@article {lapins_-n2n_2024,
	title = {DAS-N2N: machine learning distributed acoustic sensing (DAS) signal denoising without clean data},
	journal = {Geophysical Journal International},
	volume = {236},
	number = {2},
	year = {2024},
	month = {feb},
	pages = {1026{\textendash}1041},
	abstract = {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 \&lt;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.},
	issn = {0956-540X},
	doi = {10.1093/gji/ggad460},
	url = {https://doi.org/10.1093/gji/ggad460},
	author = {Lapins, S and Butcher, A and Kendall, J-M and Hudson, T S and Stork, A L and Werner, M J and Gunning, J and Brisbourne, A M}
}
