Autoencoder based Features Extraction for Automatic Classification of Earthquakes and Explosions

• Published in: 2018 IEEE ACIS 17th International Conference on Computer and Information Science (ICIS) pp. 445 - 450
• Main Authors: Saad, Omar M., Inoue, K, Shalaby, Ahmed, Sarny, Lotfy, Sayed, Mohammed S.
• Format: Conference Proceeding
• Language: English; Japanese
• Published: IEEE 01.06.2018
• Subjects: Accuracy; Autoencoder; Autoencoders; Automatic Classification; Buildings; Classification algorithms; Deep Learning; Earthquakes; Explosions; Feature extraction; Government; Monitoring; Shape
• DOI: 10.1109/ICIS.2018.8466464

Monitoring illegal explosions is mandatory for the safety of human life, environment, and protect the important buildings such as High-dam in Egypt. This kind of monitoring can be accomplished by detecting and identifying the explosions. If an illegal explosion happens such as quarry blast, an alarm should be reported to the government to take immediate action. However, the main problem is that many measured signals from received explosions are similar to earthquakes in their shape and both cannot differentiate from each other. Also, incorrect classification possibly will distort the real seismicity nature of the region. This problem motivates us to search for unique discriminating features to distinguish between earthquakes and explosions with precise accuracy. Therefore, in this paper, we propose to extract the discriminative features based on Autoencoder from the first few seconds after the P-wave arrival time of the event. The discriminative features are found to be in the first 60 samples after the arrival time of P-wave. Thus the first stage of the proposed algorithm is extracting the discriminative features via the Autoencoder. Then, softmax classifies the event based on these extracted features. The proposed algorithm achieves a classification accuracy of 98.55% when applied to 900 earthquakes and quarry blasts waveforms recorded by Egyptian National Seismic Network (ENSN).