Real-Time Classification of Earthquake using Deep Learning

Existing Earthquake Early Warning Systems (EEWSs) calculates the location and magnitude of an earthquake using real-time waveforms from seismic stations within a few seconds. Typically, three to six stations are necessary to estimate earthquake parameters. Waiting for primary (P-) wave information from closest stations results in a blind-zone area where the arrival of secondary (S-) wave cannot be provided around the epicenter of an earthquake. If an earthquake occurred under a city center, EEWSs would not work even though each building has a seismic sensor in a smart city in future. Here, we present a methodology to classify earthquake vibrations into near-source or far-source within one second after P-wave detection. This will allow warnings to citizens who are the residence of earthquake epicenter in case of an earthquake very close by. We trained a deep learning Long Short-Term Memory (LSTM) network for sequence-to -label classification. 305 three component accelerations recorded between 2000 and 2018 in Japan are used to train the artificial network by extracting thirteen features of one second of P-wave. The accuracy of the methodology is 98.2%. 54 out of 55 near-source waveforms classified correctly and only 2 of 80 waveforms were misclassified. We tested the LSTM network with 2018 Northern Osaka (M 6.1.) earthquakes in Japan where closest stations are correctly identified with 83.3% accuracy. Therefore, smart cities donated with smart automated shut-on/off machines and sensors will be more resilient against earthquake disaster even EEWSs are not available in the blind zone area in future. (C) 2018 The Authors. Published by Elsevier B.V.