Shallow Profile Data Denoising Method Based on Improved Cycle-consistent Generative Adversarial Network

Abstract

This study applied the cycle-consistent generative adversarial network method to the denoising of shallow profile data to realize intelligent denoising. This could help resolve the problem of noise and low resolution of shallow profile data. To do this, the cycle generative adversarial network with special symmetric generation countermeasure network cycle mechanism and "cycle consistency loss" was selected. We improved the performance of the network learning and training by optimizing the network structure. Next, based on the optimized shallow profile sample set training network, random noise was removed from the shallow profile data and the signal-to-noise ratio of the data was improved. The effectiveness and adaptability of this method for denoising shallow profile data were verified by trial calculations of experimental and actual data and by comparison with the traditional band-pass filtering method.

Keywords

• artificial intelligence
• shallow profile data
• random noise
• data denoising