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<p>This paper presents a methodology for selecting an effective set of sensors in an existing tsunami monitoring system, providing the data to reconstruct the initial tsunami waveform and to determine the amplitudes of incoming waves at given locations. Remote tsunami waveforms are inverted using the truncated singular value decomposition (SVD) method. The relationship between the configuration of the monitoring system and the main inversion parameters is illustrated with examples under modelled formulations. It is found that the inversion results can be significantly improved by using data from deep-sea sensors located along the directions of most intense tsunami energy propagation. The ‘informative value’ of the data can be estimated from the intensity of the energy transfer. To select optimal sensor locations, we propose a methodology based on the estimation of the specific energy distribution of tsunami waves. It is shown that in the framework of our approach, using the most informative data, it is possible not only to reconstruct the tsunami source, but also to determine the amplitudes of the incoming waves at locations where no observations were available (forecast points), provided they were included in the preliminary calculations. The proposed methodology is illustrated with the example of a real tsunami in the Solomon Islands on 6 February 2013 and presented in the form of a diagram.</p>

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On the assessment of the efficiency of deep-sea monitoring sensors for a tsunami warning system

  • 期刊论文

  • 发表日期：

    2025年6月2日

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  • Tatyana A. Voronina,
  • Vladislav V. Voronin

摘要

This paper presents a methodology for selecting an effective set of sensors in an existing tsunami monitoring system, providing the data to reconstruct the initial tsunami waveform and to determine the amplitudes of incoming waves at given locations. Remote tsunami waveforms are inverted using the truncated singular value decomposition (SVD) method. The relationship between the configuration of the monitoring system and the main inversion parameters is illustrated with examples under modelled formulations. It is found that the inversion results can be significantly improved by using data from deep-sea sensors located along the directions of most intense tsunami energy propagation. The ‘informative value’ of the data can be estimated from the intensity of the energy transfer. To select optimal sensor locations, we propose a methodology based on the estimation of the specific energy distribution of tsunami waves. It is shown that in the framework of our approach, using the most informative data, it is possible not only to reconstruct the tsunami source, but also to determine the amplitudes of the incoming waves at locations where no observations were available (forecast points), provided they were included in the preliminary calculations. The proposed methodology is illustrated with the example of a real tsunami in the Solomon Islands on 6 February 2013 and presented in the form of a diagram.