Identification and Extraction of Seasonal Geodetic Signals Due to Surface Load Variations

Abstract

Deformation of the Earth's surface associated with redistributions of continental water mass explains, to first order, the seasonal signals observed in geodetic position time series. Discriminating these seasonal signals from other sources of deformation in geodetic measurements is essential to isolate tectonic signals and to monitor spatio-temporal variations in continental water storage. We propose a new methodology to identify and extract these seasonal signals. The approach uses a variational Bayesian Independent Component Analysis (vbICA) to extract the seasonal signals and a gravity-based deformation model to identify which of these signals are caused by surface loading. We test the procedure on two study areas, the Arabian Peninsula and the Nepal Himalaya, and find that the technique successfully extracts the seasonal signals with one or two independent components, depending on whether the load is stationary or moving. The approach is robust to spatial heterogeneities inherent to geodetic measurements and can help extract systematic errors in geodetic products (e.g., draconitic errors). We also discuss how to handle the degree-1 deformation field present in the geodetic data set but not captured by the gravity-based model.