地理空间信息2025，Vol.23Issue(3)：5-8,4.DOI:10.3969/j.issn.1672-4623.2025.03.002
利用深度学习和GNSS研究水负荷位移时空变化
Research on Spatio-temporal Change of Water Loading Displacement Using Deep Learning and GNSS
摘要
Abstract
Based on observational data from 14 global navigation satellite system(GNSS)stations in the middle and upper reaches of the Yangtze River and gravity recovery and climate experiment(GRACE)Mascon data,we put forward a water loading displacement estimation method based on a convolutional long short-term memory(LSTM)neural network.The results show that the nonlinear motion of GNSS station is obviously weakened by correcting atmospheric and non-tidal ocean loading during data preprocessing.Compared to the Green's function method,this method shows better consistency in the discrepancy between estimated water loading displacement and actual GNSS observations,with an average standard deviation reduction of 20.2%.The water loading displacement tends to be negative in summer and autumn,and positive in winter and spring.Additionally,significant annual and semi-annual periodic changes in water loading displacement are observed,along with a near one-third of a year signal.Spatially,the southwest of Hubei Province exhibits larger water loading displacement amplitudes,while the western region,especially the northwest,shows smaller amplitudes.Central regions generally have larger displacements,and the amplitude gradually decreases in the eastern areas.This study offers a new perspective for monitoring and analyzing water loading displacement in the middle and upper reaches of the Yangtze River.关键词
水负荷位移/GNSS/GRACE/卷积LSTM/格林函数Key words
water loading displacement/GNSS/GRACE/convolutional LSTM/Green's function分类
天文与地球科学引用本文复制引用
汤伟尧..利用深度学习和GNSS研究水负荷位移时空变化[J].地理空间信息,2025,23(3):5-8,4.基金项目
基于北斗地基增强的铁路运维动态基准关键技术(SKLK18-01). （SKLK18-01）
