3D geological structure inversion from Noddy-generated magnetic data using deep learning methods. (April 2021) - Guo, Jiateng; Li, Yunqiang; Jessell, Mark Walter; Giraud, Jeremie; Li, Chaoling; Wu, Lixin; Li, Fengdan; Liu, Shanjun
Abstract: Using geophysical inversion for three-dimensional (3D) geological modeling is an effective way to model underground geological structures. In this study, we propose and investigate a 3D geological structure inversion method using convolutional neural networks (CNNs). This method can quickly predict the parameters of a geological structure for constructing a 3D model. First, we sample the geological model space by generating millions of 3D geological models and their corresponding magnetic images. The dataset we use to train CNN classification and regression models includes faults, folds, tilts, tilt-faults and fold-faults. The classification model is used to judge the classification of geological structures. The regression model is used to predict the attitudes of geological structures. The method is applied to synthetic data and real survey data, and the results show that geological structures can be recovered effectively. The classification accuracy is approximately 100%, and the regression accuracy of different structures is mostly between 80% and 97%. Highlights: Millions of 3-D geological models and magnetic images are generated by PyNoddy. Convolutional neural networks (CNNs) are trained using synthetic magnetic images. The CNN classification model predicts the type of geological structure accurately. Geological structure parameters are predicted for fast and automatic 3-D modeling. The predictive capabilities are shown by case studies with real and synthetic data.