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Implement Gradient array with 2D plotting

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Test the potential as a function Tx distance from gradient grid
This commit is contained in:
D Fournier
2015-12-11 18:16:52 -08:00
parent 2aa523f0ad
commit 2a76852e33
18 changed files with 1949 additions and 99529 deletions
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simpegDCIP/Dev/Inv2D/dcinv2d.log
+218 -194
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@@ -14,7 +14,7 @@ Distributed by:
Mira Geoscience Ltd.
DCINV2D started on:12/11/2015 11:26:38
DCINV2D started on:12/11/2015 12:49:39
Reading input file: dcinv2d.inp
@@ -35,17 +35,17 @@ USE_MREF TRUE
maximum # of iterations: 100
data were read from: FWR_3D_2_2D.dat
# of current locations: 9
# of data: 45
# of current locations: 12
# of data: 72
chifact: 1.00000E+00
target misfit: 4.50000E+01
target misfit: 7.20000E+01
mesh was read from: Mesh_2D.msh
# of cells: 47 x 30
total # of cells: 1410
# of active cells: 1410
# of cells: 54 x 30
total # of cells: 1620
# of active cells: 1620
# of unique data locations: 9
# of unique data locations: 12
# 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
@@ -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: 17 + 3 + 1 = 21
number of basis vectors: 23 + 3 + 1 = 27
init cpu time: 0:00:00.06
init cpu time: 0:00:00.05
initial misfit = 5.86595E+04
initial misfit = 1.20369E+05
init. model norm = 0.00000E+00
norm comp Ws = 0.00000E+00
norm comp Wx = 0.00000E+00
@@ -73,263 +73,263 @@ Iteration 1
beta vs. misfit:
beta misfit
1.14011E+04 1.55434E+04
2.28022E+04 1.97701E+04
5.70054E+04 2.80956E+04
6.37666E+04 2.93408E+04
1.79203E+04 2.40202E+04
3.58406E+04 3.35441E+04
8.96016E+04 5.31602E+04
1.14702E+05 5.96677E+04
1.16837E+05 6.01657E+04
1.16917E+05 6.01843E+04
2.92292E+05 8.46349E+04
chosen beta = 6.37044E+04
target misfit = 2.93298E+04
achieved misfit = 2.93298E+04
model norm = 1.78131E-01
misfit change = 4.99999E-01
chosen beta = 1.16917E+05
target misfit = 6.01844E+04
achieved misfit = 6.01844E+04
model norm = 2.14462E-01
misfit change = 5.00000E-01
model norm change = 0.00000E+00
norm comp Ws = 9.36044E-02
norm comp Wx = 5.21598E-02
norm comp Wz = 3.23664E-02
norm comp Ws = 1.21979E-01
norm comp Wx = 5.71490E-02
norm comp Wz = 3.53340E-02
iter cpu time: 0:00:00.54
iter cpu time: 0:00:00.30
Iteration 2
beta vs. misfit:
beta misfit
1.13370E+04 1.41481E+04
1.59261E+04 1.63158E+04
3.18522E+04 2.20755E+04
2.92293E+04 2.83571E+04
5.84585E+04 4.27230E+04
chosen beta = 1.23494E+04
target misfit = 1.46649E+04
achieved misfit = 1.46622E+04
model norm = 7.62402E-01
misfit change = 5.00094E-01
model norm change = 3.28002E+00
chosen beta = 3.23175E+04
target misfit = 3.00922E+04
achieved misfit = 3.01009E+04
model norm = 7.74780E-01
misfit change = 4.99855E-01
model norm change = 2.61267E+00
norm comp Ws = 3.10519E-01
norm comp Wx = 3.26120E-01
norm comp Wz = 1.25764E-01
norm comp Ws = 3.95707E-01
norm comp Wx = 2.57560E-01
norm comp Wz = 1.21513E-01
iter cpu time: 0:00:00.17
iter cpu time: 0:00:00.46
Iteration 3
beta vs. misfit:
beta misfit
3.08734E+03 7.25168E+03
6.17468E+03 1.04745E+04
8.07937E+03 1.24985E+04
1.61587E+04 1.94052E+04
chosen beta = 3.15138E+03
target misfit = 7.33109E+03
achieved misfit = 7.33827E+03
model norm = 1.97756E+00
misfit change = 4.99510E-01
model norm change = 1.59385E+00
chosen beta = 1.08271E+04
target misfit = 1.50505E+04
