
number of basis vectors: 17 + 3 + 1 = 21
init cpu time: 0:00:00.05
init cpu time: 0:00:00.18
initial misfit = 2.29963E+05
initial misfit = 2.57080E+05
init. model norm = 0.00000E+00
norm comp Ws = 0.00000E+00
norm comp Wx = 0.00000E+00
@@ -73,365 +73,338 @@ 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
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 = 2.20496E+05
target misfit = 1.14982E+05
achieved misfit = 1.14982E+05
model norm = 2.23402E-01
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 = 1.20709E-01
norm comp Wx = 5.36664E-02
norm comp Wz = 4.90259E-02
norm comp Ws = 2.88514E+00
norm comp Wx = 3.70226E-01
norm comp Wz = 3.77603E-01
iter cpu time: 0:00:00.35
iter cpu time: 0:00:01.20
Iteration 2
beta vs. misfit:
beta misfit
5.51240E+04 5.38776E+04
1.10248E+05 8.30234E+04
3.96247E+03 6.15053E+04
7.92494E+03 9.46707E+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
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 = 4.21387E-01
norm comp Wx = 2.22769E-01
norm comp Wz = 1.69261E-01
norm comp Ws = 1.06979E+01
norm comp Wx = 1.61088E+00
norm comp Wz = 1.40814E+00
iter cpu time: 0:00:00.19
iter cpu time: 0:00:00.78
Iteration 3
beta vs. misfit:
beta misfit
1.52922E+04 2.43015E+04
3.05844E+04 3.74992E+04
1.06316E+03 2.49833E+04
2.12631E+03 4.10044E+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
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 = 8.31643E-01
norm comp Wx = 5.75399E-01
norm comp Wz = 3.36966E-01
norm comp Ws = 2.19928E+01
norm comp Wx = 4.11507E+00
norm comp Wz = 3.02271E+00
iter cpu time: 0:00:00.45
iter cpu time: 0:00:01.00
Iteration 4
beta vs. misfit:
beta misfit
5.00045E+03 1.28642E+04
1.00009E+04 1.86842E+04
3.78054E+02 1.14051E+04
7.56108E+02 1.87488E+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
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 = 1.28958E+00
norm comp Wx = 1.28927E+00
norm comp Wz = 5.53778E-01
norm comp Ws = 3.49213E+01
norm comp Wx = 8.32898E+00
norm comp Wz = 5.18098E+00
iter cpu time: 0:00:00.21
iter cpu time: 0:00:00.86
Iteration 5
beta vs. misfit:
beta misfit
1.50950E+03 5.99773E+03
3.01900E+03 9.21452E+03
1.50000E+02 5.05151E+03
3.00000E+02 8.76679E+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
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 = 1.74210E+00
norm comp Wx = 2.47066E+00
norm comp Wz = 9.74574E-01
norm comp Ws = 4.74916E+01
norm comp Wx = 1.43993E+01
norm comp Wz = 7.86671E+00
iter cpu time: 0:00:00.27
iter cpu time: 0:00:00.88
Iteration 6
beta vs. misfit:
beta misfit
4.92062E+02 2.05275E+03
9.84123E+02 3.92917E+03
6.50498E+01 1.83324E+03
1.30100E+02 3.68412E+03
1.37599E+02 3.90387E+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
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 = 2.19885E+00
norm comp Wx = 3.77400E+00
norm comp Wz = 1.79107E+00
norm comp Ws = 5.75580E+01
norm comp Wx = 2.11440E+01
norm comp Wz = 1.08731E+01
iter cpu time: 0:00:00.21
iter cpu time: 0:00:00.81
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
3.43499E+01 8.99667E+02
6.86999E+01 1.65819E+03
8.25106E+01 2.00613E+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
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 = 2.65166E+00
norm comp Wx = 4.83319E+00
norm comp Wz = 2.92980E+00
norm comp Ws = 6.57720E+01
norm comp Wx = 2.69488E+01
norm comp Wz = 1.38454E+01
iter cpu time: 0:00:00.26
iter cpu time: 0:00:00.89
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
2.00625E+01 5.63946E+02
4.01250E+01 9.43655E+02
