A non-uniform spatiotemporal kriging interpolation algorithm for landslide displacement data

Abstract

The analysis of landslides using monitoring data is a commonly used method for landslide prediction and early warning. However, the loss of data due to breakdown of the monitoring equipment or interference of external factors is unavoidable in the process of monitoring landslide data. An interpolation algorithm can supplement and correct the data to solve the problem of data loss. This multi-position and long-term monitoring data is non-linear, multidimensional and time-varying, which makes it difficult for the commonly used spatiotemporal kriging interpolation methods to construct an appropriate model straightaway. This paper presents a non-uniform spatiotemporal kriging interpolation method. It breaks through the restriction of Euclidean distance in the spatial dimension while breaking away from linear relationship in the temporal dimension. The spatiotemporal deformation field model is constructed using spatiotemporal optimal weights combination and subsequently optimized by particle swarm optimization algorithm. The ordinary kriging interpolation is extended to the non-uniform spatiotemporal kriging interpolation under the spatiotemporal constraints condition. This method is successfully applied to the interpolation of the monitoring data of landslide displacement. It provides better data for studies of landslide disasters and is of great practical significance for prevention and prediction of landslide disasters.