Abstract. On 20 April 2013, Lushan experienced a magnitude 7.0 earthquake. In seismic assessments, borehole strain meters, 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 datasets. 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 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 accelerations of anomalous accumulation before the earthquake. Approximately four months before the earthquake event, one acceleration suggests the pre-release of energy from a weak fault section. Conversely, the acceleration a few days before the earthquake indicated a strong fault section reaching 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 con-ducting multi-station studies of pre-earthquake anomalies.

中文翻译：

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使用Graph WaveNet提取钻孔应变数据中的震前异常：以芦山地震为例

摘要。2013年4月20日，庐山发生7.0级地震。在地震评估中，钻孔应变计因其在跟踪地壳变形方面的卓越灵敏度和固有可靠性而得到广泛应用。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。本研究提出使用图波网图神经网络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的数据建立节点图结构，涵盖2010-2013年。在排除气压、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。重点关注距离震中最近的古杂站、小庙站和泸州站，拟合结果显示震前有两次异常堆积加速度。地震发生前大约四个月，一次加速度表明能量从弱断层预释放。相反，地震前几天的加速度表明强断层已达到不稳定状态，应变不断累积。我们初步推测这两次异常累积加速度可能与大地震的准备阶段有关。这项研究强调了图神经网络在对震前异常进行多站研究方面的巨大潜力。