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DOI: 10.14569/IJACSA.2021.0120286
PDF

Fully Convolutional Networks for Local Earthquake Detection

Author 1: Youness Choubik
Author 2: Abdelhak Mahmoudi
Author 3: Mohammed Majid Himmi

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 2, 2021.

  • Abstract and Keywords
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Abstract: Automatic earthquake detection is widely studied to replace manual detection, however, most of the existing methods are sensitive to seismic noise. Hence, the need for Machine and Deep Learning has become more and more significant. Regardless of successful applications of the Fully Convolutional Networks (FCN) in many different fields, to the best of our knowledge, they are not yet applied in earthquake detection. In this paper, we propose an automatic earthquake detection model based on FCN classifier. We used a balanced subset of STanford EArthquake Dataset (STEAD) to train and validate our classifier. Each sample from the subset is re-sampled from 100Hz to 50Hz then normalized. We investigated different, widely used, feature normalization methods, which consist of normalizing all features in the same range, and we showed that feature normalization is not suitable for our data. On the contrary, sample normalization, which consists of normalizing each sample of our dataset individually, improved the accuracy of our classifier by ∼16% compared to using raw data. Our classifier exceeded 99% on training data, compared to 􀀀83% when using raw data. To test the efficiency of our classifier, we applied it to real continuous seismic data from XB Network from Morocco and compared the results to our catalog containing 77 earthquakes. Our results show that we could detect 75 out of 77 earthquakes contained in the catalog.

Keywords: Earthquake detection; fully convolutional networks; data normalization; classification

Youness Choubik, Abdelhak Mahmoudi and Mohammed Majid Himmi, “Fully Convolutional Networks for Local Earthquake Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 12(2), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120286

@article{Choubik2021,
title = {Fully Convolutional Networks for Local Earthquake Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120286},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120286},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {2},
author = {Youness Choubik and Abdelhak Mahmoudi and Mohammed Majid Himmi}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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