Noise suppression of distributed acoustic sensing vertical seismic profile data based on time-frequency analysis

Shao, Dan; Li, Tonglin; Han, Liguo; Li, Yue

doi:10.1007/s11600-022-00820-9

Artykuł - szczegóły

Tytuł artykułu

Noise suppression of distributed acoustic sensing vertical seismic profile data based on time-frequency analysis

Autorzy

Shao Dan , Li Tonglin , Han Liguo , Li Yue

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-022-00820-9

Warianty tytułu

Języki publikacji

Abstrakty

Distributed acoustic sensing (DAS) technology is a novel technology applied in vertical seismic profile (VSP) exploration, which has many advantages, such as low cost, high precision, strong tolerance to harsh acquisition environment. However, the field DAS-VSP data are often disturbed by complex background noise and coupling noise with strong energy, affecting the quality of seismic data seriously. Therefore, we develop a time–frequency analysis method based on low-rank and sparse matrix decomposition (LSMD) and data position points distribution maps (DPM) to separate signals from noise. We adopt Multisynchrosqueezing Transform to construct the approximate ideal time–frequency representation of DAS data, which reduces the difficulty of signal to noise separation and avoids the loss of some effective information to a certain extent. The LSMD is performed to separate the signal component and noise component preliminarily. In addition, combined with the separated low-rank matrix and sparse matrix, we propose the DPM to improve the accuracy of signal component extraction and the recovery ability of the method for weak signals through the joint analysis of the maps in time domain and frequency domain. Both synthetic and field experiments show that the proposed method can suppress coupling noise and background noise and recover weak energy signals in DAS VSP data effectively.

Słowa kluczowe

distributed acoustic sensing DAS vertical seismic profile VSP time-frequency analysis noise suppression

rozproszone wykrywanie akustyczne DAS pionowy profil sejsmiczny VSP analiza czasowo-częstotliwościowa wyciszenie dźwięku

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2022

Tom

Vol. 70, no 4

Strony

1539--1549

Opis fizyczny

Bibliogr. 38 poz.

Twórcy

autor

Shao Dan

1204329101@qq.com

College of Geo-Exploration Science and Technology, Jilin University, Changchun 130000, China

https://orcid.org/0000-0002-7429-3569

autor

Li Tonglin

litl@jlu.edu.cn

College of Geo-Exploration Science and Technology, Jilin University, Changchun 130000, China

https://orcid.org/0000-0002-2874-8112

autor

Han Liguo

hanliguo@jlu.edu.cn

College of Geo-Exploration Science and Technology, Jilin University, Changchun 130000, China

