A convolutional neural network-based classification of local earthquakes and tectonic tremors in Sanriku-oki, Japan, using S-net data

Low-frequency tremors have been widely detected in many tectonic zones, and are often located adjacent to megathrust zones, indicating that their spatiotemporal evolution provides important insights into megathrust events. The envelope correlation method (ECM) is commonly used to detect tremors. How...

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Bibliographic Details
Published in:	Earth, planets, and space Vol. 73; no. 1; pp. 1 - 10
Main Authors:	Takahashi, Hidenobu, Tateiwa, Kazuya, Yano, Keisuke, Kano, Masayuki
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 15-10-2021 Springer Springer Nature B.V SpringerOpen
Subjects:	4. Seismology Artificial neural networks Classification Convolutional neural network Earth and Environmental Science Earth Sciences Earthquakes Event classification Express Letter Geology Geophysics/Geodesy Identification and classification Japan Trench Neural networks Noise Railway stations S-net Seismic activity Slow earthquakes Spectrograms Tectonics Tectonics (Geology) Tremors Waveforms Japan Japan Trench Slow earthquakes S-net Tremors Convolutional neural network Event classification
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Summary:	Low-frequency tremors have been widely detected in many tectonic zones, and are often located adjacent to megathrust zones, indicating that their spatiotemporal evolution provides important insights into megathrust events. The envelope correlation method (ECM) is commonly used to detect tremors. However, the ECM also detects regular earthquakes, which requires the separation of these two signals after the initial detection. In addition, signals of tremors are weak, so classifying tremors from noises is also an essential problem. We develop a convolutional neural network (CNN)-based method using a single S-net station located off Sanriku region, Northeast Japan, to classify local earthquakes, tremors, and noise. Along the Japan Trench, especially in a region focused in this study, local earthquakes and tremors occurred in coexistence within a small region, so detection, location, and discrimination of these events are the key to understand the relationship between slow and regular earthquakes. The spectrograms of the three-component velocity waveforms that were recorded during 16 August 2016 to 14 August 2018 are used as the training and test datasets for the CNN. The CNN successfully classified 100%, 96%, and 98% of the earthquakes, tremors, and noise, respectively. We also showed a successful application of our method to continuous waveform data including a tremor to explore the feasibility of the proposed method in classifying tremors and noise in continuous streaming data. The output probabilities for the true classifications decrease with increasing epicentral distance and/or decreasing event magnitude. This highlights the need to train the CNN using tremors proximal to the seismic stations for detecting tremors using multiple stations.
ISSN:	1880-5981 1343-8832 1880-5981
DOI:	10.1186/s40623-021-01524-y