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https://github.com/wassname/simpeg.git
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D Fournier
415 lines
9.0 KiB
Plaintext
415 lines
9.0 KiB
Plaintext
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Parallelized with OpenMP. # of threads: 4
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DCIP2D - Version 5 (BETA) 20110811: DCINV2D
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Developed by University of British Columbia
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Geophysical Inversion Facility (UBC-GIF)
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(C) Copyright 1992 - 2011, UBC-GIF,
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Department of Earth and Ocean Sciences, UBC
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http://www.eos.ubc.ca/research/ubcgif/
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Distributed by:
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Mira Geoscience Ltd.
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DCINV2D started on:12/11/2015 12:49:39
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Reading input file: dcinv2d.inp
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----------------------------------------------
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OBS LOC_X FWR_3D_2_2D.dat
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MESH FILE Mesh_2D.msh
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CHIFACT 1 100.000000
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TOPO DEFAULT %s
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INIT_MOD DEFAULT
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REF_MOD VALUE 1.000000e-03
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ALPHA DEFAULT
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WEIGHT DEFAULT
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STORE_ALL_MODELS FALSE
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INVMODE SVD
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USE_MREF TRUE
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----------------------------------------------
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maximum # of iterations: 100
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data were read from: FWR_3D_2_2D.dat
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# of current locations: 12
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# of data: 72
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chifact: 1.00000E+00
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target misfit: 7.20000E+01
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mesh was read from: Mesh_2D.msh
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# of cells: 54 x 30
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total # of cells: 1620
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# of active cells: 1620
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# of unique data locations: 12
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# of wave values: 13
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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
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reference conductivity model is set to a constant: 1.000000E-03
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initial model is set to the reference model.
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using default length scales (Lx, Lz): ( 8.00000E+01, 8.00000E+01)
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corresponding alpha (a_s, a_x, a_z): ( 1.56250E-04, 1.0000E+00, 1.0000E+00)
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Using basis vectors and SVD.
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reference model will be used in the derivative terms.
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number of basis vectors: 23 + 3 + 1 = 27
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init cpu time: 0:00:00.05
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initial misfit = 1.20369E+05
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init. model norm = 0.00000E+00
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norm comp Ws = 0.00000E+00
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norm comp Wx = 0.00000E+00
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norm comp Wz = 0.00000E+00
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Iteration 1
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beta vs. misfit:
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beta misfit
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1.79203E+04 2.40202E+04
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3.58406E+04 3.35441E+04
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8.96016E+04 5.31602E+04
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1.14702E+05 5.96677E+04
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1.16837E+05 6.01657E+04
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1.16917E+05 6.01843E+04
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2.92292E+05 8.46349E+04
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chosen beta = 1.16917E+05
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target misfit = 6.01844E+04
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achieved misfit = 6.01844E+04
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model norm = 2.14462E-01
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misfit change = 5.00000E-01
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model norm change = 0.00000E+00
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norm comp Ws = 1.21979E-01
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norm comp Wx = 5.71490E-02
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norm comp Wz = 3.53340E-02
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iter cpu time: 0:00:00.30
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Iteration 2
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beta vs. misfit:
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beta misfit
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2.92293E+04 2.83571E+04
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5.84585E+04 4.27230E+04
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chosen beta = 3.23175E+04
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target misfit = 3.00922E+04
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achieved misfit = 3.01009E+04
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model norm = 7.74780E-01
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misfit change = 4.99855E-01
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model norm change = 2.61267E+00
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norm comp Ws = 3.95707E-01
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norm comp Wx = 2.57560E-01
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norm comp Wz = 1.21513E-01
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iter cpu time: 0:00:00.46
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Iteration 3
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beta vs. misfit:
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beta misfit
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8.07937E+03 1.24985E+04
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1.61587E+04 1.94052E+04
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chosen beta = 1.08271E+04
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target misfit = 1.50505E+04
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achieved misfit = 1.50193E+04
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model norm = 1.64045E+00
