利用图形波网从钻孔应变数据中提取震前异常:中国 2013 年芦山地震案例研究

IF 3.2 2区 地球科学 Q1 GEOCHEMISTRY & GEOPHYSICS
Solid Earth Pub Date : 2024-07-22 DOI:10.5194/se-15-877-2024
Chenyang Li, Yu Duan, Ying Han, Zining Yu, Chengquan Chi, Dewang Zhang
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引用次数: 0

摘要

摘要2013 年 4 月 20 日,芦山发生了 7.0 级地震。在地震评估中,钻孔应变计因其在跟踪地壳形变方面的显著灵敏度和固有可靠性而被广泛使用。然而,传统的数据处理方法在处理海量数据集时遇到了挑战。本研究提出使用 Graph WaveNet 图神经网络来分析震中附近多个台站的钻孔应变数据,并利用芦山震中附近四个台站 2010-2013 年的数据建立了节点图结构。在排除了气压、气温和降雨的潜在影响后,我们对震前异常进行了统计分析。以距离震中最近的姑咱站、小庙站和泸州站为重点,拟合结果显示震前发生了两次异常累积加速事件。第一个加速事件发生在地震发生前约 4 个月,表明能量从薄弱断层段预先释放。相反,地震前几天观测到的加速度事件表明,强断层段达到了应变累积的不稳定状态。我们初步推断,这两个异常累积加速度可能与大地震的准备阶段有关。这项研究凸显了图神经网络在开展震前异常多站研究方面的巨大潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Extraction of pre-earthquake anomalies from borehole strain data using Graph WaveNet: a case study of the 2013 Lushan earthquake in China
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.
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来源期刊
Solid Earth
Solid Earth GEOCHEMISTRY & GEOPHYSICS-
CiteScore
6.90
自引率
8.80%
发文量
78
审稿时长
4.5 months
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.
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