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: 1/12/2016 16:42:18 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-02 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: 9 # of data: 45 chifact: 1.00000E+00 target misfit: 4.50000E+01 mesh was read from: Mesh_2D.msh # of cells: 81 x 45 total # of cells: 3645 # of active cells: 3645 # of unique data locations: 9 # 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-02 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: 17 + 3 + 1 = 21 init cpu time: 0:00:00.18 initial misfit = 2.57080E+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 1.11493E+03 4.74485E+04 2.22985E+03 5.68364E+04 5.57463E+03 8.02516E+04 1.39366E+04 1.21526E+05 1.57750E+04 1.28279E+05 1.58488E+04 1.28536E+05 1.58499E+04 1.28540E+05 3.96247E+04 1.79392E+05 chosen beta = 1.58499E+04 target misfit = 1.28540E+05 achieved misfit = 1.28540E+05 model norm = 3.63297E+00 misfit change = 5.00000E-01 model norm change = 0.00000E+00 norm comp Ws = 2.88514E+00 norm comp Wx = 3.70226E-01 norm comp Wz = 3.77603E-01 iter cpu time: 0:00:01.20 Iteration 2 beta vs. misfit: beta misfit 3.96247E+03 6.15053E+04 7.92494E+03 9.46707E+04 chosen beta = 4.25263E+03 target misfit = 6.42701E+04 achieved misfit = 6.42774E+04 model norm = 1.37169E+01 misfit change = 4.99943E-01 model norm change = 2.77567E+00 norm comp Ws = 1.06979E+01 norm comp Wx = 1.61088E+00 norm comp Wz = 1.40814E+00 iter cpu time: 0:00:00.78 Iteration 3 beta vs. misfit: beta misfit 1.06316E+03 2.49833E+04 2.12631E+03 4.10044E+04 chosen beta = 1.51222E+03 target misfit = 3.21387E+04 achieved misfit = 3.17674E+04 model norm = 2.91306E+01 misfit change = 5.05777E-01 model norm change = 1.12370E+00 norm comp Ws = 2.19928E+01 norm comp Wx = 4.11507E+00 norm comp Wz = 3.02271E+00 iter cpu time: 0:00:01.00 Iteration 4 beta vs. misfit: beta misfit 3.78054E+02 1.14051E+04 7.56108E+02 1.87488E+04 chosen beta = 6.00000E+02 target misfit = 1.58837E+04 achieved misfit = 1.56569E+04 model norm = 4.84312E+01 misfit change = 5.07137E-01 model norm change = 6.62554E-01 norm comp Ws = 3.49213E+01 norm comp Wx = 8.32898E+00 norm comp Wz = 5.18098E+00 iter cpu time: 0:00:00.86 Iteration 5 beta vs. misfit: beta misfit 1.50000E+02 5.05151E+03 3.00000E+02 8.76679E+03 chosen beta = 2.60199E+02 target misfit = 7.82847E+03 achieved misfit = 7.79602E+03 model norm = 6.97577E+01 misfit change = 5.02073E-01 model norm change = 4.40344E-01 norm comp Ws = 4.74916E+01 norm comp Wx = 1.43993E+01 norm comp Wz = 7.86671E+00 iter cpu time: 0:00:00.88 Iteration 6 beta vs. misfit: beta misfit 6.50498E+01 1.83324E+03 1.30100E+02 3.68412E+03 1.37599E+02 3.90387E+03 chosen beta = 1.37400E+02 target misfit = 3.89801E+03 achieved misfit = 3.89800E+03 model norm = 8.95751E+01 misfit change = 5.00001E-01 model norm change = 2.84090E-01 norm comp Ws = 5.75580E+01 norm comp Wx = 2.11440E+01 norm comp Wz = 1.08731E+01 iter cpu time: 0:00:00.81 Iteration 7 beta vs. misfit: beta misfit 3.43499E+01 8.99667E+02 6.86999E+01 1.65819E+03 8.25106E+01 2.00613E+03 chosen beta = 8.02499E+01 target misfit = 1.94900E+03 achieved misfit = 1.94758E+03 model norm = 1.06566E+02 misfit change = 5.00364E-01 model norm change = 1.89686E-01 norm comp Ws = 6.57720E+01 norm comp