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Monitoring evolution process of subsurface geo-interfaces in active slow-moving landslides can help understand landslide thermo-hydro-mechanical dynamics and predict potential landslide hazards. However, characterizing the behavior of these geo-interfaces and revealing their interactions remain challenging due to the general lack of high-resolution subsurface observations. To this end, we propose a novel fiber optic nerve sensing (FONS) system based on ultra-weak fiber Bragg grating (UWFBG) to sense the temperature, moisture and strain of geomaterials along a borehole in nearly real-time. The system is able to accurately locate and identify multiple potential slip surfaces and other critical geo-interface behaviors that may be relevant to landslide instability. The measurements confirm the foremost contribution of short-duration high-intensity extreme rainfall to accelerating landslide movement. We also attempted to employ machine learning algorithms based on classification principles to predict what hydrometeorological regimes would drive an accelerated deformation event. These subsurface data will allow us to investigate the multi-physical characteristics of geo-interfaces from daily to annual and even multi-annual scales and link cyclic thermo-hydro-mechanical external conditions to progressive failure. This work highlights the increasing impact of extreme weather events on landslide geohazards and the importance of multidisciplinary approaches for accurate prediction and early warning. Integrating FONS with remote sensing and ground-based technologies can create a comprehensive space-sky-ground-subsurface monitoring framework for landslides.
河野 et al. (Mon,) studied this question.