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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 15, issue 1 | Copyright
Nat. Hazards Earth Syst. Sci., 15, 187-196, 2015
https://doi.org/10.5194/nhess-15-187-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 30 Jan 2015

Research article | 30 Jan 2015

Randomly distributed unit sources to enhance optimization in tsunami waveform inversion

I. E. Mulia and T. Asano I. E. Mulia and T. Asano
  • Department of Ocean and Civil Engineering, Kagoshima University, Kagoshima, Japan

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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In this study, we discuss the development of global stochastic search algorithms to enhance the accuracy of tsunami waveform inversion. Such methods are utilized to search for an optimum spatial distribution of unit sources prior to the inversion. The optimized location of unit sources possessing random characteristics facilitate the traditional least squares inversion to produce a more precise approximation of the sea surface deformation.
In this study, we discuss the development of global stochastic search algorithms to enhance the...
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