https://orcid.org/0000-0003-4840-8091

autor

Li Yue

liyue@jlu.edu.cn

College of Communication Engineering, Jilin University, Changchun 130000, China

https://orcid.org/0000-0001-5482-6244

Bibliografia

1. Auger F, Flandrin P (1995) Improving the readability of time–frequency and time-scale representations by the reassignment method. IEEE Trans Signal Process 43(5):1068–1089. https://doi.org/10.1109/78.382394
2. Auger F, Flandrin P, Lin Y, McLaughlin S, Meignen S, Oberlin T, Wu H (2013) Time–frequency reassignment and synchrosqueezing: an overview. IEEE Signal Process Mag 30(6):32–41. https://doi.org/10.1109/MSP.2013.2265316
3. Bakku SK, Wills P, Fehler M (2014) Monitoring hydraulic fracturing using distributed acoustic sensing in a treatment well. SEG Tech Program Expand Abstr 33:5003–5008. https://doi.org/10.1190/segam2014-1280.1
4. Binder G, Titov A, Liu Y, Simmons J, Tura A, Byerley G, Monk D (2020) Modeling the seismic response of individual hydraulic fracturing stages observed in a time-lapse distributed acoustic sensing vertical seismic profiling survey. Geophysics 85(4):T225–T235. https://doi.org/10.1190/geo2019-0819.1
5. Cai Z, Cheng TH, Lu C, Subramanian KR (2001) Efficient wavelet-based image denoising algorithm. Electron Lett 37(11):683–685. https://doi.org/10.1049/el:20010466
6. Chen Y, Zhou Y, Chen W, Zu S, Huang W, Zhang D (2017) Empirical low-rank approximation for seismic noise attenuation. IEEE Trans Geosci Remote Sens 55(8):4696–4711. https://doi.org/10.1109/TGRS.2017.2698342
7. Constantinou A, Farahani A, Cuny T, Hartog A (2016) Improving DAS acquisition by real time monitoring of wireline cable coupling. SEG Tech Program Expand Abstr 35:5603–5607. https://doi.org/10.1190/segam2016-13950092.1
8. Correa J, Egorov A, Tertyshnikov K, Bona A, Pevzner R, Dean T, Freifeld B, Marshall S (2017) Analysis of signal to noise and directivity characteristics of DAS VSP at near and far offsets: a CO2CRC Otway Project data example. Lead Edge 36(12):994a1-994a7. https://doi.org/10.1190/tle36120994a1.1
9. Daley TM, Freifeld BM, Ajo-Franklin J, Dou S, Pevzner R, Shulakova V, Kashikar S, Miller DE, Goetz J, Henninges J, Lueth S (2013) Field testing of fiber-optic distributed acoustic sensing (DAS) for subsurface seismic monitoring. Lead Edge 32(6):699–706. https://doi.org/10.1190/tle32060699.1
10. Daley TM, Miller DE, Dodds K, Cook P, Freifeld BM (2016) Field testing of modular borehole monitoring with simultaneous distributed acoustic sensing and geophone vertical seismic profiles at Citronelle, Alabama: field testing of MBM. Geophys Prospect 64(5):1318–1334. https://doi.org/10.1111/1365-2478.12324
11. Daubechies I, Lu J, Wu H (2011) Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool. Appl Comput Harmon Anal 30(2):243–261. https://doi.org/10.1016/j.acha.2010.08.002
12. Dong X, Jiang H, Zheng S, Li Y, Yang B (2019) Signal-to-noise ratio enhancement for 3C downhole microseismic data based on the 3D shearlet transform and improved back-propagation neural networks. Geophysics 84(4):V245–V254. https://doi.org/10.1190/geo2018-0621.1
13. Douglas A (1997) Bandpass filtering to reduce noise on seismograms: is there a better way? Bull Seismol Soc Am 87(3):770–777. https://doi.org/10.1785/BSSA0870030770
14. Frignet BG, Hartog AH (2014) Optical vertical seismic profile on wireline cable. In: Trans SPWLA 55th annual logging symposium Abu Dhabi, pp 18–22
15. Gómez JL, Velis DR (2016) A simple method inspired by empirical mode decomposition for denoising seismic data. Geophysics 81(6):V403–V413. https://doi.org/10.1190/geo2015-0566.1
16. Goudarzi A, Riahi MA (2012) Seismic coherent and random noise attenuation using the undecimated discrete wavelet transform method with WDGA technique. J Geophys Eng 9(6):619–631. https://doi.org/10.1088/1742-2132/9/6/619
17. Hartog A, Frignet B, Mackie D, Clark M (2014) Vertical seismic optical profiling on wireline logging cable. Geophys Prospect 62(4):693–701. https://doi.org/10.1111/1365-2478.12141
18. Huang Z, Zhang J, Zhao T, Sun Y (2016) Synchrosqueezing S-transform and its application in seismic spectral decomposition. IEEE Trans Geosci Remote Sens 54(2):817–825. https://doi.org/10.1109/TGRS.2015.2466660
