Articles | Volume 25, issue 1
https://doi.org/10.5194/nhess-25-231-2025
https://doi.org/10.5194/nhess-25-231-2025
Research article
 | 
14 Jan 2025
Research article |  | 14 Jan 2025

Analysis of borehole strain anomalies before the 2017 Jiuzhaigou Ms 7.0 earthquake based on a graph neural network

Chenyang Li, Changfeng Qin, Jie Zhang, Yu Duan, and Chengquan Chi

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Cited articles

Abu Bakar, M. A., Mohd Ariff, N., Abu Bakar, S., Goh, P. C., and Rajendran, R.: Peramalan Kualiti Udara menggunakan Kaedah Pembelajaran Mendalam Rangkaian Perlingkaran Temporal (TCN), Sains Malays., 51, 3785–3793, https://doi.org/10.17576/jsm-2022-5111-22, 2021. 
Akhoondzadeh, M., De Santis, A., Marchetti, D., and Shen, X.: Swarm-TEC Satellite Measurements as a Potential Earthquake Precursor Together With Other Swarm and CSES Data: The Case of Mw 7.6 2019 Papua New Guinea Seismic Event, Front. Earth Sci., 10, 820189, https://doi.org/10.3389/feart.2022.820189, 2022. 
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Bilal, M. A., Ji, Y., Wang, Y., Akhter, M. P., and Yaqub, M.: Early Earthquake Detection Using Batch Normalization Graph Convolutional Neural Network (BNGCNN), Appl. Sci.-Basel, 12, 7548, https://doi.org/10.3390/app12157548, 2022. 
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Short summary
In this study, we advance the field of earthquake prediction by introducing a pre-seismic anomaly extraction method based on the structure of a graph WaveNet model, which reveals the temporal correlation and spatial correlation of the strain observation data from different boreholes prior to the occurrence of an earthquake event.
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