SelfMixed: Self-supervised Mixed Noise Attenuation for Distributed Acoustic Sensing Data

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 propose 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 utilize the ℓ 2 -norm to characterize {random noise}, the ℓ 1 -norm for erratic noise, and horizontal smoothness and vertical non-smoothness for horizontal noise. Additionally, we employ 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.