Abstract
ABSTRACTEarthquake forecasting is one of the most challenging tasks in the field of seismology that aims to save human life and mitigate catastrophic damages. We have designed a real-time earthquake forecasting framework to forecast earthquakes and tested it in seismogenic regions in southwestern China. The input data are the features provided by the multicomponent seismic monitoring system acoustic electromagnetic to AI (AETA), in which the data are recorded using two types of sensors per station: electromagnetic (EM) and geo-acoustic (GA) sensors. The target is to forecast the location and magnitude of the earthquake that may occur next week, given the data of the current week. The proposed method is based on dimension reduction from massive EM and GA data using principal component analysis, which is followed by random-forest-based classification. The proposed algorithm is trained using the available data from 2016 to 2020 and evaluated using real-time data during 2021. As a result, the testing accuracy reaches 70%, whereas the precision, recall, and F1-score are 63.63%, 93.33%, and 75.66%, respectively. The mean absolute error of the distance and the predicted magnitude using the proposed method compared to the catalog solution are 381 km and 0.49, respectively.
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