Research progress and prospect of land subsidence

Abstract

Land subsidence is a worldwide geological hazard. Differential land subsidence has posed the major threat to urban infrastructure, linear rail transit and underground space development and utilization, and also restricted the sustainable development of the economy and society. This paper systematically elaborates on the research progresses on land deformation acquisition, evolution mechanism and simulation of land subsidence, and focuses on the land deformation acquisition technology based on InSAR monitoring and multi-source deformation data fusion, as well as the correlation analysis, statistical analysis, machine learning and other methods to analyze the relationship between the evolution of land subsidence and various influencing factors based on geotechnical experiment and long time series observation data. On this basis, the advantages and disadvantages of land subsidence simulation models such as groundwater flow field-land deformation model, mathematical statistical model and machine learning model are explored. It is found that multi-source deformation data fusion can improve the spatiotemporal resolution of land deformation. The differences in geological structure, lithology, groundwater exploitation, and dynamic and static loads are factors contributing to the differential evolution of land subsidence. The difficulty in balancing the computational efficiency and interpretability of mathematical models for land subsidence is the main problem in simulation. According to the literature review, the current researches mainly focus on land subsidence caused by groundwater over-exploitation. This paper further proposes the future research directions for land subsidence, under the background of climate change, new hydrological condition and dataset, and based on the fusion of data through remote sensing and field observations, integrating the latest progress of InSAR, GeoAI, cloud platform and other technologies to reveal the evolution mechanism of land subsidence considering the climate change and anthropic activities and provide technical support for regional land subsidence prevention and urban safety.

Keywords

• land subsidence
• insar
• evolutionary mechanism
• machine learning
• coupling model