Initial tsunami source estimation by inversion with an intelligent selection of model parameters and time delays

We propose a method for accurately estimating the initial tsunami source. Our technique is independent of the earthquake parameters, because we only use recorded tsunami waveforms and an auxiliary basis function, instead of a fault model. We first use the measured waveforms to roughly identify the source area using backward propagated travel times, and then infer the initial sea surface deformation through inversion analysis. A computational intelligence approach based on a genetic algorithm combined with a pattern search was used to select appropriate least squares model parameters and time delays. The proposed method significantly reduced the number of parameters and suppressed the negative effect of regularization schemes that decreased the plausibility of the model. Furthermore, the stochastic approach for deriving the time delays is a more flexible strategy for simulating actual phenomena that occur in nature. The selected parameters and time delays increased the accuracy, and the model's ability to reveal the underlying physics associated with the tsunami-generating processes. In this paper, we applied the method to the 2011 Tohoku-Oki tsunami event and examined its effectiveness by comparing the results to those using the conventional method.