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Statistical characteristics for the background noise in distributed acoustic sensing: analysis and application to suppression

T. ZHONG1,2 ,  Y. CHEN2 ,  X.T. DONG3 ,  Y. LI4,* ,  N. WU4
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1 Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology, Jilin 132012, P.R. China,
2 Northeast Electric Power University, Department of Communication Engineering, Jilin 132012, P.R. China,
3 Jilin University, College of Instrumentation and Electrical Engineering, Changchun 130026, P.R. China,
4 Jilin University, Department of Information Engineering, Changchun 130026, P.R. China,
JSE 2022, 31(2), 131–151;
Submitted: 13 February 2021 | Accepted: 6 December 2021 | Published: 1 April 2022
© 2022 by the Authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Zhong, T., Chen, Y., Dong, X.T., Li, Y. and Wu, N., 2022. Statistical characteristics for the background noise in distributed acoustic sensing: analysis and application to suppression. Journal of Seismic Exploration, 31: 131-151. Distributed acoustic sensing (DAS) is a novel technology that utilizes a fiber-optic cable instead of geophones, which has attracted increasing attention in seismic data acquisition. However, owing to the existence of background noise, the current quality of the DAS records requires improvement. In this study, the stationarity and spectral characteristics for DAS background noise are investigated. Additionally, the dataset used for the analysis is collected while satisfying the practical requirements of the exploration industry. The results demonstrate that the DAS background noise is a broadband interference with local stationarity. On this basis, an adaptive time-frequency peak filtering (TFPF) algorithm is proposed to attenuate the background noise. Unlike traditional TFPF algorithms, this improved method adaptively chooses appropriate filtering parameters instead of using a fixed parameter set to the whole seismic record to achieve better attenuation performance. Specifically, the signal and noise segments can be recognized by taking advantage of the differences in stationarity. Consequently, we can adaptively select different filtering parameters for signal and noise segments to get better performance in noise attenuation and signal restoration. Synthetic and field data experimental results indicate that the proposed adaptive TFPF algorithm can suppress the DAS background noise and accurately recover the reflection events, especially under low signal-to-noise ratio conditions.

Keywords
distributed acoustic sensing
background noise attenuation
time-frequency peak filtering
seismic data processing
References
  1. Ardekani, I.T. and Abdulla, W.H., 2013. Stochastic modelling and analysis offiltered-xleast-mean-square adaptation algorithm. IET Sign. Process., 7: 486-496.
  2. Azerad, P., Bouharguane, A. and Crouzet, J.F., 2012. Simultaneous denoising andenhancement of signals by a fractal conservation law. Commun. Nonlinear. Sci., 17:867-811.
  3. Bayram, M. and Baraniuk, R., 2000. Multiple window time-varying spectrum estimation.
  4. In: Fitzgerald, W.J. (Ed.), Nonlinear and Nonstationary Signal Processing.Cambridge University Press, Cambridge: 292-316.
  5. Bellefleur, G , Schetselaar, E., Wade, D., White, D., Enkin, R. and Schmitt, D.R., 2020.
  6. Vertical seismic profiling using Distributed Acoustic Sensing (DAS) withscatter-enhanced fibre-optic cable at the Cu-Au new Afton Porphyry deposit, BritishColumbia, Canada. Geophys. Prosp., 68: 313-333.
  7. Binder, G., Titov A., Liu Y., Simmons, J. and Monk, D., 2020. Modeling the seismicresponse of individual hydraulic fracturing stages observed in a time-lapse DASVSP survey. Geophysics, 85(4): T225-T235.
  8. Boashash, B. and Mesbah, M., 2004. Signal enhancement by time-frequency peakfiltering. IEEE Transact. Signal Process., 52: 929-937.
  9. Chatfield, C., 2003. The Analysis of Time Series: An Instruction, 6th ed.. Chapman andHall/CRC Press, Houston.
  10. Correa, J., Egorov, A., Tertyshnikov, K., Bona, A. and Pevzner, R., 2017. Analysis ofsignal to noise and directivity characteristics of DAS VSP at near and far offsets - a
  11. CO2CRC Otway Project data example. The Leading Edge, 36: 994a1—994a7.
  12. Daley, T.M., Miller, D.E., Dodds, K., Cook, P. and Freifeld, B. M., 2016. Field testing ofmodular borehole monitoring with simultaneous distributed acoustic sensing andgeophone vertical seismic profiles at Citronelle, Alabama. Geophys. Prosp., 64:1318-1334.
  13. Dong, X., Li, Y., Wu, N., Tian, Y. and Yu, P., 2018. The S-STK/LTK algorithm forarrival time picking of microseismic signals. J. Geophys. Engineer., 15:1484-1491.150
  14. Egorov, A., Correa, J., Bona, A., Pevzner, R., Tertyshnikov, K., Glubokovskikh, S.,
  15. Puzyev, V. and Gurevich, B., 2018. Elastic full waveform inversion of verticalseismic profile data acquired with distributed acoustic sensors. Geophysics, 83(3),R273-R281.
