DAS-N2N: machine learning distributed acoustic sensing (DAS) signal denoising without clean data

Journal article

This paper presents a weakly supervised machine learning method, which we call DAS-N2N, for suppressing strong random noise in distributed acoustic sensing (DAS) recordings. DAS-N2N requires no manually produced labels (i.e. pre-determined examples of clean event signals or sections of noise) for training and aims to map random noise processes to a chosen summary statistic, such as the distribution mean, median or mode, whilst retaining the true underlying signal. This is achieved by splicing (joining together) two fibres hosted within a single optical cable, recording two noisy copies of the same underlying signal corrupted by different independent realizations of random observational noise. A deep learning model can then be trained using only these two noisy copies of the data to produce a near fully denoised copy. Once the model is trained, only noisy data from a single fibre is required. Using a data set from a DAS array deployed on the surface of the Rutford Ice Stream in Antarctica, we demonstrate that DAS-N2N greatly suppresses incoherent noise and enhances the signal-to-noise ratios (SNR) of natural microseismic icequake events. We further show that this approach is inherently more efficient and effective than standard stop/pass band and white noise (e.g. Wiener) filtering routines, as well as a comparable self-supervised learning method based on masking individual DAS channels. Our preferred model for this task is lightweight, processing 30 s of data recorded at a sampling frequency of 1000 Hz over 985 channels (approximately 1 km of fibre) in <1 s. Due to the high noise levels in DAS recordings, efficient data-driven denoising methods, such as DAS-N2N, will prove essential to time-critical DAS earthquake detection, particularly in the case of microseismic monitoring.

Authors: Lapins, S; Butcher, A; Kendall, J-M; Hudson, TS; Stork, AL

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Oxford University Press
• Journal: Geophysical Journal International
• Volume: 236
• Issue: 2
• Pages: 1026-1041
• Publication date: 2023-11-28
• Acceptance date: 2023-11-15
• DOI: 10.1093/gji/ggad460
• EISSN: 1365-246X
• ISSN: 0956-540X
• Language: English
• Keywords: FFR