Fully Convolutional Networks for Local Earthquake Detection

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.