Early identification of an impending rockslide location via a spatially-aided Gaussian mixture model

Abstract

Movement of soil and rocks in an unstable slope under gravitational forces is an example of a complex system that is highly dynamic in space and time. A typical failure in such systems is a landslide. Fundamental studies of granular media failure combined with a complex network analysis of radar monitoring data show that distinct partitions emerge in the kinematic field in the early stages of the prefailure regime, and these patterns yield clues to the ultimate location of failure. In this study we address this partitioning of constituent units in complex systems by clustering the kinematic data, specifically, with a Gaussian mixture model. In addition, we assume that neighboring units should move together. As a result, spatial information is taken into account in our model so that spatial proximity is retained. Our case study of a rockslide from high resolution radar monitoring data shows that, by incorporating spatial information, our approach is more effective in revealing the dynamics of the system and detecting the location of a potential landslide, compared to the use of only the kinematics.