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:12/10/2015 14:20:13 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: 10 # of data: 54 chifact: 1.00000E+00 target misfit: 5.40000E+01 mesh was read from: Mesh_2D.msh # of cells: 50 x 30 total # of cells: 1500 # of active cells: 1500 # of unique data locations: 10 # 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: 19 + 3 + 1 = 23 init cpu time: 0:00:00.04 initial misfit = 2.87480E+04 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 1.05827E+04 7.91147E+03 2.11653E+04 9.97641E+03 5.29133E+04 1.42234E+04 5.43732E+04 1.43748E+04 chosen beta = 5.43657E+04 target misfit = 1.43740E+04 achieved misfit = 1.43740E+04 model norm = 1.00462E-01 misfit change = 5.00000E-01 model norm change = 0.00000E+00 norm comp Ws = 5.19270E-02 norm comp Wx = 2.47955E-02 norm comp Wz = 2.37393E-02 iter cpu time: 0:00:00.64 Iteration 2 beta vs. misfit: beta misfit 8.89064E+03 7.03214E+03 1.35914E+04 8.23443E+03 2.71829E+04 1.09665E+04 chosen beta = 9.42699E+03 target misfit = 7.18700E+03 achieved misfit = 7.18162E+03 model norm = 4.48909E-01 misfit change = 5.00374E-01 model norm change = 3.46846E+00 norm comp Ws = 1.84955E-01 norm comp Wx = 1.65870E-01 norm comp Wz = 9.80840E-02 iter cpu time: 0:00:00.40 Iteration 3 beta vs. misfit: beta misfit 1.30271E+03 3.38723E+03 2.35675E+03 4.32491E+03 4.71349E+03 5.60434E+03 chosen beta = 1.50085E+03 target misfit = 3.59081E+03 achieved misfit = 3.60067E+03 model norm = 1.47576E+00 misfit change = 4.98627E-01 model norm change = 2.28744E+00 norm comp Ws = 4.08324E-01 norm comp Wx = 8.20762E-01 norm comp Wz = 2.46674E-01 iter cpu time: 0:00:00.49 Iteration 4 beta vs. misfit: beta misfit 3.75212E+02 1.34820E+03 7.50424E+02 2.29809E+03 chosen beta = 5.46411E+02 target misfit = 1.80034E+03 achieved misfit = 1.83379E+03 model norm = 3.31412E+00 misfit change = 4.90710E-01 model norm change = 1.24570E+00 norm comp Ws = 6.83124E-01 norm comp Wx = 2.07548E+00 norm comp Wz = 5.55519E-01 iter cpu time: 0:00:00.20 Iteration 5 beta vs. misfit: beta misfit 1.36603E+02 5.05265E+02 2.73206E+02 9.48634E+02 chosen beta = 2.63165E+02 target misfit = 9.16894E+02 achieved misfit = 9.16736E+02 model norm = 5.54836E+00 misfit change = 5.00086E-01 model norm change = 6.74157E-01 norm comp Ws = 8.54281E-01 norm comp Wx = 3.70325E+00 norm comp Wz = 9.90823E-01 iter cpu time: 0:00:00.17 Iteration 6 beta vs. misfit: beta misfit 6.57912E+01 2.23512E+02 1.31582E+02 4.05775E+02 1.51607E+02 4.68975E+02 chosen beta = 1.48251E+02 target misfit = 4.58368E+02 achieved misfit = 4.58273E+02 model norm = 7.68792E+00 misfit change = 5.00103E-01 model norm change = 3.85621E-01 norm comp Ws = 1.03639E+00 norm comp Wx = 5.25876E+00 norm comp Wz = 1.39277E+00 iter cpu time: 0:00:00.29 Iteration 7 beta vs. misfit: beta misfit 3.70626E+01 