3-D Gravity Inversion Based on Deep Convolution Neural Networks

Abstract

The distribution of physical features in the Earth’s interior could be estimated by geophysical inversion from the acquired data at or above the surface. Inverse problems are generally considered as least-squares optimization issues in high-dimensional parameter space. Existing approaches are largely based on linear inversion methods, which are limited by the initial model. Nonlinear inversion methods, despite their significant ability in uncertainty quantification, still remain a formidable computational task. In this letter, a new gravity inversion approach is developed based on convolutional neural networks (CNNs). Although the training stage of this method is time-consuming, the actual prediction can be performed in only seconds. Thus, the high computational time of geophysical inversion can be considerably decreased once an appropriate network is constructed. The tests on synthetic data demonstrate that good results could be attained by applying this method to gravity data inversion compared with the least-squares regularization inversion and fully convolutional networks (FCNs).