Removing Land Subsidence Impact from GPS Horizontal Motion in Tianjin, China

The phenomenon of land subsidence has been demonstrated to exert a considerable influence on GPS observations. However, to date, no study which has successfully removed the impact of land subsidence on GPS horizontal motion has been conducted. We developed an original sequence-to-sequence deep learning model for the elimination of the impact of land subsidence on GPS horizontal motion, employing gated recurrent units. The model is capable of predicting the horizontal motion of the target GPS station with the impact of land subsidence removed by learning the implicit relationship between the horizontal motion and vertical data of the station. A local model was constructed for each GPS station in the Tianjin subsidence area, and the corresponding dataset was generated for the purposes of model training and testing. The vertical data, with the impact of land subsidence removed, were employed as model inputs for the purpose of predicting the horizontal motion of the same station, with the impact of land subsidence similarly removed. The results demonstrate that following the removal of the impact of land subsidence, the dispersion of GPS horizontal motion within the Tianjin subsidence area is markedly diminished, and the horizontal motion trend exhibits greater consistency with that observed at neighboring stations in non-subsidence regions. The impact of land subsidence on GPS horizontal motion exhibits variability across different regions of the Tianjin subsidence area and among disparate stations.