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:12/14/2015 14:39:32 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: 11 # of data: 65 chifact: 1.00000E+00 target misfit: 6.50000E+01 mesh was read from: Mesh_2D.msh # of cells: 58 x 30 total # of cells: 1740 # of active cells: 1740 # of unique data locations: 11 # 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: 21 + 3 + 1 = 25 init cpu time: 0:00:00.05 initial misfit = 2.29963E+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 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 chosen beta = 2.20496E+05 target misfit = 1.14982E+05 achieved misfit = 1.14982E+05 model norm = 2.23402E-01 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 iter cpu time: 0:00:00.35 Iteration 2 beta vs. misfit: beta misfit 5.51240E+04 5.38776E+04 1.10248E+05 8.30234E+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 norm comp Ws = 4.21387E-01 norm comp Wx = 2.22769E-01 norm comp Wz = 1.69261E-01 iter cpu time: 0:00:00.19 Iteration 3 beta vs. misfit: beta misfit 1.52922E+04 2.43015E+04 3.05844E+04 3.74992E+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 norm comp Ws = 8.31643E-01 norm comp Wx = 5.75399E-01 norm comp Wz = 3.36966E-01 iter cpu time: 0:00:00.45 Iteration 4 beta vs. misfit: beta misfit 5.00045E+03 1.28642E+04 1.00009E+04 1.86842E+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 norm comp Ws = 1.28958E+00 norm comp Wx = 1.28927E+00 norm comp Wz = 5.53778E-01 iter cpu time: 0:00:00.21 Iteration 5 beta vs. misfit: beta misfit 1.50950E+03 5.99773E+03 3.01900E+03 9.21452E+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 norm comp Ws = 1.74210E+00 norm comp Wx = 2.47066E+00 norm comp Wz = 9.74574E-01 iter cpu time: 0:00:00.27 Iteration 6 beta vs. misfit: beta misfit 4.92062E+02 2.05275E+03 9.84123E+02 3.92917E+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 norm comp Ws = 2.19885E+00 norm comp Wx = 3.77400E+00 norm comp Wz = 1.79107E+00 iter cpu time: 0:00:00.21 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 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 norm comp Ws = 2.65166E+00 norm comp Wx = 4.83319E+00 norm comp Wz = 2.92980E+00 iter cpu time: 0:00:00.26 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 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 norm comp Ws = 3.12662E+00 norm comp Wx = 5.80666E+00 norm comp Wz = 3.86755E+00 iter cpu time: 0:00:00.30 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 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 norm comp Ws = 3.56241E+00 norm comp Wx = 6.64092E+00 norm comp Wz = 4.83532E+00 iter cpu time: 0:00:00.21 Iteration 10 beta vs. misfit: beta misfit 3.58330E+01 1.94330E+02 7.16660E+01 2.56470E+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 norm comp Ws = 4.09954E+00 norm comp Wx = 7.65264E+00 norm comp Wz = 5.77092E+00 iter cpu time: 0:00:00.26 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 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 norm comp Ws = 5.01489E+00 norm comp Wx = 8.20406E+00 norm comp Wz = 6.88163E+00 iter cpu time: 0:00:00.22 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 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 norm comp Ws = 5.70037E+00 norm comp Wx = 9.53924E+00 norm comp Wz = 8.03554E+00 iter cpu time: 0:00:00.23 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 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 norm comp Ws = 5.80622E+00 norm comp Wx = 9.69831E+00 norm comp Wz = 8.40024E+00 iter cpu time: 0:00:00.26 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 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 norm comp Ws = 5.35300E+00 norm comp Wx = 9.06543E+00 norm comp Wz = 8.60927E+00 iter cpu time: 0:00:00.30 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 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 norm comp Ws = 5.25353E+00 norm comp Wx = 9.12332E+00 norm comp Wz = 8.19063E+00 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 Exit at convergence. Iterations performed: 16 total cpu time: 0:00:04.60 DCINV2D ended on:12/14/2015 14:39:36