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misfit change = 5.01035E-01
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model norm change = 1.11731E+00
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norm comp Ws = 7.14500E-01
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norm comp Wx = 6.78931E-01
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norm comp Wz = 2.47017E-01
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iter cpu time: 0:00:00.17
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Iteration 4
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beta vs. misfit:
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beta misfit
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2.70679E+03 5.57540E+03
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5.41357E+03 9.10604E+03
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chosen beta = 4.12295E+03
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target misfit = 7.50965E+03
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achieved misfit = 7.52021E+03
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model norm = 2.79115E+00
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misfit change = 4.99297E-01
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model norm change = 7.01455E-01
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norm comp Ws = 1.04121E+00
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norm comp Wx = 1.32464E+00
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norm comp Wz = 4.25302E-01
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iter cpu time: 0:00:00.18
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Iteration 5
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beta vs. misfit:
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beta misfit
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1.03074E+03 2.54379E+03
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2.06147E+03 4.28531E+03
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chosen beta = 1.73265E+03
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target misfit = 3.76010E+03
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achieved misfit = 3.75408E+03
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model norm = 4.19293E+00
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misfit change = 5.00801E-01
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model norm change = 5.02222E-01
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norm comp Ws = 1.40651E+00
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norm comp Wx = 2.08363E+00
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norm comp Wz = 7.02789E-01
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iter cpu time: 0:00:00.35
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Iteration 6
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beta vs. misfit:
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beta misfit
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4.33163E+02 1.39429E+03
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8.66326E+02 2.12817E+03
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chosen beta = 7.05172E+02
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target misfit = 1.87704E+03
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achieved misfit = 1.85569E+03
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model norm = 5.84262E+00
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misfit change = 5.05685E-01
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model norm change = 3.93448E-01
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norm comp Ws = 1.80726E+00
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norm comp Wx = 2.92669E+00
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norm comp Wz = 1.10867E+00
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iter cpu time: 0:00:00.20
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Iteration 7
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beta vs. misfit:
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beta misfit
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1.76293E+02 8.23938E+02
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3.52586E+02 1.12973E+03
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chosen beta = 2.28828E+02
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target misfit = 9.27847E+02
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achieved misfit = 9.18382E+02
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model norm = 7.96769E+00
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misfit change = 5.05101E-01
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model norm change = 3.63718E-01
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norm comp Ws = 2.39387E+00
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norm comp Wx = 3.93052E+00
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norm comp Wz = 1.64329E+00
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iter cpu time: 0:00:00.18
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Iteration 8
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beta vs. misfit:
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beta misfit
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1.18196E+01 5.56593E+02
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2.60031E+01 5.49364E+02
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5.72069E+01 5.79150E+02
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1.14414E+02 6.61868E+02
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chosen beta = 2.60031E+01
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target misfit = 4.59191E+02
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achieved misfit = 5.49364E+02
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model norm = 1.20297E+01
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misfit change = 4.01813E-01
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model norm change = 5.09814E-01
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norm comp Ws = 2.50439E+00
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norm comp Wx = 6.75644E+00
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norm comp Wz = 2.76890E+00
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iter cpu time: 0:00:00.34
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Iteration 9
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beta vs. misfit:
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beta misfit
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2.95490E+00 2.89198E+02
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6.50079E+00 2.90609E+02
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1.30016E+01 2.96823E+02
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chosen beta = 6.50079E+00
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target misfit = 2.74682E+02
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achieved misfit = 2.90609E+02
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model norm = 1.61063E+01
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misfit change = 4.71008E-01
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model norm change = 3.38877E-01
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norm comp Ws = 3.18688E+00
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norm comp Wx = 8.74009E+00
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norm comp Wz = 4.17936E+00
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iter cpu time: 0:00:00.18
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Iteration 10
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beta vs. misfit:
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beta misfit
