Pre-earthquake anomaly extraction from borehole strain data based on machine learning

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 l...

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Bibliographic Details
Published in:Scientific reports 2023-11, Vol.13 (1), p.20095-20095, Article 20095
Main Authors: Chi, Chengquan, Li, Chenyang, Han, Ying, Yu, Zining, Li, Xiang, Zhang, Dewang
Format: Article
Language:English
Subjects:
704/2151
704/4111
Accuracy
Algorithms
Boreholes
Data processing
Decomposition
Deep learning
Deformation
Earthquake prediction
Earthquakes
Humanities and Social Sciences
Information science
Learning algorithms
Machine learning
Magnetic fields
Methods
multidisciplinary
Science
Science (multidisciplinary)
Seismic activity
Signal processing
Statistical analysis
Trends
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Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-47387-z