Analysis of borehole strain anomalies before the 2017 Jiuzhaigou <i>M</i><sub>s</sub> 7.0 earthquake based on a graph neural network
<p>On 8 August 2017, a strong earthquake of magnitude 7.0 occurred in Jiuzhaigou, Sichuan Province, China. To assess pre-earthquake anomalies, we utilized variational mode decomposition to preprocess borehole strain observation data and combined them with a graph WaveNet neural network model t...
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Copernicus Publications
2025-01-01
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| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | https://nhess.copernicus.org/articles/25/231/2025/nhess-25-231-2025.pdf |
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| author | C. Li C. Li C. Qin C. Qin J. Zhang J. Zhang Y. Duan Y. Duan C. Chi C. Chi |
| author_facet | C. Li C. Li C. Qin C. Qin J. Zhang J. Zhang Y. Duan Y. Duan C. Chi C. Chi |
| author_sort | C. Li |
| collection | DOAJ |
| description | <p>On 8 August 2017, a strong earthquake of magnitude 7.0 occurred in Jiuzhaigou, Sichuan Province, China. To assess pre-earthquake anomalies, we utilized variational mode decomposition to preprocess borehole strain observation data and combined them with a graph WaveNet neural network model to process data from multiple stations. We obtained 1-year data from four stations near the epicenter as the training dataset and data from 1 January to 10 August 2017 as the test dataset. For the prediction results of the variational mode decomposition–graph WaveNet model, the anomalous days were extracted using statistical methods, and the results of anomalous-day accumulation at multiple stations showed that an increase in the number of anomalous days occurred 15–32 d before the earthquake. The acceleration effect of anomalous accumulation was most obvious 20 d before the earthquake, and an increase in the number of anomalous days also occurred in the 1 to 3 d post-earthquake. We tentatively deduce that the pre-earthquake anomalies are caused by the diffusion of strain energy near the epicenter during the accumulation process, which can be used as a signal of pre-seismic anomalies, whereas the post-earthquake anomalies are caused by the frequent occurrence of aftershocks.</p> |
| format | Article |
| id | doaj-art-a00c8785612c4394a55e2c6eb7967eb5 |
| institution | Kabale University |
| issn | 1561-8633 1684-9981 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Natural Hazards and Earth System Sciences |
| spelling | doaj-art-a00c8785612c4394a55e2c6eb7967eb52025-01-14T06:56:11ZengCopernicus PublicationsNatural Hazards and Earth System Sciences1561-86331684-99812025-01-012523124510.5194/nhess-25-231-2025Analysis of borehole strain anomalies before the 2017 Jiuzhaigou <i>M</i><sub>s</sub> 7.0 earthquake based on a graph neural networkC. Li0C. Li1C. Qin2C. Qin3J. Zhang4J. Zhang5Y. Duan6Y. Duan7C. Chi8C. Chi9School of Information Science and Technology, Hainan Normal University, Haikou, 571158, ChinaKey Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of Education, Haikou, 571158, ChinaSchool of Information Science and Technology, Hainan Normal University, Haikou, 571158, ChinaKey Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of Education, Haikou, 571158, ChinaSchool of Information Science and Technology, Hainan Normal University, Haikou, 571158, ChinaKey Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of Education, Haikou, 571158, ChinaSchool of Information Science and Technology, Hainan Normal University, Haikou, 571158, ChinaKey Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of Education, Haikou, 571158, ChinaSchool of Information Science and Technology, Hainan Normal University, Haikou, 571158, ChinaKey Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of Education, Haikou, 571158, China<p>On 8 August 2017, a strong earthquake of magnitude 7.0 occurred in Jiuzhaigou, Sichuan Province, China. To assess pre-earthquake anomalies, we utilized variational mode decomposition to preprocess borehole strain observation data and combined them with a graph WaveNet neural network model to process data from multiple stations. We obtained 1-year data from four stations near the epicenter as the training dataset and data from 1 January to 10 August 2017 as the test dataset. For the prediction results of the variational mode decomposition–graph WaveNet model, the anomalous days were extracted using statistical methods, and the results of anomalous-day accumulation at multiple stations showed that an increase in the number of anomalous days occurred 15–32 d before the earthquake. The acceleration effect of anomalous accumulation was most obvious 20 d before the earthquake, and an increase in the number of anomalous days also occurred in the 1 to 3 d post-earthquake. We tentatively deduce that the pre-earthquake anomalies are caused by the diffusion of strain energy near the epicenter during the accumulation process, which can be used as a signal of pre-seismic anomalies, whereas the post-earthquake anomalies are caused by the frequent occurrence of aftershocks.</p>https://nhess.copernicus.org/articles/25/231/2025/nhess-25-231-2025.pdf |
| spellingShingle | C. Li C. Li C. Qin C. Qin J. Zhang J. Zhang Y. Duan Y. Duan C. Chi C. Chi Analysis of borehole strain anomalies before the 2017 Jiuzhaigou <i>M</i><sub>s</sub> 7.0 earthquake based on a graph neural network Natural Hazards and Earth System Sciences |
| title | Analysis of borehole strain anomalies before the 2017 Jiuzhaigou <i>M</i><sub>s</sub> 7.0 earthquake based on a graph neural network |
| title_full | Analysis of borehole strain anomalies before the 2017 Jiuzhaigou <i>M</i><sub>s</sub> 7.0 earthquake based on a graph neural network |
| title_fullStr | Analysis of borehole strain anomalies before the 2017 Jiuzhaigou <i>M</i><sub>s</sub> 7.0 earthquake based on a graph neural network |
| title_full_unstemmed | Analysis of borehole strain anomalies before the 2017 Jiuzhaigou <i>M</i><sub>s</sub> 7.0 earthquake based on a graph neural network |
| title_short | Analysis of borehole strain anomalies before the 2017 Jiuzhaigou <i>M</i><sub>s</sub> 7.0 earthquake based on a graph neural network |
| title_sort | analysis of borehole strain anomalies before the 2017 jiuzhaigou i m i sub s sub thinsp 7 0 earthquake based on a graph neural network |
| url | https://nhess.copernicus.org/articles/25/231/2025/nhess-25-231-2025.pdf |
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