关键词: 岩质边坡, 环境背景噪声, 波速变化, 多物理场耦合模型

Abstract:

Ambient seismic noise interferometry has been recently used to monitor the damage evolution of landslides. The measured velocity changes of slopes at shallow depth play a crucial role in better understanding the mechanical changes of slopes under different external loadings. However, previous studies have primarily focused on the effects of key triggers such as earthquakes and rainfall, or have been limited to analyzing the evolution mechanisms before and after slope instability. In contrast, long-term continuous monitoring of typical rock slopes in southwest China remains scarce, particularly in terms of exploring the quantitative relationships between external factors such as air temperature, rainfall, and seismicity, and seismic velocity changes. This limits a better understanding of how seasonal external factor variations affect slope stability.

Therefore, in this study, we conducted a 2.5-year in situ monitoring campaign of daily seismic velocity variations on the slow-moving Pubugou rock slope in southwestern China, using continuous ambient noise data recorded by two seismic stations deployed from October 1, 2020, to February 26, 2023. To assess the impact of long-term environmental fluctuations on slope stability, collocated meteorological and rainfall sensors were installed to record air temperature and precipitation at a daily sampling rate. Furthermore, we cataloged 27 446 local earthquakes(M_L1.0-6.8) occurring within a 250-km radius of the slope during the study period. Based on these integrated observations, we systematically investigated the relationships between seasonal seismic velocity changes and external forcing factors—specifically, air temperature, rainfall, and regional seismicity. The results show that the seismic velocity changes exhibit seasonal and reversible fluctuations in the range of -1.5% to 2.0% on an annual scale which is well linearly correlated with the changes in the air temperature, while the precipitation induced seismic velocity decreases by -0.3%~-0.6% during the monsoon. We find that the M_L6.8 Luding earthquake that occurred on September 5, 2022 induced a seismic velocity decrease of ~-2.3%, followed by a 23-day recovery phase to the pre-earthquake state by removing the influence of the seasonal air temperature fluctuation using a linear regression model. While the M_L5.6 aftershock occurred on January 26, 2023 induced a velocity decrease of ~-0.4% in the temperature-corrected time series of seismic velocity changes. Furthermore, we propose a seismic velocity-based coupling model on the slope by using the multiple linear regression method to quantify the external loadings, such as the air temperature, rainfall, and seismic activities. The optimal fitting coefficients for the temperature, rainfall-induced pore pressure, and earthquake-induced seismic intensity on the slope are 1.0×10^-3, -3.59×10^-5, and -6.68×10^-4, respectively. These coefficients suggest that the seasonal seismic velocity changes of the slope are positively correlated with the temperature and negatively correlated with the pore pressure and seismic intensity, as we expected. Finally, our results highlight the strong potential of the method to assess the risk of the slope instability by proposing an empirical warning index based on the analysis of a short-term decrease in the seismic velocity changes observed through long-term monitoring.

Key words: rock slope, ambient background noise, seismic velocity changes, multi-physics coupling model