{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T17:12:54Z","timestamp":1774631574483,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T00:00:00Z","timestamp":1730937600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Mineral Exploration Cooperative Research Centre whose activities are funded by the Australian Government\u2019s Cooperative Research Centre Program","award":["2024\/36"],"award-info":[{"award-number":["2024\/36"]}]},{"name":"Mineral Exploration Cooperative Research Centre whose activities are funded by the Australian Government\u2019s Cooperative Research Centre Program","award":["GILIII000114"],"award-info":[{"award-number":["GILIII000114"]}]},{"name":"Mineral Exploration Cooperative Research Centre whose activities are funded by the Australian Government\u2019s Cooperative Research Centre Program","award":["202206450061"],"award-info":[{"award-number":["202206450061"]}]},{"name":"Department of Industry Innovation and Science through the 2021 Global Innovation Linkage","award":["2024\/36"],"award-info":[{"award-number":["2024\/36"]}]},{"name":"Department of Industry Innovation and Science through the 2021 Global Innovation Linkage","award":["GILIII000114"],"award-info":[{"award-number":["GILIII000114"]}]},{"name":"Department of Industry Innovation and Science through the 2021 Global Innovation Linkage","award":["202206450061"],"award-info":[{"award-number":["202206450061"]}]},{"name":"sponsors at the Curtin Reservoir Geophysics Consortium (CRGC)","award":["2024\/36"],"award-info":[{"award-number":["2024\/36"]}]},{"name":"sponsors at the Curtin Reservoir Geophysics Consortium (CRGC)","award":["GILIII000114"],"award-info":[{"award-number":["GILIII000114"]}]},{"name":"sponsors at the Curtin Reservoir Geophysics Consortium (CRGC)","award":["202206450061"],"award-info":[{"award-number":["202206450061"]}]},{"name":"China Scholarship Council State Scholarship Fund","award":["2024\/36"],"award-info":[{"award-number":["2024\/36"]}]},{"name":"China Scholarship Council State Scholarship Fund","award":["GILIII000114"],"award-info":[{"award-number":["GILIII000114"]}]},{"name":"China Scholarship Council State Scholarship Fund","award":["202206450061"],"award-info":[{"award-number":["202206450061"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Over the last decade, distributed acoustic sensing (DAS) has received growing attention in the field of seismic acquisition and monitoring due to its potential high spatial sampling rate, low maintenance cost and high resistance to temperature and pressure. Despite its undeniable advantages, DAS faces some challenges, including a low signal-to-noise ratio, which partly results from the instrument-specific noise generated by DAS interrogators. We present a comparison between two deep learning approaches to address DAS hardware noise and enhance the quality of DAS data. These approaches have the advantage of including real instrumental noise in the neural network training dataset. For the supervised learning (SL) approach, real DAS instrumental noise measured on an acoustically isolated coil is added to synthetic data to generate training pairs of clean\/noisy data. For the second method, the Noise2Noise (N2N) approach, the training is performed on noisy\/noisy data pairs recorded simultaneously on the downgoing and upgoing parts of a downhole fiber-optic cable. Both approaches allow for the removal of unwanted noise that lies within the same frequency band of the useful signal, a result that cannot be achieved by conventional denoising techniques employing frequency filtering.<\/jats:p>","DOI":"10.3390\/rs16224150","type":"journal-article","created":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T06:41:22Z","timestamp":1730961682000},"page":"4150","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Removing Instrumental Noise in Distributed Acoustic Sensing Data: A Comparison Between Two Deep Learning Approaches"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7341-6769","authenticated-orcid":false,"given":"Xihao","family":"Gu","sequence":"first","affiliation":[{"name":"Center for Exploration Geophysics, Curtin University, Perth, WA 6845, Australia"},{"name":"School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China"}]},{"given":"Olivia","family":"Collet","sequence":"additional","affiliation":[{"name":"Center for Exploration Geophysics, Curtin University, Perth, WA 6845, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8456-7391","authenticated-orcid":false,"given":"Konstantin","family":"Tertyshnikov","sequence":"additional","affiliation":[{"name":"Center for Exploration Geophysics, Curtin University, Perth, WA 6845, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2555-6860","authenticated-orcid":false,"given":"Roman","family":"Pevzner","sequence":"additional","affiliation":[{"name":"Center for Exploration Geophysics, Curtin University, Perth, WA 6845, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hartog, A.H. 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