Abstract: Given the demands of highly concealed and complex construction and operation, digital intelligent management and control of engineering projects has become an inevitable trend. To accurately characterize the internal spatial distribution of a three-dimensional (3D) geological model, detect its complex structures and material storage rules, and realize visualization interpolation of advance geological information, an ordinary Kriging (OK) interpolation method based on particle swarm optimization (PSO) and support vector regression (SVR) is proposed using a semiuniform grid 3D geological modeling method. First, stratigraphic division of the 3D geological modeling area is performed based on borehole data, lithological descriptions, depth information, and sampling locations. Then, based on sparse borehole data at the geological level, the OK interpolation method is adopted to achieve optimal, unbiased estimation of spatial attributes. Concurrently, an SVR model is used to correct elevation residuals with parameter optimization realized through PSO. Finally, a 3D geological modeling algorithm is employed to construct a 3D complex geological body model in the advance geological interpretation area. This approach effectively characterizes special geological phenomena, such as stratum interbedding and pink-out, while satisfying 3D mapping accuracy requirements for internal attribute information following mechanical parameter interpretation of advance geological information. It lays a foundation for reconstructing 3D geological and tunnel surface models at the engineering regional scale using multisource information and geological constraints and provides visual models for multiregion parallel-solving algorithms tailored to tunnel construction.

Key words: metro, three-dimensional geological modeling, Kriging interpolation, support vector regression, special geology, particle swarm optimization algorithm, Blender visualization platform