LSTM-Autoencoder Network for the Detection of Seismic Electric Signals

Abstract

Seismic electric signals (SESs) are essential short-term precursors of earthquakes. Accurate and efficient detection of SESs is significant to short-term predictions of earthquakes. However, SESs are usually disturbed by various noises and are thus difficult to recognize. Although conventional techniques have made substantial efforts in improving the SES detection accuracy, the success rates of SES detection at certain stations are still less satisfactory due to the complexity and diversity of noises. In this study, we apply deep learning to extract SESs and develop a novel deep learning network based on geoelectric field characteristics by combining the long short-term memory (LSTM) blocks with an autoencoder structure and a time-step attention module. The detection results of both synthetic and real data demonstrate that our proposed network yields superior performance in detecting embedded SESs in the presence of severe noise interference compared with traditional methods and several well-known networks. Moreover, our novel network shows the excellent ability of massive data processing, generalization and migration, which can process one-day’s worth of data in only milliseconds, adapt to SESs whose durations and amplitudes are different from those of the training set and be easily transferred to newly acquired data. The proposed novel method can provide more efficient and accurate detection results, which will broaden the data availability of hazard mitigation based on SESs.