Deformation Feature Extraction for GNSS Landslide Monitoring Series Based on Robust Adaptive Sliding-Window Algorithm

Abstract

Global navigation satellite system technology has been widely used for high-precision, real-time monitoring of landslides. To improve forecasts and early warnings, the true deformation features must be extracted from the global navigation satellite system monitoring series. However, as the deformation rate changes at different creep stages, the relationship between noise and true deformation may also change, making it difficult to accurately describe the deformation. In this study, an adaptive sliding window algorithm is proposed to account for this relationship change. First, the window was defined with an equal window width and step length, which improved the efficiency of feature extraction. Second, the median and normalized interquartile ranges were used to estimate the window samples and obtain a continuous and reliable series. Finally, the window sample breakdown point was defined to adjust the window parameter. These steps were repeated for the adjusted window to achieve adaptive processing of the monitoring series. The results based on both simulated and real landslide monitoring series demonstrated that the proposed method can provide adaptive, robust, and reliable deformation information for landslide warnings. The adaptive sliding window method also successfully assisted in the early warning of a loess landslide in Heifangtai, Gansu province, northwest of the Chinese Loess Plateau, indicating its practical application potential.