Dlep: A Deep Learning Model For Earthquake Prediction

Earthquakes are one of the most costly natural disasters facing human beings, which happens without an explicit warning, therefore earthquake prediction becomes a very important and challenging task for humanity. Although many existing methods attempt to address this task, most of them use either seismic indicators (explicit features) designed by geologists, or feature vectors (implicit features) extracted by deep learning methods, to characterize an earthquake 14 earthquake prediction. The problem of combining these two kind of features to improve final earthquake prediction performance remains pretty much open. To this end, we propose a deep learning model named DEEP to effectively fuse the explicit and implicit features for accurate earthquake prediction. In DEEP, we adopt eight precursory pattern-based indicators as the explicit features, and use a convolutional neural network (CNN) to extract implicit features. Then, an attention-based strategy is suggested to fuse these two kinds of features well. In addition, a dynamic loss function is designed to deal with the category imbalance of seismic data. Finally, experimental results on eight datasets from different regions demonstrate the effectiveness of the proposed DLEP for earthquake prediction comparing to several state-of-the-art baselines.