Abstract
Abstract. In tsunami waveform inversion using the conventional Green's function technique, an optimal solution is sometimes difficult to obtain because of various factors. This study proposes a new method to both optimize the determination of the unknown parameters and introduce a global optimization method for tsunami waveform inversion. We utilize a genetic algorithm that further enhanced by a pattern search method to find an optimal distribution of unit source locations prior to the inversion. Unlike the conventional method that characterized by equidistant unit sources, our method generates a random spatial distribution of unit sources inside the inverse region. This leads to a better approximation of the initial profile of a tsunami. The method has been tested using an artificial tsunami source with real bathymetry data. Comparison results demonstrate that the proposed method has considerably outperformed the conventional one in terms of model accuracy.
Highlights
Direct observation of sea surface deformation after the occurrence of an earthquake is still difficult to obtain; its estimation is often performed by consideration of relevant seismic information or the hydrodynamic response of the sea determined from recorded tsunami waveforms
We proposed a new approach to tackle the same problem by determining the optimal position or spatial distribution of unit sources located around the tsunami source or epicenter
Alternative distributions to represent the initial water height should be considered because the tsunami source does not always follow the Gaussian distribution
Summary
Direct observation of sea surface deformation after the occurrence of an earthquake is still difficult to obtain; its estimation is often performed by consideration of relevant seismic information or the hydrodynamic response of the sea determined from recorded tsunami waveforms. A more realistic approach was proposed by Satake (1987) who analyzed recorded waveforms to infer earthquake source parameters or coseismic slip, using the Green’s function technique. This study is in line with that of Aida because we are more interested in estimating sea surface deformation than a slip on the fault plane The motivation behind this is that tsunami excitation can sometimes occur as a result of various factors that are independent of the associated seismic characteristics (Geist, 2002). Several studies using tsunami waveform inversion to estimate sea surface deformation without fault model assumptions have been widely developed. Baba et al (2005) used a simplified fault model by disregarding actual earthquake parameters to produce the initial profile on each unit source, whereas Satake et al (2005) proposed a more direct approximation using a pyramidal shape with a flat top. The Green’s function evolves dynamically at each generation of the GA and PS iteration
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