A paradigm for developing earthquake probability forecasts based on geoelectric data

TL;DR

This paper improves a geoelectric signal-based earthquake forecasting algorithm by removing time parameters, extending to multiple stations, and identifying optimal frequency bands, aiming to enhance probabilistic earthquake prediction.

Contribution

It introduces an improved algorithm for earthquake forecasting using geoelectric data, including multi-station analysis and optimal frequency band identification.

Findings

Enhanced algorithm with no time parameter for coarse-graining

Identification of frequency bands with highest signal-to-noise ratio

Evidence of a seismoelectric relationship suitable for machine learning

Abstract

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.