A linear regression framework for predicting subsurface geometries and displacement rates in deep-seated, slow-moving landslides

A new numerical integration/linear regression tool is used to investigate kinematic behavior in deep-seated (sliding surface deeper than 3 m) mass movements. The technique is developed in the context of real inclinometer records and numerical integration using nine inclinometer case histories from four well documented, large landslides: Carrot River, Montebestia, Karya village, and Pietrapertosa. Axial metric, subsurface geometric deformation rates are predicted and compared for four different case studies from the literature: a reactivated, composite, extremely slow earth slide-earthflow; a reactivated composite, extremely slow debris slide-rock fall; an active, composite, very slow earth slide-earth flow; and a reactivated complex, slow earth slide-earth spread. Sensitivity analysis, based on sliding surface depth-to-length (D/L) ratios, show that the mobility of these slow-moving masses is closely dependent on the mode of sliding. The results also show that short term and long term dynamics of slow moving landslides can be captured by geometrical patterns. Because the parameter determined is a geometric property, the technique used in the investigation can be applied in new landslide problems independent of local conditions and triggering mechanisms, within the confines of the stipulated boundary conditions. Hence with this framework also, unrelated landslide problems can be analyzed and compared, as demonstrated herein. Additionally, this approach is useful for two dimensional reconstructions of subsurface displacement and velocity profiles, and thus may act as a precursor to detailed field investigation programs, warning systems and mitigation projects at minimal costs.