# Research on 3D Geological Modeling Method Based on Deep Neural Networks for Drilling Data
## 1 Introduction  
The ongoing urbanization in China has brought forth challenges including population concentration, environmental pollution, and a scarcity of surface resources, impacting the quality of life for urban residents. Confronted with limited surface land resources, individuals have increasingly turned their attention below the city's surface, investigating the feasibility of utilizing underground spaces and, at times, contemplating the establishment of subterranean cities. The development of underground spaces can satisfy the urgent demand for additional urban development areas and efficiently circumvent challenges such as compensation and relocation, which are frequently encountered in above-ground construction projects. Nevertheless, the construction of underground engineering projects presents its own unique set of challenges, encompassing geological conditions, structural load-bearing capacity, corrosion prevention, and waterproofing. Geological factors, in particular, act as the fundamental determinants of the suitability of underground spaces for construction, essentially forming the foundation for urban and underground development. Following the inception of 3D geological modeling, this interdisciplinary technology, integrating geology, computer science, statistics, and geographic information technology, has gained extensive traction across diverse geoscience domains. Traditional 3D modeling methods predominantly depend on visually representing geological structures through the creation of mesh models and the manual drawing of profiles to construct geological surfaces across multiple cross-sections. This approach is time-consuming, difficult to update, and heavily reliant on the modeler's expertise, leading to variable model quality. An alternative to explicit 3D modeling involves downscaling 3D data without considering the spatial structure of the geological body. However, this method can introduce additional errorsduring the application of geological profiles and data processing [1; 2]. [PERSON] and colleagues [3] introduced the 3D inclined domain method for creating fully 3D surfaces, which substantially improves model quality through the automated extraction of azimuthal data from geological contacts, the construction of inclined domains, and the automatic generation of geological levels [4]. In 2005, [PERSON] and collaborators [5] presented a method for constructing 3D geological models utilizing the Earth's surface data, freely accessible satellite imagery, and DEM data. This approach is especially valuable for modeling straightforward sites. While it enables a rapid and precise quantitative description of geometric contact surfaces between strata and faults using drilling data, it lacks accuracy in modeling stratigraphic heterogeneity in the absence of drilling data. Consequently, this method is primarily suited for applications like cost-benefit analysis, geomechanics of geotechnical bodies, and environmental impact studies [6]. In situations where drilling data or structural information is inadequate, spatial interpolation is often employed to supplement the data and construct the model. [PERSON] and colleagues [7] utilized the potential field synergistic kriging method, which integrates planar geological maps, DTM, structured data, drillings, and geological interpretations. This approach was applied for three-dimensional geological modeling and the evaluation of model uncertainty. In practical applications, actual geological conditions are frequently unknown, rendering uncertainty an intrinsic attribute of geological models. Indeed, some scholars contend that assessing uncertainty is as crucial as the model itself [8; 9; 10]. To expedite the construction of a precise and easily updated 3D geological model while minimizing model uncertainty, this paper introduces a novel 3D modeling approach rooted in deep learning. This method draws upon a comprehensive integration of knowledge and techniques from various fields, including geology, geography, and computer science.