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We examine the precursory behavior of geoelectric signals before largeearthquakes by means of an algorithm including an alarm-based model and binaryclassification. This algorithm, introduced originally by Chen and Chen [Nat.Hazards., 84, 2016], is improved by removing a time parameter forcoarse-graining of earthquake occurrences, as well as by extending the singlestation method into a joint stations method. We also determine the optimalfrequency bands of earthquake-related geoelectric signals with the highestsignal-to-noise ratio. Using significance tests, we also provide evidence of anunderlying seismoelectric relationship. It is appropriate for machine learningto extract this underlying relationship, which could be used to quantifyprobabilistic forecasts of impending earthquakes, and to get closer tooperational earthquake prediction.

A paradigm for developing earthquake probability forecasts based on geoelectric data