achieved misfit = 1.50193E+04
model norm = 1.64045E+00
misfit change = 5.01035E-01
model norm change = 1.11731E+00
norm comp Ws = 5.74759E-01
norm comp Wx = 1.07019E+00
norm comp Wz = 3.32607E-01
norm comp Ws = 7.14500E-01
norm comp Wx = 6.78931E-01
norm comp Wz = 2.47017E-01
iter cpu time: 0:00:00.14
iter cpu time: 0:00:00.17
Iteration 4
beta vs. misfit:
beta misfit
7.87844E+02 2.34933E+03
1.57569E+03 4.17369E+03
2.70679E+03 5.57540E+03
5.41357E+03 9.10604E+03
chosen beta = 1.34889E+03
target misfit = 3.66914E+03
achieved misfit = 3.69116E+03
model norm = 3.63935E+00
misfit change = 4.96999E-01
model norm change = 8.40325E-01
chosen beta = 4.12295E+03
target misfit = 7.50965E+03
achieved misfit = 7.52021E+03
model norm = 2.79115E+00
misfit change = 4.99297E-01
model norm change = 7.01455E-01
norm comp Ws = 9.31633E-01
norm comp Wx = 2.00929E+00
norm comp Wz = 6.98426E-01
norm comp Ws = 1.04121E+00
norm comp Wx = 1.32464E+00
norm comp Wz = 4.25302E-01
iter cpu time: 0:00:00.43
iter cpu time: 0:00:00.18
Iteration 5
beta vs. misfit:
beta misfit
3.37223E+02 1.15980E+03
6.74446E+02 1.95927E+03
1.03074E+03 2.54379E+03
2.06147E+03 4.28531E+03
chosen beta = 6.23201E+02
target misfit = 1.84558E+03
achieved misfit = 1.83831E+03
model norm = 5.55702E+00
misfit change = 5.01969E-01
model norm change = 5.26927E-01
chosen beta = 1.73265E+03
target misfit = 3.76010E+03
achieved misfit = 3.75408E+03
model norm = 4.19293E+00
misfit change = 5.00801E-01
model norm change = 5.02222E-01
norm comp Ws = 1.39116E+00
norm comp Wx = 2.97085E+00
norm comp Wz = 1.19501E+00
norm comp Ws = 1.40651E+00
norm comp Wx = 2.08363E+00
norm comp Wz = 7.02789E-01
iter cpu time: 0:00:00.13
iter cpu time: 0:00:00.35
Iteration 6
beta vs. misfit:
beta misfit
1.55800E+02 7.04026E+02
3.11600E+02 1.03301E+03
4.33163E+02 1.39429E+03
8.66326E+02 2.12817E+03
chosen beta = 2.52300E+02
target misfit = 9.19156E+02
achieved misfit = 9.06295E+02
model norm = 7.76502E+00
misfit change = 5.06996E-01
model norm change = 3.97336E-01
chosen beta = 7.05172E+02
target misfit = 1.87704E+03
achieved misfit = 1.85569E+03
model norm = 5.84262E+00
misfit change = 5.05685E-01
model norm change = 3.93448E-01
norm comp Ws = 1.93177E+00
norm comp Wx = 4.15601E+00
norm comp Wz = 1.67723E+00
norm comp Ws = 1.80726E+00
norm comp Wx = 2.92669E+00
norm comp Wz = 1.10867E+00
iter cpu time: 0:00:00.14
iter cpu time: 0:00:00.20
Iteration 7
beta vs. misfit:
beta misfit
5.45665E+01 4.43592E+02
6.30749E+01 4.61153E+02
1.26150E+02 5.89041E+02
1.76293E+02 8.23938E+02
3.52586E+02 1.12973E+03
chosen beta = 5.90843E+01
target misfit = 4.53148E+02
achieved misfit = 4.52929E+02
model norm = 1.10434E+01
misfit change = 5.00242E-01
model norm change = 4.22194E-01
chosen beta = 2.28828E+02
target misfit = 9.27847E+02
achieved misfit = 9.18382E+02
model norm = 7.96769E+00
misfit change = 5.05101E-01
model norm change = 3.63718E-01
norm comp Ws = 2.44126E+00
norm comp Wx = 6.33488E+00
norm comp Wz = 2.26722E+00
norm comp Ws = 2.39387E+00
norm comp Wx = 3.93052E+00
norm comp Wz = 1.64329E+00
iter cpu time: 0:00:00.15
iter cpu time: 0:00:00.18
Iteration 8
beta vs. misfit:
beta misfit
1.76205E+00 2.33067E+02
3.87651E+00 2.33770E+02
8.52832E+00 2.36313E+02
1.47711E+01 2.41686E+02
2.95422E+01 2.66924E+02
1.18196E+01 5.56593E+02
2.60031E+01 5.49364E+02
5.72069E+01 5.79150E+02
1.14414E+02 6.61868E+02
chosen beta = 3.87651E+00
target misfit = 2.26464E+02
achieved misfit = 2.33770E+02
