{"title":"Multiple noise reduction for distributed acoustic sensing data processing through densely connected residual convolutional networks","authors":"","doi":"10.1016/j.jappgeo.2024.105464","DOIUrl":null,"url":null,"abstract":"<div><p>Distributed acoustic sensor (DAS), which utilizes the entire optical fiber as the sensing medium, provides distinct advantages of high resolution, dynamic monitoring, and resistance to high temperatures. This technology finds diverse applications in the seismic exploration, oil survey, and submarine cable monitoring industries. However, DAS signals are susceptible to various kinds of noise, such as horizontal noise, erratic noise, random noise, and so on, which significantly degrade the SNR. This low SNR is likely to affect some subsequent analyses, such as inversion and interpretation. The mixed noises feature of the DAS data poses a serious challenge for SNR enhancement. To address this issue, we develop a supervised learning-based densely connected residual convolutional denoising network (DCRCDNet), which leverages both encoding and decoding processes to extract features and reconstruct DAS data. The design of dense connectivity and residual blocks allow the network to extract both shallow and deep features. The network is trained using both synthetic and field data to obtain the optimal network parameters. Testing on synthetic data demonstrates that DCRCDNet improves the signal-to-noise ratio (SNR) from −10.21 dB to 15.61 dB. The test results from both synthetic and field data indicate that, compared to traditional filtering methods and other deep learning approaches, this network effectively suppresses noise in DAS signals. Consequently, DCRCDNet shows great potential in reconstructing DAS signals from hidden noise, suppressing strong and mixed noise, and extracting hidden signals.</p></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Geophysics","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0926985124001800","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Distributed acoustic sensor (DAS), which utilizes the entire optical fiber as the sensing medium, provides distinct advantages of high resolution, dynamic monitoring, and resistance to high temperatures. This technology finds diverse applications in the seismic exploration, oil survey, and submarine cable monitoring industries. However, DAS signals are susceptible to various kinds of noise, such as horizontal noise, erratic noise, random noise, and so on, which significantly degrade the SNR. This low SNR is likely to affect some subsequent analyses, such as inversion and interpretation. The mixed noises feature of the DAS data poses a serious challenge for SNR enhancement. To address this issue, we develop a supervised learning-based densely connected residual convolutional denoising network (DCRCDNet), which leverages both encoding and decoding processes to extract features and reconstruct DAS data. The design of dense connectivity and residual blocks allow the network to extract both shallow and deep features. The network is trained using both synthetic and field data to obtain the optimal network parameters. Testing on synthetic data demonstrates that DCRCDNet improves the signal-to-noise ratio (SNR) from −10.21 dB to 15.61 dB. The test results from both synthetic and field data indicate that, compared to traditional filtering methods and other deep learning approaches, this network effectively suppresses noise in DAS signals. Consequently, DCRCDNet shows great potential in reconstructing DAS signals from hidden noise, suppressing strong and mixed noise, and extracting hidden signals.
分布式声学传感器(DAS)利用整根光纤作为传感介质,具有高分辨率、动态监测和耐高温等显著优势。这项技术在地震勘探、石油调查和海底电缆监测行业有着广泛的应用。然而,DAS 信号容易受到各种噪声的影响,如水平噪声、不稳定噪声、随机噪声等,从而显著降低信噪比。这种低信噪比很可能会影响一些后续分析,如反演和解释。DAS 数据的混合噪声特征对信噪比的提升提出了严峻的挑战。为解决这一问题,我们开发了一种基于监督学习的密集连接残差卷积去噪网络(DCRCDNet),利用编码和解码过程提取特征并重建 DAS 数据。密集连接和残差块的设计使网络既能提取浅层特征,也能提取深层特征。该网络使用合成数据和现场数据进行训练,以获得最佳网络参数。对合成数据的测试表明,DCRCDNet 可将信噪比 (SNR) 从 -10.21 dB 提高到 15.61 dB。合成数据和现场数据的测试结果表明,与传统过滤方法和其他深度学习方法相比,该网络能有效抑制 DAS 信号中的噪声。因此,DCRCDNet 在从隐藏噪声中重建 DAS 信号、抑制强噪声和混合噪声以及提取隐藏信号方面显示出巨大的潜力。
期刊介绍:
The Journal of Applied Geophysics with its key objective of responding to pertinent and timely needs, places particular emphasis on methodological developments and innovative applications of geophysical techniques for addressing environmental, engineering, and hydrological problems. Related topical research in exploration geophysics and in soil and rock physics is also covered by the Journal of Applied Geophysics.