GeoPDNN: A Semisupervised Deep Learning Neural Network Using Pseudolabels for Three-dimensional Urban Geological Modelling and Uncertainty Analysis from Borehole Data

Abstract. Boreholes are one of the main tools for high-precision urban geology exploration and large-scale geological investigations. At present, machine learning based 3D geological modelling methods for borehole data have difficulty building a finer and more complex model and analysing the modelling results with uncertainty. In this paper, a semisupervised learning algorithm using pseudolabels for 3D geological modelling from borehole data is proposed. We establish a 3D geological model using borehole data from a complex real urban local survey area in Shenyang, and the modelling results are compared with implicit surface modelling and traditional machine learning modelling methods. Finally, an uncertainty analysis of the model is made. The results show that the method effectively expands the sample space, the modelling results perform well in terms of spatial morphology and geological semantics, and the proposed modelling method can achieve good modelling results for more complex geological regions.