Deep learning-based stochastic modelling and uncertainty analysis of fault networks

Limited by the survey data and current interpretation methods, the modelling processes of fault networks are fraught with uncertainties. In hydraulic geological engineering, the location uncertainty of faults plays a vital role in decision-making and engineering safety. However, traditional uncertainty modelling methods have difficulty obtaining accurate uncertainty quantification and topology representation. To this end, we proposed a novel solution for uncertainty analysis and three-dimensional modelling for faults via a deep learning approach. A spatial uncertainty perception (SUP) method is first presented based on a modified deep mixture density network (MDN), which can be used to learn the spatial distributions of fault zones, calculate the probability of fault models, and simulate stochastic models with certain confidence degrees. After that, a graph representation (GRep) method is developed to express the topological form and geological ages of fault networks. The GRep makes it possible to automatically simulate the spatial distributions of fault belts, thus providing an effective way for the uncertainty modelling and assessment of fault networks. The two methods are then performed in the geological engineering of a practical hydraulic project. The results show that this solution can conduct accurate uncertainty evaluations and visualizations on fault networks, thus providing suggestions for subsequent geological investigations.