Deformation Forecasting of Huangtupo Riverside Landslide in the Case of Frequent Microseisms

Ever since the impoundment of Three Gorges Reservoir（TGR）, the seismicity in head region of TGR has increased significantly. Coupled with wide fluctuation of water level each year, it becomes more important to study the deformation forecasting of landslides beside TGR. As a famous active landslide beside TGR, Huangtupo riverside landslide is selected for a case study. Based on long term water level fluctuation and seismic monitoring, three typical adverse conditions are determined. With the established 3D numerical landslide model, seepage-dynamic coupling calculation is conducted under the seismic intensity of V degree. Results are as follows： 1. the dynamic water pressure formed by water level fluctuation will intensify the deformation of landslide; 2. under seismic load, the dynamic hysteresis is significant in defective geological bodies, such as weak layer and slip zone soil, because of much higher damping ratios, the seismic accelerate would be amplified in these elements; 3. microseisms are not intense enough to cause the landslide instability suddenly, but long term deformation accumulation effect of landslide should be paid more attention; 4. in numerical simulation, the factors of unbalance force and excess pore pressure also can be used in forecasting deformation tendency of landslide.

Forecasting landslide deformation by integrating domain knowledge into interpretable deep learning considering spatiotemporal correlations

Forecasting landslide deformation is challenging due to influence of various internal and external factors on the occurrence of systemic and localized heterogeneities.Despite the potential to improve landslide predictability,deep learning has yet to be sufficiently explored for complex deformation patterns associated with landslides and is inherently opaque.Herein,we developed a holistic landslide deformation forecasting method that considers spatiotemporal correlations of landslide deformation by integrating domain knowledge into interpretable deep learning.By spatially capturing the interconnections between multiple deformations from different observation points,our method contributes to the understanding and forecasting of landslide systematic behavior.By integrating specific domain knowledge relevant to each observation point and merging internal properties with external variables,the local heterogeneity is considered in our method,identifying deformation temporal patterns in different landslide zones.Case studies involving reservoir-induced landslides and creeping landslides demonstrated that our approach(1)enhances the accuracy of landslide deformation forecasting,(2)identifies significant contributing factors and their influence on spatiotemporal deformation characteristics,and(3)demonstrates how identifying these factors and patterns facilitates landslide forecasting.Our research offers a promising and pragmatic pathway toward a deeper understanding and forecasting of complex landslide behaviors.