A Stable Method for Estimating the Derivatives of Potential Field Data Based on Deep Learning

Abstract

The estimation of the derivatives is an important part of potential field data processing and interpretation. In literature, a lot of methods have been presented to estimate the derivatives accurately and stably. However, existing methods still have some limitations. For example, the derivative estimation of high-noise data is unstable, and the determination of some parameters is difficult. To solve the problems of the classical methods mentioned above, a stable method for estimating the derivatives of potential field data based on deep learning is proposed. The proposed method constructs the network based on U-Net and builds a nonlinear mapping relationship between the noisy data and the derivatives of potential field data. After training with the designed datasets, the proposed network achieved the ability to eliminate the influence of noise and intelligently estimate the derivatives of potential field data. The proposed method is tested on synthetic data and real data in the Goiás Alkaline Province, Brazil, taking estimating the vertical derivatives of gravity anomaly as examples. The results indicate that the proposed method generates stable and accurate derivatives with the noisy data.