LEQNet: Light Earthquake Deep Neural Network for Earthquake Detection and Phase Picking - Lim, Jongseong; Jung, Sunghun; JeGal, Chan; Jung, Gwanghoon; Yoo, Jung Ho; Gahm, Jin Kyu; Song, Giltae (Frontiers in Earth Science, 2022)
Developing seismic signal detection and phase picking is an essential step for an on-site early earthquake warning system. A few deep learning approaches have been developed to improve the accuracy of seismic signal detection and phase picking. To run the existing deep learning models, high-throughput computing resources are required. Additionally, the deep learning architecture must be optimized for mounting the model in small devices using low-cost sensors for earthquake detection. In this study, we designed a lightweight deep neural network model that operates on a very small device. We reduce the size of the deep learning model using the deeper bottleneck, recursive structure, and depthwise separable convolution. We evaluated our lightweight deep learning model using the Stanford Earthquake Dataset and compared it with the EQTransformer. While our model size is reduced by 87.68% compared to EQTransformer, the performance of our model is comparable to EQTransformer.