A Three-Dimensional Geological Structure Modeling Framework and Its Application in Machine Learning

Three-dimensional geological structure analysis is fundamental to geoscientific research. With the application of artificial intelligence in geological structure analysis, deep learning methods raise the demand for diversity in labeled structural learning sets. To improve the generalizability and flexibility of the training sets, a three-dimensional structural modeling framework is established in this paper. Firstly, the three-dimensional fold pattern is approached by the Fourier series and Gaussian equation. Secondly, to supplement the deficiency of the stochastic simulation algorithm in simulating listric faults, an ellipsoidal surface method with random perturbation is established. Thirdly, the near-field displacement of oblique-slip faults is modeled under the assumption of rotational consistency. Finally, the fault drag is defined by the magnitude and direction of near-field displacement and the drag radius. By randomly combining parameters in some predefined ranges, the proposed modeling framework can automatically construct numerous structural models with rich geological information. To validate the applicability of the proposed modeling framework, the generated models are used as learning sets to train a U-shaped fully convolutional neural network. Experiments using synthetic and field seismic data for fault interpretation show that the trained network based on the proposed modeling framework can provide better fault interpretation results compared to conventional algorithms. These results show that the proposed geological models have better generalizability and can effectively improve the applicability of machine learning.