A paradigm for developing earthquake probability forecasts based on geoelectric data

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.