Protecting the weak signals in distributed acoustic sensing data processing using local orthogonalization: The FORGE data example

被引:0
|
作者
Oboue, Yapo Abole Serge Innocent [1 ]
Chen, Yunfeng [1 ]
Fomel, Sergey [2 ]
Chen, Yangkang [2 ]
机构
[1] Zhejiang Univ, Key Lab Geosci Big Data & Deep Resource Zhejiang P, Hangzhou, Peoples R China
[2] Univ Texas Austin, Bur Econ Geol, Univ Stn, Austin, TX USA
基金
中国国家自然科学基金;
关键词
31;
D O I
10.1190/GEO2022-0676.1
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
The development of the distributed acoustic sensing (DAS) technique enables us to record seismic data at a significantly improved spatial sampling rate at meter scales, which offers new opportunities for high -resolution subsurface imaging. However, DAS recordings are often characterized by a low signal-to-noise ratio (S/N) due to the presence of data noise, significantly degrading the reliability of imaging and interpretation. Current DAS data noise reduction methods remain insufficient in simultaneously preserving weak signals and eliminating various types of noise. Particularly when dealing with DAS data that are contaminated by four types of noise (i.e., high -frequency noise, high -amplitude erratic noise, horizontal noise, and random background noise), it becomes challenging to attenuate the strong noise while maintaining fine -scale features. To address these issues, we develop an integrated local orthogonalization (LO) method that can remove a mixture of different types of noise while protecting the useful signal. Our LO method effectively eliminates the aforementioned noise by concatenating multiple denoising operators including a band-pass filter, a structure -oriented, spatially varying median filter, a dip filter in the frequency-wavenumber domain, and a curvelet filter. Next, the local orthogonalization weighting operator is applied to extract signal energy from the removed noise section. We demonstrate the robustness of our LO method on various challenging DAS data sets from the Frontier Observatory for Research in Geothermal Energy geothermal field. The denoising results demonstrate that our LO method can successfully minimize the levels of different types of noise while preserving the energy of weak signals.
引用
收藏
页码:V103 / V118
页数:16
相关论文
共 50 条
  • [41] Plane-wave Full Waveform Inversion Using Distributed Acoustic Sensing Data in an Elastic Medium
    Jeong, Seoje
    Chung, Wookeen
    Shin, Sungryul
    Kim, Sumin
    GEOPHYSICS AND GEOPHYSICAL EXPLORATION, 2022, 25 (04): : 214 - 226
  • [42] Identification of Bird Species in Large Multi-channel Data Streams Using Distributed Acoustic Sensing
    Jensen, Andrew L.
    Redford, William A.
    Shergill, Nimran P.
    Beardslee, Luke B.
    Donahue, Carly M.
    DATA SCIENCE IN ENGINEERING, VOL. 10, IMAC 2024, 2025, : 97 - 107
  • [43] De-noising distributed acoustic sensing data using an adaptive frequency-wavenumber filter
    Isken, Marius Paul
    Vasyura-Bathke, Hannes
    Dahm, Torsten
    Heimann, Sebastian
    GEOPHYSICAL JOURNAL INTERNATIONAL, 2022, 231 (02) : 944 - 949
  • [44] Reliable Earthquake Source Parameters Using Distributed Acoustic Sensing Data Derived from Coda Envelopes
    Gok, Rengin
    Walter, William R.
    Barno, Justin
    Downie, Carlos
    Mellors, Robert J.
    Mayeda, Kevin
    Roman-Nieves, Jorge
    Templeton, Dennise
    Ajo-Franklin, Jonathan
    SEISMOLOGICAL RESEARCH LETTERS, 2024, 95 (04) : 2208 - 2220
  • [45] Protecting Activity Sensing Data Privacy Using Hierarchical Information Dissociation
    Wang, Guangjing
    Guo, Hanqing
    Wang, Yuanda
    Chen, Bocheng
    Zhou, Ce
    Yan, Qiben
    2024 IEEE CONFERENCE ON COMMUNICATIONS AND NETWORK SECURITY, CNS 2024, 2024,
  • [46] Data processing and augmentation of acoustic array signals for fault detection with machine learning
    Janssen, L. A. L.
    Arteaga, I. Lopez
    JOURNAL OF SOUND AND VIBRATION, 2020, 483
  • [47] Comparison of geophone and surface-deployed distributed acoustic sensing seismic data
    Spikes, Kyle T.
    Tisato, Nicola
    Hess, Thomas E.
    Holt, John W.
    GEOPHYSICS, 2019, 84 (02) : A25 - A29
  • [48] Real-Time Train Tracking from Distributed Acoustic Sensing Data
    Wiesmeyr, Christoph
    Litzenberger, Martin
    Waser, Markus
    Papp, Adam
    Garn, Heinrich
    Neunteufel, Gunther
    Doeller, Herbert
    APPLIED SCIENCES-BASEL, 2020, 10 (02):
  • [49] Characteristics of microseismic data recorded by distributed acoustic sensing systems in anisotropic media
    Baird, A. F.
    Stork, A. L.
    Horne, S. A.
    Naldrett, G.
    Kendall, J-M
    Wookey, J.
    Verdon, J. P.
    Clarke, A.
    GEOPHYSICS, 2020, 85 (04) : KS139 - KS147
  • [50] Application of machine learning to microseismic event detection in distributed acoustic sensing data
    Stork, Anna L.
    Baird, Alan F.
    Horne, Steve A.
    Naldrett, Garth
    Lapins, Sacha
    Kendall, J-Michael
    Wookey, James
    Verdon, James P.
    Clarke, Andy
    Williams, Anna
    GEOPHYSICS, 2020, 85 (05) : KS149 - KS160