Deep Learning Approach for Earthquake Parameters Classification in Earthquake Early Warning System

Abstract

Magnitude determination of earthquakes is a mandatory step before an earthquake early warning (EEW) system sends an alarm. Beneficiary users of EEW systems dependon how far they are located from such strong events. Therefore,determining the locations of these shakes is an important is sue for the tranquility of citizens as well. In light of that, this article proposes a magnitude, location, depth, and origin timecategorization using earthquake Ml magnitudes between 2 and 9.The dataset used is the fore and aftershocks of the great Tohokuearthquake of March 11,2011, recorded by three stations fromthe Japanese Hi-net seismic network. The proposed algorithmdepends on a convolutional neural network (CNN) which hasthe ability to extract significant features from waveforms thatenabled the classifier to reach a robust performance in the required earthquake parameters. The classification accuracies ofthe suggested approach for magnitude, origin time, depth, andlocation are 93.67%,89.55%,92.54%,and 89.50%, respectively.