{"title":"Extraction of pre-earthquake anomalies from borehole strain data using Graph WaveNet: a case study of the 2013 Lushan earthquake in China","authors":"Chenyang Li, Yu Duan, Ying Han, Zining Yu, Chengquan Chi, Dewang Zhang","doi":"10.5194/se-15-877-2024","DOIUrl":null,"url":null,"abstract":"Abstract. On 20 April 2013, Lushan experienced an earthquake with a magnitude of 7.0. In seismic assessments, borehole strainmeters, 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 dataset-s. 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 the 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 acceleration events of anomalous accumulation that occurred before the earthquake. Occurring approximately 4 months before the earthquake event, the first acceleration event indicated the pre-release of energy from a weak fault section. Conversely, the acceleration event observed a few days before the earthquake indicated a strong fault section that reached 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 conducting multistation studies of pre-earthquake anomalies.","PeriodicalId":21912,"journal":{"name":"Solid Earth","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Solid Earth","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.5194/se-15-877-2024","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract. On 20 April 2013, Lushan experienced an earthquake with a magnitude of 7.0. In seismic assessments, borehole strainmeters, 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 dataset-s. 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 the 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 acceleration events of anomalous accumulation that occurred before the earthquake. Occurring approximately 4 months before the earthquake event, the first acceleration event indicated the pre-release of energy from a weak fault section. Conversely, the acceleration event observed a few days before the earthquake indicated a strong fault section that reached 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 conducting multistation studies of pre-earthquake anomalies.
期刊介绍:
Solid Earth (SE) is a not-for-profit journal that publishes multidisciplinary research on the composition, structure, dynamics of the Earth from the surface to the deep interior at all spatial and temporal scales. The journal invites contributions encompassing observational, experimental, and theoretical investigations in the form of short communications, research articles, method articles, review articles, and discussion and commentaries on all aspects of the solid Earth (for details see manuscript types). Being interdisciplinary in scope, SE covers the following disciplines:
geochemistry, mineralogy, petrology, volcanology;
geodesy and gravity;
geodynamics: numerical and analogue modeling of geoprocesses;
geoelectrics and electromagnetics;
geomagnetism;
geomorphology, morphotectonics, and paleoseismology;
rock physics;
seismics and seismology;
critical zone science (Earth''s permeable near-surface layer);
stratigraphy, sedimentology, and palaeontology;
rock deformation, structural geology, and tectonics.