SelfMixed: Self-supervised mixed noise attenuation for distributed acoustic sensing data

Author:

Xu Zitai1ORCID,Wu Bangyu2ORCID,Luo Yisi2ORCID,Yang Liuqing3ORCID,Chen Yangkang4ORCID

Affiliation:

1. Xi’an Jiaotong University, School of Mathematics and Statistics, Xi’an, China.

2. Xi’an Jiaotong University, School of Mathematics and Statistics, Xi’an, China. (corresponding author)

3. China University of Petroleum (Beijing), State Key Laboratory of Petroleum Resources and Prospecting, Beijing, China and China University of Petroleum (Beijing), National Engineering Laboratory of Offshore Oil Exploration, Beijing, China.

4. The University of Texas at Austin, John A. and Katherine G. Jackson School of Geosciences, Bureau of Economic Geology, Austin, Texas, USA.

Abstract

Distributed acoustic sensing (DAS) is an emerging data acquisition technique known for its high sensing density, cost effectiveness, and environmental friendliness, making it a technology with significant future application potential in many fields. However, DAS signals are often contaminated by various types of noise, such as high-frequency, high-amplitude erratic, and horizontal noise, making their processing challenging. Therefore, it is crucial to leverage the physical characteristics of these diverse types of noise in DAS data and effectively attenuate them. In this work, we develop SelfMixed, a novel self-supervised learning method for mixed noise suppression of DAS data. We fully exploit the physical characteristics of different types of noise in DAS data and introduce a physical characteristic-based training strategy. Specifically, we use the [Formula: see text] norm to characterize random noise, the [Formula: see text] norm for erratic noise, and horizontal smoothness and vertical nonsmoothness for horizontal noise. In addition, we use a blind-spot-based training strategy for DAS denoising, relying solely on observed noisy data. To more effectively attenuate horizontal noise, we also introduce a Fourier transform-based parameterization method. By combining self-supervised deep priors with the physical characteristics of mixed DAS noise, our method effectively attenuates complex mixed noise in field DAS data. Extensive experiments on synthetic and field data from various geographic scenarios validate the superiority of SelfMixed over seven state-of-the-art DAS denoising approaches.

Funder

Natural Science Basic Research Program of Shaanxi Province

National Natural Science Foundation of China

Publisher

Society of Exploration Geophysicists

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