Assessment of hydrological loading displacement from GNSS and GRACE data using deep learning algorithms.

ABSTRACT: This work introduces a novel method for estimating hydrological loading displacement using 3D Convolutional Neural Networks (3D-CNN). This approach utilizes vertical displacement time series data from 41 Global Navigation Satellite System (GNSS) stations across Yunnan Province, China, and its adjacent areas, coupled with spatiotemporal variations in terrestrial water storage derived from the Gravity Recovery and Climate Experiment satellites (GRACE). The 3D-CNN method demonstrates markedly higher inversion precision compared to conventional load Green's function inversion techniques. This improvement is evidenced by substantial reductions in deviations from GNSS observations across various statistical metrics: the maximum deviation decreased by 1.34 millimeters, the absolute minimum deviat

SUBMITTER: Wei C 

PROVIDER: S-EPMC11839998 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature