Data- and knowledge-driven three-dimensional geological reconstruction method for tunnel engineering

The creation of a three-dimensional (3D) geological model plays a crucial guiding role in engineering. However, in practice, due to the sparsity of boreholes and the invisibility of strata, accurately reconstructing a 3D geological model has always been a challenging task. In this study, a data- and knowledge-driven 3D geological reconstruction method is proposed, where the Inverse Distance Weighting (IDW) method is integrated with computer vision techniques to improve the accuracy and reliability of geological modeling. The reconstruction of the geological model is realized by the reconstruction of continuous cross-sections in one direction. The reconstruction method integrates two deep learning models: a repair model that learns stratigraphic relationships from borehole data to reconstruct cross-sections, and an interpolation model that predicts intermediate sections by capturing stratigraphic distribution and variation patterns. The comparison with the IDW method and the ordinary kriging method on the virtual data verifies that the proposed method can capture the spatial distribution characteristics of the strata. An engineering example proves that the proposed method can be successfully applied to complex stratum modeling. The proposed method enhances and facilitates intuitive observation of both the reconstructed results and their uncertainties. The proposed method can provide guidance for underground engineering construction sites and contribute to their digital transformation.