4.18598E+01 9.78372E+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
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 = 3.12662E+00
norm comp Wx = 5.80666E+00
norm comp Wz = 3.86755E+00
norm comp Ws = 7.39851E+01
norm comp Wx = 3.20341E+01
norm comp Wz = 1.68852E+01
iter cpu time: 0:00:00.30
iter cpu time: 0:00:00.91
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
1.04076E+01 3.54740E+02
2.08152E+01 5.47348E+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
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 = 3.56241E+00
norm comp Wx = 6.64092E+00
norm comp Wz = 4.83532E+00
norm comp Ws = 8.27069E+01
norm comp Wx = 3.78831E+01
norm comp Wz = 2.05954E+01
iter cpu time: 0:00:00.21
iter cpu time: 0:00:00.78
Iteration 10
beta vs. misfit:
beta misfit
3.58330E+01 1.94330E+02
7.16660E+01 2.56470E+02
4.31579E+00 2.09965E+02
8.63158E+00 2.93092E+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
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 = 4.09954E+00
norm comp Wx = 7.65264E+00
norm comp Wz = 5.77092E+00
norm comp Ws = 9.43748E+01
norm comp Wx = 4.67077E+01
norm comp Wz = 2.57538E+01
iter cpu time: 0:00:00.26
iter cpu time: 0:00:00.66
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
1.43202E+00 1.08765E+02
2.86405E+00 1.48776E+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
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 = 5.01489E+00
norm comp Wx = 8.20406E+00
norm comp Wz = 6.88163E+00
norm comp Ws = 1.10709E+02
norm comp Wx = 6.50107E+01
norm comp Wz = 3.14941E+01
iter cpu time: 0:00:00.22
iter cpu time: 0:00:00.81
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
4.24735E-01 4.69288E+01
8.49470E-01 5.82083E+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
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 = 5.70037E+00
norm comp Wx = 9.53924E+00
norm comp Wz = 8.03554E+00
norm comp Ws = 1.22208E+02
norm comp Wx = 7.90861E+01
norm comp Wz = 3.81319E+01
iter cpu time: 0:00:00.23
iter cpu time: 0:00:00.72
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
5.07840E-01 2.85576E+01
6.33710E-01 3.23824E+01
1.13145E+00 5.17204E+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
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 = 5.80622E+00
norm comp Wx = 9.69831E+00
norm comp Wz = 8.40024E+00
norm comp Ws = 1.26074E+02
norm comp Wx = 7.63269E+01
norm comp Wz = 3.53536E+01
iter cpu time: 0:00:00.26
iter cpu time: 0:00:01.02
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
9.73609E-01 3.64390E+01
9.95336E-01 3.71506E+01
1.23871E+00 4.57613E+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
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 = 5.35300E+00
norm comp Wx = 9.06543E+00
norm comp Wz = 8.60927E+00
norm comp Ws = 1.26466E+02
norm comp Wx = 7.21243E+01
norm comp Wz = 3.19461E+01
iter cpu time: 0:00:00.30
iter cpu time: 0:00:01.21
Target misfit achieved. Minimizing model norm.
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
1.21843E+00 3.86348E+01
1.21978E+00 3.86736E+01
1.44018E+00 4.53382E+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
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 = 5.25353E+00
norm comp Wx = 9.12332E+00
norm comp Wz = 8.19063E+00
norm comp Ws = 1.25759E+02
norm comp Wx = 6.90749E+01
norm comp Wz = 3.41159E+01
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
iter cpu time: 0:00:00.76
Exit at convergence.
Iterations performed: 16
Iterations performed: 15
total cpu time: 0:00:04.60
total cpu time: 0:00:13.54
DCINV2D ended on:12/14/2015 14:39:36
DCINV2D ended on: 1/12/2016 16:42:31