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.