Chi Chengquan, Li Chenyang, Han Ying, Yu Zining, Li Xiang, Zhang Dewang
School of Information Science and Technology, Hainan Normal University, Haikou, China.
College of Geography and Environmental Science, Hainan Normal University, Haikou, China.
Sci Rep. 2023 Nov 16;13(1):20095. doi: 10.1038/s41598-023-47387-z.
Borehole strain monitoring plays a critical role in earthquake precursor research. With the accumulation of observation data, traditional data processing methods struggle to handle the challenges of big data. This study proposes a segmented variational mode decomposition method and a GRU-LUBE deep learning network based on machine learning theory. The algorithm enhances data correlation during decomposition and effectively predicts borehole strain data changes. We extract pre-earthquake anomalies from four-component borehole strain data of the Guza station for two major earthquakes in Sichuan (Wenchuan and Lushan earthquakes), obtaining more comprehensive anomalies than previous studies. Statistical analysis reveals similar abnormal phenomena in the Guza station's borehole strain data before both earthquakes, suggesting shared crustal stress accumulation and release patterns. These findings highlight the need for further research to improve earthquake prediction and preparedness through understanding underlying mechanisms.
钻孔应变监测在地震前兆研究中起着至关重要的作用。随着观测数据的积累，传统的数据处理方法难以应对大数据带来的挑战。本研究基于机器学习理论提出了一种分段变分模态分解方法和一个GRU-LUBE深度学习网络。该算法在分解过程中增强了数据相关性，并有效地预测了钻孔应变数据的变化。我们从四川两次大地震（汶川地震和芦山地震）的姑咱台四分量钻孔应变数据中提取了震前异常，获得了比以往研究更全面的异常。统计分析表明，两次地震前姑咱台钻孔应变数据均出现了类似的异常现象，这表明地壳应力积累和释放模式具有共性。这些发现凸显了通过理解潜在机制进一步开展研究以改善地震预测和防范工作的必要性。
