Monitoring performance of slopes via ambient seismic noise recordings

Long-term monitoring of slopes is of significance for engineering geology research and geo-disaster prevention. There is a growing need to develop fast, nondestructive and affordable techniques that can detect gradual and cyclic changes inside slopes. For this purpose, we propose a fast and low-cost computational framework based on processing the single-point ambient seismic noise recordings by the horizontal-to-vertical spectral ratio (HVSR) method. To test the efficiency of the proposed framework, we conduct a demonstration study in a road-cut slope in colluvium deposits in Southwest China. First, we carry out short-term ambient seismic noise surveys on the slope, delineate the shear wave velocity (Vs) structure of the slope, verify and establish this structure as the reference Vs model for the slope. Then we conduct the long-term monitoring of the ambient seismic noise on two profiles of the slope, calculate the HVSR curves and observe the time-dependent variations of the predominant peaks that reflect temporal changes of subsurface interfaces. Finally, we perform the Vs inversion to investigate changes in the Vs structure with rainfall. Through the monitoring, we identify the rainfall-induced slope failure and discover that both predominant frequency and shallow subsurface Vs of slope are negatively correlated with rainfall. The HVSR calculation, the predominant peak identification and the Vs inversion can all be implemented in minutes, which is much faster than the array-based surface wave method. The theoretical analysis and the demonstration application show that the framework we proposed in this study has great potential for monitoring changes in performance of slopes.