Fast Forward Modeling of 3-D Gravity Data for Curved Hexahedral Grid Based on Neural Network

Unstructured grids are widely used in the processing and interpretation of geophysical data with terrain due to their excellent ability to simulate shape. Among them, the efficiency of the curved hexahedral grid in its gravity forward modeling based on the isoparametric finite-element method is poor due to the complex transformations involving numerous morphological nodes, which limits its application to large-scale data. For this reason, combined with the deep learning technology, this letter proposes a fast forward method of 3-D gravity data for the curved hexahedral grid based on the backpropagation (BP) neural network. In the training phase, the method learns the complex mapping of curved hexahedral elements to their gravity sensitivities through the neural network, thereby achieving fast forward modeling during the prediction phase. Numerical examples show that the new method has good simulation accuracy and generalization ability. Under the premise that the training phase can be completed upfront with its cost excluded, its forward efficiency is tens of times higher than that of the isoparametric finite-element method. The successful application of the new method in the actual terrain model of Mount Taishan area in China further proves its practicality.