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:12/11/2015 12:49:39 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: 12 # of data: 72 chifact: 1.00000E+00 target misfit: 7.20000E+01 mesh was read from: Mesh_2D.msh # of cells: 54 x 30 total # of cells: 1620 # of active cells: 1620 # 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 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: 23 + 3 + 1 = 27 init cpu time: 0:00:00.05 initial misfit = 1.20369E+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 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 = 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 = 1.21979E-01 norm comp Wx = 5.71490E-02 norm comp Wz = 3.53340E-02 iter cpu time: 0:00:00.30 Iteration 2 beta vs. misfit: beta misfit 2.92293E+04 2.83571E+04 5.84585E+04 4.27230E+04 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 = 3.95707E-01 norm comp Wx = 2.57560E-01 norm comp Wz = 1.21513E-01 iter cpu time: 0:00:00.46 Iteration 3 beta vs. misfit: beta misfit 8.07937E+03 1.24985E+04 1.61587E+04 1.94052E+04 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 = 7.14500E-01 norm comp Wx = 6.78931E-01 norm comp Wz = 2.47017E-01 iter cpu time: 0:00:00.17 Iteration 4 beta vs. misfit: beta misfit 2.70679E+03 5.57540E+03 5.41357E+03 9.10604E+03 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 = 1.04121E+00 norm comp Wx = 1.32464E+00 norm comp Wz = 4.25302E-01 iter cpu time: 0:00:00.18 Iteration 5 beta vs. misfit: beta misfit 1.03074E+03 2.54379E+03 2.06147E+03 4.28531E+03 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 = 1.40651E+00 norm comp Wx = 2.08363E+00 norm comp Wz = 7.02789E-01 iter cpu time: 0:00:00.35 Iteration 6 beta vs. misfit: beta misfit 4.33163E+02 1.39429E+03 8.66326E+02 2.12817E+03 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 = 1.80726E+00 norm comp Wx = 2.92669E+00 norm comp Wz = 1.10867E+00 iter cpu time: 0:00:00.20 Iteration 7 beta vs. misfit: beta misfit 1.76293E+02 8.23938E+02 3.52586E+02 1.12973E+03 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 = 2.39387E+00 norm comp Wx = 3.93052E+00 norm comp Wz = 1.64329E+00 iter cpu time: 0:00:00.18 Iteration 8 beta vs. misfit: beta misfit 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 = 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 = 2.50439E+00 norm comp Wx = 6.75644E+00 norm comp Wz = 2.76890E+00 iter cpu time: 0:00:00.34 Iteration 9 beta vs. misfit: beta misfit 2.95490E+00 2.89198E+02 6.50079E+00 2.90609E+02 1.30016E+01 2.96823E+02 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 = 3.18688E+00 norm comp Wx = 8.74009E+00 norm comp Wz = 4.17936E+00 iter cpu time: 0:00:00.18 Iteration 10 beta vs. misfit: beta misfit 1.62520E+00 1.32688E+02 3.25039E+00 1.36212E+02 8.12598E+00 1.51520E+02 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 = 3.87211E+00 norm comp Wx = 1.01396E+01 norm comp Wz = 6.97916E+00 iter cpu time: 0:00:00.19 Iteration 11 beta vs. misfit: beta misfit 6.51202E-01 1.00416E+02 1.43264E+00 1.00817E+02 2.84940E+00 1.02479E+02 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 = 4.13209E+00 norm comp Wx = 1.18099E+01 norm comp Wz = 8.87357E+00 iter cpu time: 0:00:00.29 Iteration 12 beta vs. misfit: beta misfit 6.45407E-01 7.19782E+01 7.30688E-01 7.20262E+01 1.02314E+00 7.23329E+01 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 = 4.48349E+00 norm comp Wx = 1.28628E+01 norm comp Wz = 1.08011E+01 iter cpu time: 0:00:00.19 Target misfit achieved. Minimizing model norm. Iteration 13 beta vs. misfit: beta misfit 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 = 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 = 4.54269E+00 norm comp Wx = 1.16020E+01 norm comp Wz = 9.92826E+00 iter cpu time: 0:00:00.24 Iteration 14 beta vs. misfit: beta misfit 6.94110E+00 6.96952E+01 7.21885E+00 7.05885E+01 7.67292E+00 7.20897E+01 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 = 4.37322E+00 norm comp Wx = 1.12042E+01 norm comp Wz = 9.18441E+00 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: 15 total cpu time: 0:00:04.00 DCINV2D ended on:12/11/2015 12:49:43