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https://doi.org/10.5194/egusphere-2023-2855
https://doi.org/10.5194/egusphere-2023-2855
11 Dec 2023
 | 11 Dec 2023

Extraction of Pre-earthquake Anomalies in Borehole Strain Data Using Graph WaveNet: A Case Study of the Lushan Earthquake

Chenyang Li, Yu Duan, Ying Han, Zining Yu, Chengquan Chi, and Dewang Zhang

Abstract. On 20 April 2013, Lushan experienced a magnitude 7.0 earthquake. In seismic assessments, borehole strain meters, recognized for their remarkable sensitivity and inherent reliability in tracking crustal deformation, are extensively employed. However, traditional data processing methods encounter challenges when handling massive datasets. This study proposes using a graph wavenet graph neural network to analyze borehole strain data from multiple stations near the earthquake epicenter and establishes a node graph structure using data from four stations near the Lushan epicenter, covering years 2010–2013. After excluding the potential effects of pressure, temperature, and rainfall, we statistically analyzed the pre-earthquake anomalies. Focusing on the Guza, Xiaomiao, and Luzhou stations, which are the closest to the epicenter, the fitting results revealed two accelerations of anomalous accumulation before the earthquake. Approximately four months before the earthquake event, one acceleration suggests the pre-release of energy from a weak fault section. Conversely, the acceleration a few days before the earthquake indicated a strong fault section reaching an unstable state with accumulating strain. We tentatively infer that these two anomalous cumulative accelerations may be related to the preparation phase for a large earthquake. This study highlights the considerable potential of graph neural networks in con-ducting multi-station studies of pre-earthquake anomalies.

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Journal article(s) based on this preprint

22 Jul 2024
Extraction of pre-earthquake anomalies from borehole strain data using Graph WaveNet: a case study of the 2013 Lushan earthquake in China
Chenyang Li, Yu Duan, Ying Han, Zining Yu, Chengquan Chi, and Dewang Zhang
Solid Earth, 15, 877–893, https://doi.org/10.5194/se-15-877-2024,https://doi.org/10.5194/se-15-877-2024, 2024
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

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This study advances the field of earthquake prediction through the introduction of a novel...
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