Attention-Based Fully Convolutional DenseNet for Earthquake Detection

Abstract

We propose a novel deep learning method using an attention-based fully convolutional dense network (FCDNet) for automatic earthquake detection. The FCDNet consists of encoder-decoder parts with skip connections, where each encode-decoder block contains a block of densely connected layers to enhance the feature learning capability. The spatial attention mechanism is added within the FCDNet to assign greater attention to useful features and hence improve the accuracy of earthquake detection. The time-frequency representations of three-component seismograms produced by the Stockwell transform are used for better extracting the hidden data features. The attention-based FCDNet extracts the time-frequency features needed for distinguishing the seismic signal from the background noise. We evaluate the performance of the proposed method using a Mediterranean dataset. The attention-based FCDNet is trained using 90% of the Mediterranean dataset and tested using the remaining 10%. Accordingly, the training and testing accuracies are 97.71% and 97.02%, respectively. The intersection over union (IoU), precision, recall, and F1-score of the attention-based FCDNet are 93.80%, 99.72%, 99.55%, and 99.64%, respectively. Moreover, to evaluate the generalization ability of the trained model, we utilize 100,000 seismic waveforms recorded in different seismic regions from the global STanford EArthquake Dataset (STEAD) dataset for testing, which shows robust performance. We also apply the attention-based FCDNet to the Japanese seismic data and compare the performance to the CRED and SCALODEEP methods. The attention-based FCDNet outperforms the benchmark methods and achieves a higher detection accuracy of 99.46%. The attention-based FCDNet is additionally evaluated using one-day continuous seismic data recording a seismic swarm that occurred in the Helike region. As a result, the attention-based FCDNet recognizes 135 earthquakes and raises 15 false alarms with a detection accuracy of 90.06%.