  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.
