| Budget Amount *help |
¥3,300,000 (Direct Cost: ¥3,300,000)
Fiscal Year 2004: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 2003: ¥2,300,000 (Direct Cost: ¥2,300,000)
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| Research Abstract |
Inversion of subsurface physical parameters from earthquake or microtremor data is one of the often used works in Earthquake Engineering. Usually least-square method is applied in the inversion. However, we have difficulties to find an optimal model because of nonlinear characteristics of misfit function to be minimized. Recently several modem optimization methods without use of gradient information of misfit surface are developed in the field of operation research. In this study, we investigated applicability of heuristic search methods in inversion of wave data.
First, three heuristic search methods, Genetic Algorithm, Simulated Annealing and Tabu Search are implemented to invert Rayleigh wave phase velocity for shallow S-wave velocity profiling. Unlike linearized least-squares inversion, they do not require derivative calculation, or an initial mode, only the forward modeling calculation. The performances of the three heuristic techniques are compared with numerical experiments. With
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common paramerization and search limits, the three algorithms can reconstruct well shallow S-wave profiles with and without a velocity reversal from the synthetic data without any specific initial models. The Genetic Algorithms and the Simulated Annealing show a fast convergence of the misfits. However, the Simulated Annealing can find models with the smallest misfits. It is also found that the decrease of the misfits is the most gradual in the Tabu Search. Next we applied the three methods to inversion of spectral ratios of earthquake records from a vertical bore hole array to estimate S-wave velocity and Q-values. We can observe the same conclusions as obtained above. Furthermore the simulated annealing is used to a joint inversion of resistivity sounding data and microtrernor H/V spectral data. We can estimate resistivity and S-wave profiles simultaneously from this joint inversion. Finally, neural network technique is applied to automatic estimation of onset times of initial P-and S-waves. We can estimate travel time delays due to sedimentary layers in Tokyo area from the estimated travel time data. Less
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