Multifeature Fusion-Based Earthquake Event Classification Using Transfer Learning

Gwantae Kim, Bonhwa Ku, Hanseok Ko

Abstract: This letter proposes a multifeature fusion model using deep convolution neural networks and transfer learning approach for earthquake event classification. There are several feature representations for seismic analysis, such as the time domain, the frequency domain, and the time–frequency domain. To successfully classify various earthquake events, we propose a novel model that combines these features hierarchically. In addition, we apply a transfer learning to mitigate overfitting problem of deep learning model while achieving high classification performance. To evaluate our approach, we conduct experiments with the Korean peninsula earthquake database from 2016 to 2018 and a large earthquake database on the Circum-Pacific belt in 2019. The experimental results show that the proposed method outperforms over the compared state-of-the-art methods.