Three-Dimensional Gravity Interface Inversion Based on Artificial Neural Network and Discrete Cosine Transform Algorithm

This study applies artificial neural networks to three-dimensional gravity density interface inversion. Parker’s formula based on fast Fourier transform method plays an important role in gravity interface inversion. In the training process of the artificial neural network, random generated underground interface geometries are used as inputs, while the output is the gravity anomaly data calculated by Parker’s formula. A large-scale input-output training set is formed for the training process of the artificial neural network. In addition, discrete cosine transform (DFT) is introduced to compress and store matrices, which reduces computational memory, decreases computation time, and improves computational efficiency in the training and testing processes of the artificial neural network. A deep learning interface inversion algorithm based on the U-net network model is designed. On the basis of the traditional loss function, a smooth loss term and an overfitting suppression term are added to improve the smoothness and convergence efficiency of the gravity interface inversion results. Finally, the inversion prediction is verified through the test sample set to validate the generalization of the established deep learning network model. This paper analyzes the effectiveness and practicality of this method in density interface inversion through theoretical models and actual data experiments. The deep learning interface inversion method based on the improved loss function constraint effectively improves the convergence efficiency and computational stability of density interface inversion. Applying this method to synthetic data and actual measured data processing has achieved good results.