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

ZHANG Yi; DING Renwei; ZHAO Shuo; SUN Shimin; HAN Tianjiao

doi:10.15953/j.ctta.2022.053

Volume 32 Issue 1

Jan. 2023

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CT Theory and Applications > 2023 > 32(1): 15-25. > DOI: 10.15953/j.ctta.2022.053

ZHANG Y, DING R W, ZHAO S, et al. Shallow Profile Data Denoising Method Based on Improved Cycle-consistent Generative Adversarial Network[J]. CT Theory and Applications, 2023, 32(1): 15-25. DOI: 10.15953/j.ctta.2022.053. (in Chinese).

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

College of Earth Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China

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Received Date: March 26, 2022
Revised Date: April 05, 2022
Accepted Date: April 18, 2022
Available Online: April 28, 2022
Published Date: January 30, 2023

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Abstract

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.
- artificial intelligence,
- shallow profile data,
- random noise,
- data denoising

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

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