Abstract. With the rapid growth of data volume and the development of artificial intelligence technology, deep-learning methods are a new way to model land subsidence. We utilized a long short-term memory (LSTM) model, a deep-learning-based time-series processing method to model the land subsidence under multiple influencing factors. Land subsidence has non-linear and time dependency characteristics, which the LSTM model takes into account. This paper modelled the time variation in land subsidence for 38 months from 2011 to 2015. The input variables included the change in land subsidence detected by InSAR technology, the change in confined groundwater level, the thickness of the compressible layer and the permeability coefficient. The results show that the LSTM model performed well in areas where the subsidence is slight but poorly in places with severe subsidence.

In order to promote the transformation and development of geological work, realize the efficient collection of geological survey data and improve the management and service capabilities, it is necessary to carry out the standardization of geological data. The purpose and significance of the standard construction are expounded, the construction framework of the geological data standardization system is proposed on the basis of analyzing the current situation of the standard construction at domestic and abroad and the demand for the construction of the geological survey standard. The relationship between the geological survey data standard and the informatization standard system is expounded, the overall structure of data standardization is analyzed, the overall structure of the data standardization system with the data model as the core is formed, and the main operation process of the system is described. Problems that need to be further solved are put forward from the aspects of docking the development needs of geological survey business and the consistency of old and new standards.

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Land subsidence is a serious geo-hazard in Beijing Plain, which has threatened the safety of the operation of the metropolis. This study focuses on the land subsidence in the Chaobai River alluvial fan, where is the main groundwater supply region. The vertical and the East-West deformation from June 2015 to December 2017 was derived based on the SAR imaging geometry deduction. Then, the spatial variation characteristics of the deformation were analysed and the relations with the impact factors were carried out. Results show that the nugget effect (i.e., random to total spatial variance ratio) values of the vertical and the East-West deformation at regional scale were 13 % and 49 %, respectively. This indicates that the distribution of the vertical deformation is dominated by regional influencing factors, while both regional and local-scale impact factors are important for the distribution of the East-West deformation. In the southern part of the study area, the extraction of groundwater is the dominant factor affecting the spatial distribution of the vertical displacement, while the dominant factor of East-West deformation is not obvious. This study can enrich the understanding of land subsidence distribution and will help us better understand the causes of land subsidence.

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