Wx = 2.69488E+01 norm comp Wz = 1.38454E+01 iter cpu time: 0:00:00.89 Iteration 8 beta vs. misfit: beta misfit 2.00625E+01 5.63946E+02 4.01250E+01 9.43655E+02 4.18598E+01 9.78372E+02 chosen beta = 4.16303E+01 target misfit = 9.73791E+02 achieved misfit = 9.73755E+02 model norm = 1.22904E+02 misfit change = 5.00019E-01 model norm change = 1.53314E-01 norm comp Ws = 7.39851E+01 norm comp Wx = 3.20341E+01 norm comp Wz = 1.68852E+01 iter cpu time: 0:00:00.91 Iteration 9 beta vs. misfit: beta misfit 1.04076E+01 3.54740E+02 2.08152E+01 5.47348E+02 chosen beta = 1.72632E+01 target misfit = 4.86877E+02 achieved misfit = 4.81217E+02 model norm = 1.41185E+02 misfit change = 5.05813E-01 model norm change = 1.48742E-01 norm comp Ws = 8.27069E+01 norm comp Wx = 3.78831E+01 norm comp Wz = 2.05954E+01 iter cpu time: 0:00:00.78 Iteration 10 beta vs. misfit: beta misfit 4.31579E+00 2.09965E+02 8.63158E+00 2.93092E+02 chosen beta = 5.72809E+00 target misfit = 2.40608E+02 achieved misfit = 2.34998E+02 model norm = 1.66836E+02 misfit change = 5.11659E-01 model norm change = 1.81682E-01 norm comp Ws = 9.43748E+01 norm comp Wx = 4.67077E+01 norm comp Wz = 2.57538E+01 iter cpu time: 0:00:00.66 Iteration 11 beta vs. misfit: beta misfit 1.43202E+00 1.08765E+02 2.86405E+00 1.48776E+02 chosen beta = 1.69894E+00 target misfit = 1.17499E+02 achieved misfit = 1.13855E+02 model norm = 2.07214E+02 misfit change = 5.15505E-01 model norm change = 2.42021E-01 norm comp Ws = 1.10709E+02 norm comp Wx = 6.50107E+01 norm comp Wz = 3.14941E+01 iter cpu time: 0:00:00.81 Iteration 12 beta vs. misfit: beta misfit 4.24735E-01 4.69288E+01 8.49470E-01 5.82083E+01 chosen beta = 7.90778E-01 target misfit = 5.69276E+01 achieved misfit = 5.61534E+01 model norm = 2.39426E+02 misfit change = 5.06799E-01 model norm change = 1.55454E-01 norm comp Ws = 1.22208E+02 norm comp Wx = 7.90861E+01 norm comp Wz = 3.81319E+01 iter cpu time: 0:00:00.72 Iteration 13 beta vs. misfit: beta misfit 5.07840E-01 2.85576E+01 6.33710E-01 3.23824E+01 1.13145E+00 5.17204E+01 chosen beta = 9.52357E-01 target misfit = 4.50000E+01 achieved misfit = 4.40177E+01 model norm = 2.37754E+02 misfit change = 2.16117E-01 model norm change = -6.98309E-03 norm comp Ws = 1.26074E+02 norm comp Wx = 7.63269E+01 norm comp Wz = 3.53536E+01 iter cpu time: 0:00:01.02 Iteration 14 beta vs. misfit: beta misfit 9.73609E-01 3.64390E+01 9.95336E-01 3.71506E+01 1.23871E+00 4.57613E+01 chosen beta = 1.21709E+00 target misfit = 4.50000E+01 achieved misfit = 4.49504E+01 model norm = 2.30537E+02 misfit change = -2.11895E-02 model norm change = -3.03571E-02 norm comp Ws = 1.26466E+02 norm comp Wx = 7.21243E+01 norm comp Wz = 3.19461E+01 iter cpu time: 0:00:01.21 Target misfit achieved. Minimizing model norm. Iteration 15 beta vs. misfit: beta misfit 1.21843E+00 3.86348E+01 1.21978E+00 3.86736E+01 1.44018E+00 4.53382E+01 chosen beta = 1.42896E+00 target misfit = 4.50000E+01 achieved misfit = 4.49850E+01 model norm = 2.28950E+02 misfit change = -7.69307E-04 model norm change = -6.88525E-03 norm comp Ws = 1.25759E+02 norm comp Wx = 6.90749E+01 norm comp Wz = 3.41159E+01 iter cpu time: 0:00:00.76 Exit at convergence. Iterations performed: 15 total cpu time: 0:00:13.54 DCINV2D ended on: 1/12/2016 16:42:31