Articles | Volume 22, issue 6
Research article
09 Jun 2022
Research article |  | 09 Jun 2022

Hidden-state modeling of a cross-section of geoelectric time series data can provide reliable intermediate-term probabilistic earthquake forecasting in Taiwan

Haoyu Wen, Hong-Jia Chen, Chien-Chih Chen, Massimo Pica Ciamarra, and Siew Ann Cheong

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Manuscript not accepted for further review
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Cited articles

Abdel-Hamid, O. and Jiang, H.: Fast speaker adaptation of hybrid NN/HMM model for speech recognition based on discriminative learning of speaker code, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 7942–7946,, 2013. 
Asim, K., Martínez-Álvarez, F., Basit, A., and Iqbal, T.: Earthquake magnitude prediction in Hindukush region using machine learning techniques, Nat. Hazards, 85, 471–486, 2017. 
Asim, K. M., Idris, A., Martínez-Álvarez, F., and Iqbal, T.: Short term earthquake prediction in Hindukush region using tree based ensemble learning, 2016 International conference on frontiers of information technology (FIT), 365–370,, 2016. 
Batac, R. C. and Kantz, H.: Observing spatio-temporal clustering and separation using interevent distributions of regional earthquakes, Nonlin. Processes Geophys., 21, 735–744,, 2014. 
Beyreuther, M. and Wassermann, J.: Continuous earthquake detection and classification using discrete Hidden Markov Models, Geophys. J. Int., 175, 1055–1066, 2008. 
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.
Final-revised paper