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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.

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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.

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