Abstract

The safe disposal of spent fuel and high-level radioactive waste has become a critical bottleneck restricting the sustainable development of nuclear energy, and 3D geological modeling serves as a core technology for repository siting and safety assessment. Taking the upper member of the Lower Cretaceous Bayingobi Formation in the Tamusu area as the research object, this study focuses on sedimentary facies identification, lithofacies prediction, 3D geological modeling, and candidate site optimization. A convolutional neural network (CNN) + attention algorithm is proposed for high-precision lithofacies identification, and a Geo-CVAE-GAN model is constructed to address data sparsity and reconstruct 3D geological models. Following the workflow of single-well fine analysis, multi-method fusion prediction, and 3D geological modeling, the Sequential Indicator Simulation (SIS) algorithm is improved to build a 3D lithofacies model, and four-property parameter modeling is completed under facies control. Optimal sites are delineated via 3D spatial superimposition based on parameter thresholds. The results show that favorable mudstone layers display a dual-layer structure: stable thick layers in deep strata and thin superimposed layers in shallow strata. A preliminary total area of approximately 165 km2 is identified in Preselected Sections I and II, with target intervals at a 400–800 m depth, mud content exceeding 75%, and excellent physical properties, including low porosity, low permeability, and low water saturation. This study reveals the spatial distribution of favorable mudstone in the Tamusu area, and the preferred zones fully meet the siting criteria for high-level radioactive waste repositories, providing a reliable geological basis and technical support for subsequent exploration and engineering design.

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