Fully Convolutional Networks for Local Earthquake Detection

Youness Choubik,Mohammed Majid,Abdelhak Mahmoudi

doi:10.14569/ijacsa.2021.0120286

Youness Choubik, Mohammed Majid + Show 1 more

Open Access

https://doi.org/10.14569/ijacsa.2021.0120286

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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.

Highlights

Earthquake detection requires discriminating real earthquakes from noise signals, which makes it a classification problem
We describe the application of Fully Convolutional Networks (FCN) for earthquake detection using seismic waveforms from a single seismic station
The Fully Convolutional Network classifier used in this study is comprised of four convolutional layers with different filter numbers and sizes (Fig. 3), followed by batch normalization that normalizes the output of the convolution layer and a ReLU activation function, which enables better training of deeper networks, compared to other activation functions [11], a Global Pooling layer that reduces the amount of parameters in the network to an output prediction for the model

Summary

INTRODUCTION

Earthquake detection requires discriminating real earthquakes from noise signals, which makes it a classification problem. Convolutional Networks (FCN) [24] are a Neural Network architectures that have been successfully applied in many different fields, such as image segmentation[13], [4], medical image analysis [7], [18], character recognition [31], time-series classification [33], [14], [22] and in seismology; for earthquake localization, by taking a window of three-component waveform data from multiple stations and predicting the earthquake location with a 3D image [38], and for fault detection, where the FCN model extracts fault features from synthetic seismic data and recognize the locations of faults with an accuracy of ∼97% [26]. We describe the results and discuss the performance of our classifier Using real continuous seismic data

DATA DESCRIPTION

TRAINING WITH THE FULLY CONVOLUTIONAL NETWORKS

RESULTS AND DISCUSSIONS

CONCLUSION