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

Lapins, S.; Butcher, A.; Kendall, J.-M.; Hudson, T.S.; Stork, A.L.; Werner, M.J.; Gunning, J.; Brisbourne, A.M. ORCID: https://orcid.org/0000-0002-9887-7120. 2024 DAS-N2N: machine learning distributed acoustic sensing (DAS) signal denoising without clean data. Geophysical Journal International, 236 (2). 1026-1041. 10.1093/gji/ggad460

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Abstract/Summary

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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1093/gji/ggad460
ISSN:	0956-540X
Additional Keywords:	DAS signal processing; Antarctica; Instrumental noise; Seismic noise; Earthquake monitoring and test-ban treaty verification
Date made live:	03 Jan 2024 16:53 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/534376

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