model norm = 1.57679E+01
misfit change = 4.83870E-01
model norm change = 4.27818E-01
chosen beta = 2.60031E+01
target misfit = 4.59191E+02
achieved misfit = 5.49364E+02
model norm = 1.20297E+01
misfit change = 4.01813E-01
model norm change = 5.09814E-01
norm comp Ws = 3.91956E+00
norm comp Wx = 8.05061E+00
norm comp Wz = 3.79775E+00
norm comp Ws = 2.50439E+00
norm comp Wx = 6.75644E+00
norm comp Wz = 2.76890E+00
iter cpu time: 0:00:00.54
iter cpu time: 0:00:00.34
Iteration 9
beta vs. misfit:
beta misfit
9.69127E-01 1.25628E+02
1.93825E+00 1.25177E+02
3.87651E+00 1.26747E+02
2.95490E+00 2.89198E+02
6.50079E+00 2.90609E+02
1.30016E+01 2.96823E+02
chosen beta = 1.93825E+00
target misfit = 1.16885E+02
achieved misfit = 1.25177E+02
model norm = 2.02946E+01
misfit change = 4.64529E-01
model norm change = 2.87081E-01
chosen beta = 6.50079E+00
target misfit = 2.74682E+02
achieved misfit = 2.90609E+02
model norm = 1.61063E+01
misfit change = 4.71008E-01
model norm change = 3.38877E-01
norm comp Ws = 5.14918E+00
norm comp Wx = 1.04854E+01
norm comp Wz = 4.66004E+00
norm comp Ws = 3.18688E+00
norm comp Wx = 8.74009E+00
norm comp Wz = 4.17936E+00
iter cpu time: 0:00:00.14
iter cpu time: 0:00:00.18
Iteration 10
beta vs. misfit:
beta misfit
4.84563E-01 8.59120E+01
9.69127E-01 8.56434E+01
1.93825E+00 8.63895E+01
1.62520E+00 1.32688E+02
3.25039E+00 1.36212E+02
8.12598E+00 1.51520E+02
chosen beta = 9.69127E-01
target misfit = 6.25885E+01
achieved misfit = 8.56434E+01
model norm = 2.27540E+01
misfit change = 3.15821E-01
model norm change = 1.21187E-01
chosen beta = 5.66721E+00
target misfit = 1.45305E+02
achieved misfit = 1.43202E+02
model norm = 2.09909E+01
misfit change = 5.07237E-01
model norm change = 3.03268E-01
norm comp Ws = 6.37929E+00
norm comp Wx = 1.05750E+01
norm comp Wz = 5.79974E+00
norm comp Ws = 3.87211E+00
norm comp Wx = 1.01396E+01
norm comp Wz = 6.97916E+00
iter cpu time: 0:00:00.13
iter cpu time: 0:00:00.19
Iteration 11
beta vs. misfit:
beta misfit
2.67558E-01 5.36200E+01
5.09212E-01 5.36850E+01
6.51202E-01 1.00416E+02
1.43264E+00 1.00817E+02
2.84940E+00 1.02479E+02
chosen beta = 5.09212E-01
target misfit = 4.50000E+01
achieved misfit = 5.36850E+01
model norm = 2.65060E+01
misfit change = 3.73156E-01
model norm change = 1.64894E-01
chosen beta = 1.43264E+00
target misfit = 7.20000E+01
achieved misfit = 1.00817E+02
model norm = 2.48156E+01
misfit change = 2.95976E-01
model norm change = 1.82208E-01
norm comp Ws = 7.06440E+00
norm comp Wx = 1.17778E+01
norm comp Wz = 7.66382E+00
norm comp Ws = 4.13209E+00
norm comp Wx = 1.18099E+01
norm comp Wz = 8.87357E+00
iter cpu time: 0:00:00.12
iter cpu time: 0:00:00.29
Iteration 12
beta vs. misfit:
beta misfit
3.57781E-01 4.27021E+01
4.26833E-01 4.27304E+01
1.06708E+00 4.32906E+01
2.66771E+00 4.58497E+01
6.45407E-01 7.19782E+01
7.30688E-01 7.20262E+01
1.02314E+00 7.23329E+01
chosen beta = 1.97938E+00
target misfit = 4.50000E+01
achieved misfit = 4.46230E+01
model norm = 2.75984E+01
misfit change = 1.68800E-01
model norm change = 4.12137E-02
chosen beta = 6.82819E-01
target misfit = 7.20000E+01
achieved misfit = 7.19973E+01
model norm = 2.81474E+01
misfit change = 2.85865E-01
model norm change = 1.34263E-01
norm comp Ws = 7.60316E+00
norm comp Wx = 1.17705E+01
norm comp Wz = 8.22479E+00
norm comp Ws = 4.48349E+00
norm comp Wx = 1.28628E+01
norm comp Wz = 1.08011E+01
iter cpu time: 0:00:00.51
iter cpu time: 0:00:00.19
Target misfit achieved. Minimizing model norm.
