EPM–DCNN: Earthquake Prediction Models Using Deep Convolutional Neural Networks

Abstract

ABSTRACT Earthquakes usually cause severe injuries and loss of life, so researchers have developed various methods to predict them. However, the prediction accuracies of these methods are not satisfactory. Unlike most artificial intelligence earthquake prediction methods using earthquake catalogs or seismic wave data, this article proposes three earthquake prediction models based on deep convolutional neural network-based (EPM-DCNN) using 11 continuous earthquake precursory observation item data, including fluid, geomagnetic, and deformation disciplines. To enhance the accuracy of the location prediction of earthquakes, we propose a method to divide the research area into six prediction blocks based on the K-means++ clustering algorithm using the epicenter of historical earthquakes. Using earthquake precursory observation time-series data from 1 January 2015 to 31 December 2018, we construct approximately 34,000 samples by sliding a fixed window size. Each sample is subdivided into 13 categories by combining the magnitude label and prediction block label. The experimental results show that EPM–DCNN B proposed in this article has an accuracy of 99.0% and a recall of 99.8%, which demonstrates the effectiveness of EPM–DCNN for seismic prediction compared to several state-of-the-art baselines.