Japan Geoscience Union Meeting 2015

Presentation information

International Session (Oral)

Symbol S (Solid Earth Sciences) » S-SS Seismology

[S-SS02] Frontier studies on subduction zone megathrust earthquakes and tsunamis

Mon. May 25, 2015 11:00 AM - 12:45 PM IC (2F)

Convener:*Kyuichi Kanagawa(Graduate School of Science, Chiba University), Demian Saffer(Dept. of Geosciences, The Pennsylvania State University, USA), Michael Strasser(Geological Institute, Seiss Federal Insitute of Technology ETH Zurich), Yasuhiro Yamada(Depertment of Urban Management Engineering, Kyoto University), Shuichi Kodaira(Institute for Research on Earth Evolution Japan Agency for Marine-Earth Science and Technology), Ryota Hino(International Research Institute of Disaster Science, Tohoku University), Kohtaro Ujiie(Graduate School of Life and Environmental Sciences, University of Tsukuba), Yoshihiro Ito(Disaster Prevention Research Institute, Kyoto University), Chair:Yoshihiro Ito(Disaster Prevention Research Institute, Kyoto University), Yasuhiro Yamada(Japan Agency for Marine-Earth Science and Technology)

11:15 AM - 11:30 AM

[SSS02-08] Proper selection of least squares parameters in tsunami waveform inversion through computational intelligence

*Iyan E. MULIA1, Toshiyuki ASANO1 (1.Department of Ocean and Civil Engineering, Kagoshima University)

Keywords:Tsunami waveform inversion, Computational intelligence, 2011 Tohoku-Oki tsunami

Nature-inspired computational methodologies have been successfully applied to many geophysical optimization problems. One of the advantages of such methods is the ability to avoid local optimum solutions, which is something classical methods fail to do. This is because the methods require no derivative information and thus the search is not restricted by the local gradient of the objective function. This feature is of importance in real world applications, where the optimization problems are often characterized by irregular error surfaces that are sometimes non-differentiable. In the standard tsunami waveform inversion based on the Green’s function superposition, the linear assumption allows model parameters to be determined straightforwardly using least squares. Therefore, the use of a more advanced method is unnecessary, unless a different design parameter is introduced leading towards a better solution. To that end, we proposed a utilization of computational intelligence in the tsunami waveform inversion using distinct design parameters.

In this study, a tsunami waveform inversion without the fault model assumption is used to test our proposed method. We develop an optimization method based on a genetic algorithm that further enhanced by a pattern search method to find optimal model parameters for the least squares inversion, which mainly dependent on the spatial distribution. Here, we do not use the method for directly estimating the unknown parameters formulated in the least squares inversion. In addition, we apply the same algorithm to determine the water movement initiation time at specified locations inside the inverse region or tsunami source-influenced area. This is applicable to cases where the transient deformation is not negligible. The results suggest that the method not only leads to better accuracy, but also increases the ability to reveal the underlying physics associated with the tsunami generation process. The method has been applied to both artificial and real event of the 2011 Tohoku-Oki tsunami.