LEQNet: Light Earthquake Deep Neural Network for Earthquake Detection and Phase Picking

Lim, Jongseong and Jung, Sunghun and JeGal, Chan and Jung, Gwanghoon and Yoo, Jung Ho and Gahm, Jin Kyu and Song, Giltae (2022) LEQNet: Light Earthquake Deep Neural Network for Earthquake Detection and Phase Picking. Frontiers in Earth Science, 10. ISSN 2296-6463

[thumbnail of pubmed-zip/versions/1/package-entries/feart-10-848237/feart-10-848237.pdf] Text
pubmed-zip/versions/1/package-entries/feart-10-848237/feart-10-848237.pdf - Published Version

Download (1MB)

Abstract

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. In addition, 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 reduced 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 EQTransformer. While our model size is reduced by 87.68% compared to EQTransformer, the performance of our model is comparable to that of EQTransformer.

Item Type: Article
Subjects: STM Open Press > Geological Science
Depositing User: Unnamed user with email support@stmopenpress.com
Date Deposited: 14 Mar 2023 11:09
Last Modified: 25 May 2024 08:49
URI: http://journal.submissionpages.com/id/eprint/649

Actions (login required)

View Item
View Item