2019/07/12 by Hong‐Jia Chen, Chien-Chih Chen, Chen, Hong-Jia +5
Computer Science · Earth and Planetary Sciences · #Earthquake Detection and Analysis #FOS: Physical sciences #Geophysics (physics.geo-ph) #Seismology and Earthquake Studies #earthquake and tectonic studies
paper · pdf · doi:10.48550/arxiv.1907.05623
openalex publication_date 2019/07/12 · openalex created_date 2019/07/23 · openalex updated_date 2026/07/28
We examine the precursory behavior of geoelectric signals before large earthquakes by means of an algorithm including an alarm-based model and binary classification. This algorithm, introduced originally by Chen and Chen [Nat. Hazards., 84, 2016], is improved by removing a time parameter for coarse-graining of earthquake occurrences, as well as by extending the single station method into a joint stations method. We also determine the optimal frequency bands of earthquake-related geoelectric signals with the highest signal-to-noise ratio. Using significance tests, we also provide evidence of an underlying seismoelectric relationship. It is appropriate for 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.