Journal article
DAS-N2N: machine learning distributed acoustic sensing (DAS) signal denoising without clean data
- Abstract:
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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 ...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 3.9MB, Terms of use)
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- Publisher copy:
- 10.1093/gji/ggad460
Authors
Bibliographic Details
- Publisher:
- Oxford University Press
- Journal:
- Geophysical Journal International More from this journal
- Volume:
- 236
- Issue:
- 2
- Pages:
- 1026-1041
- Publication date:
- 2023-11-28
- Acceptance date:
- 2023-11-15
- DOI:
- EISSN:
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1365-246X
- ISSN:
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0956-540X
Item Description
- Language:
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English
- Keywords:
- Pubs id:
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1571232
- Local pid:
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pubs:1571232
- Deposit date:
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2023-11-27
Terms of use
- Copyright holder:
- Lapins et al.
- Copyright date:
- 2023
- Rights statement:
- © The Author(s) 2023. Published by Oxford University Press on behalf of The Royal Astronomical Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
- Licence:
- CC Attribution (CC BY)
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