The Quaternary stratigraphic unit is an important underground space resource for sustainable urban development. It is of great significance to understand the spatial variation characteristics of the Quaternary stratigraphic structure and its internal attributes. However, due to the openness and complexity of the Quaternary sedimentary environment, the sedimentary characteristics of Quaternary stratigraphic units are often very complex and difficult to accurately analyze. In this study, a method for analyzing Quaternary sedimentary characteristics via 3D geological modeling based on vector and grid integration is proposed. Based on this method, the Quaternary depositional framework of Xiong’an New Area is established. The results show that the study area is mainly composed of seven Quaternary strata with different sedimentary origins, and the 3D spatial variation characteristics of lithology in each stratum are unique. Taking the vector framework model as the constraint boundary, this study constructs the lithology stochastic model of each Quaternary stratigraphic unit respectively, and accurately simulates the 3D spatial variation characteristics of the inner lithology of Quaternary stratigraphic units in the study area, which is of great significance for the urban planning, construction, and environmental protection of Xiong’an New Area.

Three-dimensional (3D) geological modeling technology offers intricate geological information services and technical support for the research of urban geological conditions and their evolutionary mechanisms. It further facilitates the analysis of environmental geological problems, encompassing their distinguishing features and genetic mechanisms. The precision of such three-dimensional models is predominantly influenced by the caliber of modeling data, the chosen modeling methodologies, and the intricacy of the modeled geological objects. This research, exemplified by the three-dimensional geological structure modeling of the Wuhan metropolitan development area, aims to explore a comprehensive three-dimensional modeling framework for urban geology, particularly in scenarios with large-scale and complex geological conditions. This study holds significant technical value in enhancing the application of 3D geological modeling, as well as practical importance for urban geological investigations and research.

The characterization of lithology within Quaternary aquifers holds significant geological importance for the protection, management, and utilization of groundwater resources, yet it continues to present considerable challenges. Indicator Kriging (IK) is a non-parametric, probability-based method of spatial interpolation. It considers the correlation and variability between data points, and its popularity stems from its alignment with geological experts’ principles. However, it still encounters issues in complex geological conditions. To address the limited capacity of conventional IK in reproducing geological variables within heterogeneous geological settings, this study develops an ordered IK method incorporating field variogram function parameters. This framework dynamically extends IK applications by integrating stratigraphic extension trends, requiring experts to formalize spatial variation trends into geological knowledge data, subsequently transformed into constraint parameters for interpolation. Estimation paths are determined via Euclidean distances between points-to-be-estimated and valid data, executing ordered IK following near-to-far and bottom-to-top principles. Results directly depict QLS formation spatial distributions or undergo expert modification for quantitative analysis, demonstrating superior integration of geological knowledge compared to empirical variogram fitting and partitioned IK estimation. The method reduces deviation from expert-interpreted spatial distributions while maintaining computational efficiency and multi-factor integration, with three case analyses confirming enhanced accuracy in lithology distribution reproduction and improved geostructural congruence in complex geological reconstruction. This approach revitalizes Kriging applications in complex geological research, synergizing domain cognition with computational efficacy to advance precision in geological characterization and support government decision-making.