A Generalized Deep Learning Approach to Seismic Activity Prediction

Abstract

Seismic activity prediction has been a challenging research domain: in this regard, accurate prediction using historical data is an intricate task. Numerous machine learning and traditional approaches have been presented lately for seismic activity prediction; however, no generalizable model exists. In this work, we consider seismic activity predication as a binary classification problem, and propose a deep neural network architecture for the classification problem, using historical data from Chile, Hindukush, and Southern California. After obtaining the data for the three regions, a data cleaning process was used, which was followed by a feature engineering step, to create multiple new features based on various seismic laws. Afterwards, the proposed model was trained on the data, for improved prediction of the seismic activity. The performance of the proposed model was evaluated and compared with extant techniques, such as random forest, support vector machine, and logistic regression. The proposed model achieved accuracy scores of 98.28%, 95.13%, and 99.29% on the Chile, Hindukush, and Southern California datasets, respectively, which were higher than the current benchmark model and classifiers. In addition, we also conducted out-sample testing, where the evaluation metrics confirmed the generality of our proposed approach.