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A paradigm for developing earthquake probability forecasts based on geoelectric data

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Abstract

We examine the precursory behavior of geoelectric signals before large earthquakes by means of a previously published algorithm including an alarm-based model and binary classification [H.-J. Chen, C.-C. Chen, Nat. Hazards 84, 877 (2016)]. The original method has been improved by removing a time parameter used for coarse-graining of earthquake occurrences, as well as by extending the single-station method into a joint-stations method. Analyzing the filtered geoelectric data with different frequency bands, we determine the optimal frequency bands of earthquake-related geoelectric signals featuring the highest signal-to-noise ratio. Based on significance tests, we also provide evidence of a relationship between geoelectric signals and seismicity. We suggest using machine learning to extract this underlying relationship, which could be used to quantify probabilistic forecasts of impending earthquakes and to get closer to operational earthquake prediction.

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Author information

Authors and Affiliations

  1. Department of Earth Sciences, National Central University, Taoyuan, 32001, Taiwan

    Hong-Jia Chen & Chien-Chih Chen

  2. Earthquake-Disaster & Risk Evaluation and Management Center, National Central University, Taoyuan, 32001, Taiwan

    Chien-Chih Chen

  3. Lithophyse, Nice, 06300, France

    Guy Ouillon

  4. Department of Management, Technology and Economics, ETH Zürich, Zürich, 8092, Switzerland

    Didier Sornette

Authors
  1. Hong-Jia Chen
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  2. Chien-Chih Chen
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  3. Guy Ouillon
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  4. Didier Sornette
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Correspondence to Hong-Jia Chen.

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Supplementary material in the form of one pdf file available from the Journal web page at https://doi.org/10.1140/epjst/e2020-000258-9.

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Chen, HJ., Chen, CC., Ouillon, G. et al. A paradigm for developing earthquake probability forecasts based on geoelectric data. Eur. Phys. J. Spec. Top. 230, 381–407 (2021). https://doi.org/10.1140/epjst/e2020-000258-9

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