High-Precision Intelligence Denoising of Potential Field Data Based on RevU-Net

Abstract

The observed potential field data are usually corrupted by noises from different sources, and these contained noises pose great adverse impacts on further data processing. In the literature, many noise reduction methods have been introduced to solve this problem, most of which are based on the idea of high-pass filter. However, there are some limitations for most of these conventional methods, such as the elimination of some effective high-frequency components caused by the shallower small-scale sources, resulting in reduced denoising precision, and the difficulties in determining the filter parameters manually. Faced with these problems, a new method based on RevU-Net architecture is first proposed to handle the above issues. The method expands the feature information through the upsampling layer at the expansion path and uses the skip-connection technology to fuse multiscale features. Also, the network updates itself toward the purpose of multiscale signal capture and separation, which provides an intelligent approach and enable it to distinguish the small-scale components and noise without human intervention. The proposed method is tested on several synthetic examples. The results illustrate that this method yields satisfactory results while preserving the features of the shallower small-scale sources without the any manually setting parameters. So, it overcomes the limitations of the conventional methods stated above.