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1.62520E+00 1.32688E+02
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3.25039E+00 1.36212E+02
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8.12598E+00 1.51520E+02
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chosen beta = 5.66721E+00
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target misfit = 1.45305E+02
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achieved misfit = 1.43202E+02
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model norm = 2.09909E+01
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misfit change = 5.07237E-01
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model norm change = 3.03268E-01
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norm comp Ws = 3.87211E+00
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norm comp Wx = 1.01396E+01
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norm comp Wz = 6.97916E+00
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iter cpu time: 0:00:00.19
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Iteration 11
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beta vs. misfit:
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beta misfit
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6.51202E-01 1.00416E+02
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1.43264E+00 1.00817E+02
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2.84940E+00 1.02479E+02
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chosen beta = 1.43264E+00
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target misfit = 7.20000E+01
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achieved misfit = 1.00817E+02
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model norm = 2.48156E+01
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misfit change = 2.95976E-01
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model norm change = 1.82208E-01
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norm comp Ws = 4.13209E+00
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norm comp Wx = 1.18099E+01
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norm comp Wz = 8.87357E+00
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iter cpu time: 0:00:00.29
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Iteration 12
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beta vs. misfit:
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beta misfit
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6.45407E-01 7.19782E+01
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7.30688E-01 7.20262E+01
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1.02314E+00 7.23329E+01
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chosen beta = 6.82819E-01
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target misfit = 7.20000E+01
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achieved misfit = 7.19973E+01
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model norm = 2.81474E+01
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misfit change = 2.85865E-01
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model norm change = 1.34263E-01
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norm comp Ws = 4.48349E+00
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norm comp Wx = 1.28628E+01
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norm comp Wz = 1.08011E+01
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iter cpu time: 0:00:00.19
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Target misfit achieved. Minimizing model norm.
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Iteration 13
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beta vs. misfit:
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beta misfit
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6.82845E-01 5.51235E+01
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6.82871E-01 5.51235E+01
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1.70718E+00 5.63927E+01
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4.26794E+00 6.20058E+01
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1.06699E+01 8.42251E+01
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chosen beta = 6.67404E+00
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target misfit = 7.20000E+01
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achieved misfit = 6.92297E+01
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model norm = 2.60729E+01
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misfit change = 3.84394E-02
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model norm change = -7.37010E-02
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norm comp Ws = 4.54269E+00
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norm comp Wx = 1.16020E+01
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norm comp Wz = 9.92826E+00
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iter cpu time: 0:00:00.24
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Iteration 14
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beta vs. misfit:
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beta misfit
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6.94110E+00 6.96952E+01
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7.21885E+00 7.05885E+01
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7.67292E+00 7.20897E+01
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chosen beta = 7.64528E+00
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target misfit = 7.20000E+01
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achieved misfit = 7.19969E+01
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model norm = 2.47618E+01
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misfit change = -3.99705E-02
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model norm change = -5.02868E-02
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norm comp Ws = 4.37322E+00
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norm comp Wx = 1.12042E+01
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norm comp Wz = 9.18441E+00
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iter cpu time: 0:00:00.34
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Iteration 15
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beta vs. misfit:
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beta misfit
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7.64561E+00 7.10307E+01
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7.64594E+00 7.10318E+01
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7.95109E+00 7.20211E+01
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chosen beta = 7.94451E+00
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target misfit = 7.20000E+01
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achieved misfit = 7.19996E+01
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model norm = 2.43805E+01
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misfit change = -3.71422E-05
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model norm change = -1.53965E-02
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norm comp Ws = 4.48699E+00
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norm comp Wx = 1.10931E+01
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norm comp Wz = 8.80046E+00
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iter cpu time: 0:00:00.26
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Exit at convergence.
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Iterations performed: 15
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total cpu time: 0:00:04.00
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DCINV2D ended on:12/11/2015 12:49:43
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