Preprints
https://doi.org/10.5194/nhess-2021-378
https://doi.org/10.5194/nhess-2021-378
 
04 Jan 2022
04 Jan 2022
Status: a revised version of this preprint was accepted for the journal NHESS and is expected to appear here in due course.

Hidden-State Modelling of a Cross-section of Geoelectric Time Series Data Can Provide Reliable Intermediate-term Probabilistic Earthquake Forecasting in Taiwan

Haoyu Wen1, Hong-Jia Chen2, Chien-Chih Chen2,3, Massimo Pica Ciamarra1, and Siew Ann Cheong1 Haoyu Wen et al.
  • 1Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371
  • 2Department of Earth Sciences, National Central University, Taoyuan 32001, Taiwan
  • 3Earthquake-Disaster & Risk Evaluation and Management Center, National Central University, Taoyuan 32001, Taiwan

Abstract. Geoelectric time series (TS) has long been studied for its potential for probabilistic earthquake forecasting, and a recent model (GEMSTIP) directly used the skewness and kurtosis of geoelectric TS to provide Time of Increased Probabilities (TIPs) for earthquakes in several months in future. We followed up on this work by applying the Hidden Markov Model (HMM) on the correlation, variance, skewness, and kurtosis TSs to identify two Hidden States (HSs) with different distributions of these statistical indexes. More importantly, we tested whether these HSs could separate time periods into times of higher/lower earthquake probabilities. Using 0.5-Hz geoelectric TS data from 20 stations across Taiwan over 7 years, we first computed the statistical index TSs, and then applied the Baum-Welch Algorithm with multiple random initializations to obtain a well-converged HMM and its HS TS for each station. We then divided the map of Taiwan into a 16-by-16 grid map and quantified the forecasting skill, i.e., how well the HS TS could separate times of higher/lower earthquake probabilities in each cell in terms of a discrimination power measure that we defined. Next, we compare the discrimination power of empirical HS TSs against those of 400 simulated HS TSs, then organized the statistical significance values from these cellular-level hypothesis testing of the forecasting skill obtained into grid maps of discrimination reliability. Having found such significance values to be high for many grid cells for all stations, we proceeded with a statistical hypothesis test of the forecasting skill at the global level, to find high statistical significance across large parts of the hyperparameter spaces of most stations. We therefore concluded that geoelectric TSs indeed contain earthquake-related information, and the HMM approach to be capable at extracting this information for earthquake forecasting.

Haoyu Wen et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-378', Anonymous Referee #1, 15 Jan 2022
    • AC1: 'Reply on RC1', Haoyu Wen, 30 Jan 2022
  • RC2: 'Comment on nhess-2021-378', Anonymous Referee #2, 18 Feb 2022
    • AC2: 'Reply on RC2', Haoyu Wen, 25 Feb 2022
      • RC3: 'Reply on AC2', Anonymous Referee #2, 03 Mar 2022
        • AC3: 'Reply on RC3', Haoyu Wen, 09 Mar 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-378', Anonymous Referee #1, 15 Jan 2022
    • AC1: 'Reply on RC1', Haoyu Wen, 30 Jan 2022
  • RC2: 'Comment on nhess-2021-378', Anonymous Referee #2, 18 Feb 2022
    • AC2: 'Reply on RC2', Haoyu Wen, 25 Feb 2022
      • RC3: 'Reply on AC2', Anonymous Referee #2, 03 Mar 2022
        • AC3: 'Reply on RC3', Haoyu Wen, 09 Mar 2022

Haoyu Wen et al.

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Short summary
Recently, there has been growing interest from earth scientists to use the electric field deep underground to forecast earthquakes. We go one step further by using the electric fields, which can be directly measured, to separate/classify time periods with two labels only according to the statistical properties of the electric fields. By checking against historical earthquake records, we found time periods covered by one of the two labels to have significantly more frequent earthquakes.
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