@@ -339,52 +339,76 @@ Iteration 13
beta vs. misfit:
beta misfit
1.99610E+00 3.93953E+01
2.01297E+00 3.94209E+01
5.03242E+00 4.59121E+01
6.82845E-01 5.51235E+01
6.82871E-01 5.51235E+01
1.70718E+00 5.63927E+01
4.26794E+00 6.20058E+01
1.06699E+01 8.42251E+01
chosen beta = 4.46061E+00
target misfit = 4.50000E+01
achieved misfit = 4.44143E+01
model norm = 2.65756E+01
misfit change = 4.67734E-03
model norm change = -3.70618E-02
chosen beta = 6.67404E+00
target misfit = 7.20000E+01
achieved misfit = 6.92297E+01
model norm = 2.60729E+01
misfit change = 3.84394E-02
model norm change = -7.37010E-02
norm comp Ws = 7.28818E+00
norm comp Wx = 1.16493E+01
norm comp Wz = 7.63807E+00
norm comp Ws = 4.54269E+00
norm comp Wx = 1.16020E+01
norm comp Wz = 9.92826E+00
iter cpu time: 0:00:00.15
iter cpu time: 0:00:00.24
Iteration 14
beta vs. misfit:
beta misfit
4.51943E+00 4.42228E+01
4.57903E+00 4.43808E+01
4.81808E+00 4.50262E+01
6.94110E+00 6.96952E+01
7.21885E+00 7.05885E+01
7.67292E+00 7.20897E+01
chosen beta = 4.80820E+00
target misfit = 4.50000E+01
achieved misfit = 4.49992E+01
model norm = 2.59337E+01
misfit change = -1.31690E-02
model norm change = -2.41533E-02
chosen beta = 7.64528E+00
target misfit = 7.20000E+01
achieved misfit = 7.19969E+01
model norm = 2.47618E+01
misfit change = -3.99705E-02
model norm change = -5.02868E-02
norm comp Ws = 7.05571E+00
norm comp Wx = 1.14342E+01
norm comp Wz = 7.44373E+00
norm comp Ws = 4.37322E+00
norm comp Wx = 1.12042E+01
norm comp Wz = 9.18441E+00
iter cpu time: 0:00:00.15
iter cpu time: 0:00:00.34
Iteration 15
beta vs. misfit:
beta misfit
7.64561E+00 7.10307E+01
7.64594E+00 7.10318E+01
7.95109E+00 7.20211E+01
chosen beta = 7.94451E+00
target misfit = 7.20000E+01
achieved misfit = 7.19996E+01
model norm = 2.43805E+01
misfit change = -3.71422E-05
model norm change = -1.53965E-02
norm comp Ws = 4.48699E+00
norm comp Wx = 1.10931E+01
norm comp Wz = 8.80046E+00
iter cpu time: 0:00:00.26
Exit at convergence.
Iterations performed: 14
Iterations performed: 15
total cpu time: 0:00:03.57
total cpu time: 0:00:04.00
DCINV2D ended on:12/11/2015 11:26:42
DCINV2D ended on:12/11/2015 12:49:43