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A Logit-Based Binary Classifier of Tsunamigenic Earthquakes for the Northwestern Pacific Ocean

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Abstract

Logit analysis is widely used for binary data classification in geoscience. In this study, logistic regression was used as a tool for deriving the binary classifier of tsunamigenic and non-tsunamigenic earthquakes for near-source early warning. The catalogue of submarine earthquakes and tsunami database were merged into one seismic database, in which an additional binary variable associated with the tsunamigenic class (true or false) was assigned to each seismic event. The training dataset consisted of 712 M6.0+ submarine earthquakes, including 80 tsunamigenic and 632 non-tsunamigenic events that occurred in the northwestern part of the Pacific Ocean from 1960 to 2020. The target area has already experienced significant and catastrophic tsunamis. The best performance metrics were archived with the predictors given by the earthquake magnitude, logarithm of the source depth and the seafloor depth in the epicenter location. The current analysis clearly showed that the data-driven logit model significantly improved the performance metrics of the threshold magnitude criteria that are widely used by tsunami warning agencies. Authors suggested that logit-based binary classifier led to improve the tsunami alert efficiency in the Northwestern Pacific Ocean.

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Data Availability

The primary data used in the current study are freely available at the indicated sources. Data that result through preprocessing are available as electronic supplementary material.

Code Availability

Visual Studio Code Source Editor and Python programming language were used in the current study. The following Python libraries were employed: NumPy, Matplotlib, Scikit-learn and Global-land-mask. Code is available upon request.

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Acknowledgements

The authors thank the anonymous reviewer for constructive criticism and valuable remarks which have significantly improved the manuscript.

Funding

Funding was provided by Research program of the Far East Geological Institute of the Far Eastern Branch, Russian Academy of Sciences (grant no. 122040800201-8).

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Authors and Affiliations

  1. Far East Geological Institute, Far Eastern Branch, Russian Academy of Sciences, Vladivostok, Russia

    A. V. Konovalov & A. A. Stepnov

  2. Sakhalin State University, Yuzhno-Sakhalinsk, Russia

    G. A. Samsonov

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  1. A. V. Konovalov
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  2. A. A. Stepnov
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  3. G. A. Samsonov
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Contributions

AVK: conceptualization, methodology, data curation, writing original draft, supervision. AAS: data processing, visualization, editing. GAS: data processing, visualization.

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Correspondence to A. V. Konovalov.

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Konovalov, A.V., Stepnov, A.A. & Samsonov, G.A. A Logit-Based Binary Classifier of Tsunamigenic Earthquakes for the Northwestern Pacific Ocean. Pure Appl. Geophys. 180, 1623–1637 (2023). https://doi.org/10.1007/s00024-022-03194-7

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  • DOI: https://doi.org/10.1007/s00024-022-03194-7

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