1.66504E+02 7.41253E+01 2.51726E+02 chosen beta = 6.33124E+01 target misfit = 2.29137E+02 achieved misfit = 2.25244E+02 model norm = 1.00149E+01 misfit change = 5.08495E-01 model norm change = 3.02681E-01 norm comp Ws = 1.04755E+00 norm comp Wx = 6.91957E+00 norm comp Wz = 2.04778E+00 iter cpu time: 0:00:00.34 Iteration 8 beta vs. misfit: beta misfit 1.58281E+01 1.02052E+02 3.16562E+01 1.24062E+02 chosen beta = 2.24560E+01 target misfit = 1.12622E+02 achieved misfit = 1.09408E+02 model norm = 1.25018E+01 misfit change = 5.14267E-01 model norm change = 2.48315E-01 norm comp Ws = 1.21846E+00 norm comp Wx = 9.04398E+00 norm comp Wz = 2.23932E+00 iter cpu time: 0:00:00.17 Iteration 9 beta vs. misfit: beta misfit 1.15992E+00 7.28154E+01 2.55182E+00 7.30977E+01 5.61400E+00 7.44520E+01 1.12280E+01 7.91248E+01 chosen beta = 2.55182E+00 target misfit = 5.47041E+01 achieved misfit = 7.30977E+01 model norm = 1.46799E+01 misfit change = 3.31881E-01 model norm change = 1.74228E-01 norm comp Ws = 1.27978E+00 norm comp Wx = 1.03955E+01 norm comp Wz = 3.00464E+00 iter cpu time: 0:00:00.17 Iteration 10 beta vs. misfit: beta misfit 1.39261E+00 4.80711E+01 1.88512E+00 4.83598E+01 4.71280E+00 5.04589E+01 1.17820E+01 5.88251E+01 chosen beta = 7.06669E+00 target misfit = 5.40000E+01 achieved misfit = 5.27458E+01 model norm = 1.48886E+01 misfit change = 2.78420E-01 model norm change = 1.42172E-02 norm comp Ws = 1.42273E+00 norm comp Wx = 1.05086E+01 norm comp Wz = 2.95727E+00 iter cpu time: 0:00:00.39 Iteration 11 beta vs. misfit: beta misfit 7.23472E+00 4.12130E+01 7.40675E+00 4.14256E+01 1.85169E+01 5.95740E+01 chosen beta = 1.44535E+01 target misfit = 5.40000E+01 achieved misfit = 5.20734E+01 model norm = 1.45035E+01 misfit change = 1.27476E-02 model norm change = -2.58673E-02 norm comp Ws = 1.38232E+00 norm comp Wx = 1.04192E+01 norm comp Wz = 2.70198E+00 iter cpu time: 0:00:00.26 Iteration 12 beta vs. misfit: beta misfit 1.49882E+01 5.14497E+01 1.55427E+01 5.22662E+01 1.67582E+01 5.41032E+01 chosen beta = 1.66886E+01 target misfit = 5.40000E+01 achieved misfit = 5.39963E+01 model norm = 1.42758E+01 misfit change = -3.69261E-02 model norm change = -1.56990E-02 norm comp Ws = 1.40497E+00 norm comp Wx = 1.00935E+01 norm comp Wz = 2.77734E+00 iter cpu time: 0:00:00.17 Target misfit achieved. Minimizing model norm. Iteration 13 beta vs. misfit: beta misfit 1.66898E+01 5.35819E+01 1.66909E+01 5.35835E+01 1.69863E+01 5.40047E+01 chosen beta = 1.69830E+01 target misfit = 5.40000E+01 achieved misfit = 5.39999E+01 model norm = 1.42353E+01 misfit change = -6.75492E-05 model norm change = -2.83385E-03 norm comp Ws = 1.36827E+00 norm comp Wx = 1.00376E+01 norm comp Wz = 2.82945E+00 iter cpu time: 0:00:00.18 Exit at convergence. Iterations performed: 13 total cpu time: 0:00:03.96 DCINV2D ended on:12/10/2015 14:20:17