Extraction of pre-earthquake anomalies from borehole strain data using Graph WaveNet: a case study of the 2013 Lushan earthquake in China

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.