Reply on RC1

Abstract

Landslides present a significant hazard for humans, but continuous landslide monitoring is not yet possible due to their unpredictability. Post-event reconstruction based on field survey and remote sensing cannot provide full insight into the landslide movement process. Analysis and inversion of the seismic signals generated by landside movement has started to provide valuable data for understanding the entire process of landslide movement, from initiation to cessation, along with numerical simulation, but each method has shortcomings. Simple seismic signal analysis can detect landslide occurrence, but the propagation effect generates lags. Dynamic inversion based on long-period seismic signals gives the low-frequency curve of landslide dynamic parameters, but not the high-frequency characteristics. Numerical simulation can simulate the entire movement process, but results are strongly influenced by choice of model parameters. Developing a method for combining the three techniques has become a focus for research in recent years. Here, we develop such a protocol based on analysis of the 2018 Baige landslide (China). Seismic signal dynamic inversion results are used to verify the numerical simulation, and then the numerical simulation is dynamically constrained and optimized to obtain the best numerical value. We apply the procedure to the Baige event and, combined with field/geological survey, show it provides a comprehensive and accurate method for dynamic process reconstruction. We found that the Baige landslide was triggered by detachment of the weathered layer, with severe top fault segmentation. The landslide process comprised four stages: initiation, main slip, blocking, and deposition. Multi-method mutual verification effectively reduces the inherent ambiguity of each method, and multi-method joint analysis improves the rationality and reliability of the results. The approach outlined in this study could be used to support hazard prevention and control in sensitive areas.