Mapping 3D Structure of Loose Quaternary Deposits Combining Deep Learning and Multiple-point Statistics: An example in Chencun, Northern Pearl River Delta

Abstract. Reconstruction and cognition of structures of the Quaternary deposits, like thickness variation and displacement, is necessary for understanding neotectonics and the evolution of palaeo-valleys and deltas. Multiple-point statistics (MPS) is a useful method to reconstruct three-dimensional geological models in many fields. However, non-stationary spatial patterns and semantics in geological blocks are difficult to extract and reconstruct with the MPS-based methods, especially for those probability-based MPS methods. To reconstruct 3D characteristics of loose Quaternary deposits and the semantic relationship between them, an algorithm coupled MPS and deep artificial neural network (DANN) is proposed. The DANN is constructed and used to extract and simulate the global characteristics of geological structures. Process of sequential simulation and stratigraphic sequence calibration are implemented to build an initial model. To obtain a reasonable final realization, an iterative MPS simulation process with a multi-scale strategy is implemented. With several cross-sections and trench profiles used as modeling dataset, two concrete examples of constructing the Quaternary sediments in Chencun, South China are given. The displacements of sedimentary formation belonging to the Pleistocene reveal the strata rupture caused by the fault activities. The modeling results illustrated that the DANN used in the method can extract and simulate global structures of Quaternary deposits, and MPS simulation with the Expectation-Maximization-like iteration process can optimize local characteristics in results effectively.