Research on the Extraction of Pre-Seismic Anomalies in Borehole Strain Data of the Mado Earthquake Based on the SVMD-Informer Model

Abstract. Earthquake is a major natural disaster triggered by the accumulation and release of crustal stress, and the accurate extraction of pre-seismic anomaly signals is crucial to improve the earthquake prediction capability. In this study, an anomaly detection method for borehole strain data based on the combination of Segmented Variational Modal Decomposition (SVMD) and Informer network is proposed, and a pre-seismic anomaly extraction study is carried out for the 2021 Mado Ms7.4 earthquake in Qinghai. The SVMD method effectively solves the memory limitation problem of traditional Variational Modal Decomposition (VMD) when dealing with large-scale data through the sliding-window mechanism, and at the same time maintains the correlation between the data. The Informer network significantly reduces the computational complexity of the long-series prediction and realizes the high-precision one-time long-time series prediction by utilizing its ProbSparse self-attention mechanism and self-attention distillation. By analyzing the borehole strain data from the Mengyuan station, this study identifies the accelerated anomaly accumulation phenomenon in the two stages before the Mado earthquake: in the first stage, the number of anomalous days shows an accelerated growth starting from about three months before the earthquake (February 13, 2021); in the second stage, the anomalous accumulation tendency is further intensified since the second month before the earthquake (the end of March, 2021), and the accumulation curve shows a typical S-shape growth characteristic. The results are highly consistent with the time windows of the index of microwave radiation anomaly (IMRA), outward long-wave radiation (OLR) and geoelectric field anomalies, and with the subsurface-to-atmosphere multilayer anomalies (e.g., Benionff strain, CO concentration, electron concentration anomalies, etc.), which indicate that the borehole strain anomalies are closely related to the gestation process of the Mado earthquake. This study provides a new method for the extraction of pre-seismic anomalies based on machine learning, and provides an important basis for understanding earthquake precursors.