Abstract. Landslides present a significant hazard for humans, but continuous landslide monitoring is not yet possible due to their unpredictability. In recent years, numerical simulation and seismic inversion methods have been used to provide valuable data for understanding the entire process of landslide movement. However, each method has shortcomings. Dynamic inversion based on long-period seismic signals gives the force–time history of a landslide using an empirical Green's function but lacks detailed flowing characteristics for the hazards. Numerical simulation can simulate the entire movement process, but results are strongly influenced by the choice of modeling parameters. Therefore, developing a method for combining those two techniques has become a focus for research in recent years. In this study, we develop such a protocol based on analysis of the 2018 Baige landslide in China. Seismic signal inversion results are used to constrain and optimize the numerical simulation. We apply the procedure to the Baige event and, combined with a 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 drawbacks of each method, and multi-method joint analysis improves the rationality and reliability of the results. The approach outlined in this study could help us to better understand the landslide dynamic process.

Pipelines are important methods of oil and gas transportation and are fundamental to many country’s economies. Pipeline safety is a critical issue; over 96% of pipeline accidents due to ground movement are caused by slope hazards and these can lead to serious personnel and property losses. Therefore, effective pipeline slope hazard monitoring and early warning is crucial, but there are many limitations to existing measures. The recent advance in remote sensing technologies enables the collection of slope hazards information that maps the spatial distribution of landslide. But this approach cannot provide real-time monitoring and early warning as there is a time lag due to image processing. Also, pipelines are considered separately from the slope hazard, with only slope event occurrence assessed rather than quantification of the impact of the hazard on the pipeline. Here, we report on a whole process risk management system for the pipeline slope hazard, incorporating monitoring and early warning of pipeline slope hazards. Three sites at risk of slope hazard on the Guangdong Dapeng Liquefied Natural Gas (LNG) Company pipeline in Guandong, South China - Zhangmutou, Huoshaogang and Dapeng New District - were selected for research and implementation of the whole process risk management, monitoring and early warning system. The system is shown to operate well and, overall, we found that the three sites are relatively stable at present. This research provides widely applicable guidance for the prevention, control, and early warning of pipeline slope hazards.

China’s economic development is closely related to oil and gas resources, and the country is investing heavily in pipeline construction. Slope geological hazards seriously affect the long-term safe operation of buried pipelines, usually causing pipeline leakage, property and environmental losses, and adverse social impacts. To ensure the safety of pipelines and reduce the probability of pipeline disasters, it is necessary to predict and quantitatively evaluate slope hazards. While there has been much research focus in recent years on the evaluation of pipeline slope disasters and the stress calculation of pipelines under hazards, existing methods only provide information on the occurrence probability of slope events, not whether a slope disaster will lead to pipeline damage. Taking the 2015 Xinzhan landslide in Guizhou Province, China, as an example, this study used discrete elements to simulate landslide events and determine the risk level and scope for pipeline damage, and then established a pipe-soil coupling model to quantitatively evaluate the impact of landslide hazards for pipelines in medium- and high-risk areas. The results provide a reference for future pipeline disaster prevention and control.

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.