Autoencoder based Features Extraction for Automatic Classification of Earthquakes and Explosions

Omar M. Saad, Inoue Koji, Ahmed Shalaby, Lotfy Sarny, Mohammed S. Sayed

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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).

Original languageEnglish
Title of host publicationProceedings - 17th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2018
EditorsWei Xiong, Wenqiang Shang, Simon Xu, Hwee-Kuan Lee
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages445-450
Number of pages6
ISBN (Electronic)9781538658925
DOIs
Publication statusPublished - Sep 14 2018
Event17th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2018 - Singapore, Singapore
Duration: Jun 6 2018Jun 8 2018

Publication series

NameProceedings - 17th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2018

Other

Other17th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2018
CountrySingapore
CitySingapore
Period6/6/186/8/18

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems
  • Media Technology

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  • Cite this

    Saad, O. M., Koji, I., Shalaby, A., Sarny, L., & Sayed, M. S. (2018). Autoencoder based Features Extraction for Automatic Classification of Earthquakes and Explosions. In W. Xiong, W. Shang, S. Xu, & H-K. Lee (Eds.), Proceedings - 17th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2018 (pp. 445-450). [8466464] (Proceedings - 17th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIS.2018.8466464