DAS-N2N: machine learning distributed acoustic sensing (DAS) signal denoising without clean data
S Lapins,
A Butcher,
J-M Kendall
et al.
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 spl… Show more
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