19. Liu W, Duan Z (2020) Seismic signal denoising using f–x variational mode decomposition. IEEE Geosci Remote Sens Lett 17(8):1313–1317. https://doi.org/10.1109/LGRS.2019.2948631
20. Liu W, Cao S, Chen Y (2016) Applications of variational mode decomposition in seismic time–frequency analysis. Geophysics 81(5):V365–V378. https://doi.org/10.1190/geo2015-0489.1
21. Ma HT, Qian ZB, Li Y, Lin HB, Shao D, Yang BJ (2019) Noise reduction for desert seismic data using spectral kurtosis adaptive bandpass filter. Acta Geophys 67(1):123–131. https://doi.org/10.1007/s11600-018-0232-0
22. Madsen KN, Thompson M, Parker T, Finfer D (2013) A VSP field trial using distributed acoustic sensing in a producing well in the north sea. First Break 31(11):51–56. https://doi.org/10.3997/1365-2397.2013027
23. Mateeva A, Lopez J, Potters H, Mestayer J, Cox B, Kiyashchenko D, Wills P, Grandi S, Hornman K, Kuvshinov B, Berlang W, Yang Z, Detomo R (2014) Distributed acoustic sensing for reservoir monitoring with vertical seismic profiling: distributed acoustic sensing (DAS) for reservoir monitoring with VSP. Geophys Prospect 62(4):679–692. https://doi.org/10.1111/1365-2478.12116
24. Meignen S, Pham D (2018) Retrieval of the modes of multicomponent signals from downsampled short-time Fourier transform. IEEE Trans Signal Process 66(23):6204–6215. https://doi.org/10.1109/TSP.2018.2875390
25. Mestayer J, Cox B, Wills P, Kiyashchenko D, Lopez J, Costello M, Bourne S, Ugueto G, Lupton R, Solano G, Hill D, Lewis A (2011) Field trials of distributed acoustic sensing for geophysical monitoring. SEG Tech Program Expand Abstr 30(1):4253–4257. https://doi.org/10.1190/1.3628095
26. Ning IC, Sava P (2018) High-resolution multi-component distributed acoustic sensing. Geophys Prospect 66(6):1111–1122. https://doi.org/10.1111/1365-2478.12634
27. Olofsson B, Martine A (2017) Validation of DAS data integrity against standard geophones: DAS field test at aquistore site. Lead Edge 36(12):981–986. https://doi.org/10.1190/tle36120981.1
28. Ouadfeul S, Aliouane L (2014) Random seismic noise attenuation data using the discrete and the continuous wavelet transforms. Arab J Geosci 7(7):2531–2537. https://doi.org/10.1007/s12517-013-1005-3
29. Parker T, Shatalin S, Farhadiroushan M (2014) Distributed acoustic sensing—a new tool for seismic applications. First Break 32(2):61–69. https://doi.org/10.3997/1365-2397.2013034
30. Pons-Llinares J, Antonino-Daviu JA, Riera-Guasp M, Lee SB, Kang T, Yang C (2015) Advanced induction motor rotor fault diagnosis via continuous and discrete time–frequency tools. IEEE Trans Ind Electron 62(3):1791–1802. https://doi.org/10.1109/TIE.2014.2355816
31. Thakur G, Wu H (2011) Synchrosqueezing-based recovery of instantaneous frequency from nonuniform samples. SIAM J Math Anal 43(5):2078–2095. https://doi.org/10.1137/100798818
32. Yang Y, Peng ZK, Dong XJ, Zhang WM, Meng G (2014) General parameterized time–frequency transform. IEEE Trans Signal Process 62(11):2751–2764. https://doi.org/10.1109/TSP.2014.2314061
33. Yu G, Yu M, Xu C (2017) Synchroextracting transform. IEEE Trans Ind Electron 64(10):8042–8054. https://doi.org/10.1109/TIE.2017.2696503
34. Yu G, Cai Z, Chen Y, Wang X, Zhang Q, Li Y, Wang Y, Liu C, Zhao B, Greer J (2018) Borehole seismic survey using multimode optical fibers in a hybrid wireline. Measurement 125:694–703. https://doi.org/10.1016/j.measurement.2018.04.058
35. Yu G, Wang Z, Zhao P (2019) Multisynchrosqueezing transform. IEEE Trans Ind Electron 66(7):5441–5455. https://doi.org/10.1109/TIE.2018.2868296
36. Zhang LN, Ren YL, Lin RB (2020) Distributed acoustic sensing system and its application for seismological studies. Prog Geophys 35(1):0065–0071. https://doi.org/10.6038/pg2020DD0384
37. Zhou T, Tao D (2013) Greedy bilateral sketch, completion and smoothing. J Mach Learn Res 31:650–658
38. Zhou Y, Zhang S (2017) Robust noise attenuation based on nuclear norm minimization and a trace prediction strategy. J Appl Geophys 147:52–67. https://doi.org/10.1016/j.jappgeo.2017.09.005

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-792e2dcf-5e0a-4d79-b461-bee6929d86fc