  16. Flandrin, P., Rilling, G., Goncalves, P., 2014. Empirical mode decomposition as a filterbank. IEEE Signal Process. Lett., 11: 112-114.
  17. Gang, Y., Zhidong, C., Yuanzhong, C., Wang, X., Zhang, Q., Li, Y., Wang, Y., Liu, C.,
  18. Zhao, B. and Joe, G., 2018. Borehole seismic survey using multimode optical fibersin a hybrid wireline. Measurement, 125: 694-703.
  19. Gotz, J., Luth, S., Henninges, J. and Thomas, R., 2018. Vertical seismic profiling usingdaisy-chained deployment of fibre-optic cables in four wells simultaneously - casestudy at the Ketzin carbon dioxide storage site. Geophys. Prosp., 66: 1201-1214.
  20. Harris, K., White, D. and Samson, C., 2017. Imaging the Aquistore reservoir after 36kilotonnes of CO2 injection using distributed acoustic sensing. Geophysics, 82(6):M81-M96.
  21. Hartog, A.H., 2018. An Introduction to Distributed Optical Fibre Sensing. CRC Press,
  22. Houston. Jiang C., Li Y.. Wu N., et al. 2011. Radial-trace time-frequency peakfiltering based on correlation integral. IEEE Geosci. Remote Sens. Lett., 11:1594-1598.
  23. Karrenbach, M., Cole, S., Ridge, A., Boone, K., Kahn, D., Rich, J., Silver, K. and Langto,
  24. D., 2019. Fiber-optic distributed acoustic sensing of microseismicity, strain andtemperature during hydraulic fracturing. Geophysics, 84(1): D11-D23.
  25. Kobayashi, Y., Uematsu, Y., Mochiji, S. and Xue, Z., 2020. A field experiment ofwalkaway distributed acoustic sensing vertical seismic profile in a deep anddeviated onshore well in Japan using a fibre optic cable deployed inside coiledtubing. Geophys. Prosp., 68: 501-520.
  26. Li, G., Li, Y. and Yang, B., 2017. Seismic exploration random noise on land: modelingand application to noise suppression. IEEE Transact. Geosci. Remote Sens., 55:4668-4681.
  27. Mateeva, A., Lopez, J., Potters, H., Mestayer, J., Cox, B., Kiyashchenko, D., Wills, P.,
  28. Grandi, S., Hornman, K., Kuvshinov, B., Berlang, W., Yang, Z. and Detomo, R.,
  29. Distributed acoustic sensing for reservoir monitoring with vertical seismicprofiling, Geophys. Prosp., 62: 679-692.
  30. Martins, H.F., Fernandez-Ruiz, M., Costa, L., Williams, E. and Gonzalez-Herraez. M..,,
  31. Monitoring of remote seismic events in metropolitan area fibers usingdistributed acoustic sensing (DAS) and spatiotemporal signal processing. OpticalFiber Communication Conference, San Diego.
  32. Meng, F., Li, Y., Wu, N. and Lin, H., 2015. A fractal conservation law for simultaneousdenoising and enhancement of seismic data. IEEE Geosci. Remote Sens. Lett., 12:374-378.
  33. Moghtaderi, A., Flandrin, P. and Borgnat, P., 2013. Trend filtering via empirical modedecompositions. Computat. Statist. Data Analys., 58: 114-126.
  34. Parker, T., Shatalin, S. and Farhadiroushan, M. 2014. Distributed acoustic sensing - anew tool for seismic applications. First Break , 32: 61-69.
  35. Poletto, F., Finfer, D., Corubolo, P. and Farina, B., 2016. Dual wavefields fromdistributed acoustic sensing measurements. Geophysics, 81(6): D585-D597.
  36. Riedel, M., Cosma, C., Enescu, N., Koivisto, E., Komminaho, K and Vaittinen, K., 2018.
  37. Underground vertical seismic profiling with conventional and fiber-optic systemsfor exploration in the Kylylahti Polymetallic Mine, Eastern Finland. Minerals, 8(11):
  38. Rodrigue, LV. and Wuestefeld, A., 2020. Strain microseismics: Radiation patterns,synthetics, and moment tensor resolvability with distributed acoustic sensing inisotropic media. Geophysics, 85(3): KS101-KS114.
  39. Soto, M.A., Ramirez, J. and Thévenaz, A.L., 2016. Intensifying the response ofdistributed optical fibre sensors using 2D and 3D image restoration. NatureCommun., 7: 10870.
  40. Souza, D., Chanussot, J., Favre, A.C. and Borgnat, P., 2013. A new nonparametricmethod for testing stationarity based on trend analysis in the time marginaldistribution. IEEE International Conference on Acoustics, Speech and SignalProcessing, 320-324.151
  41. Spikes, K.T., Tisato, N., Hess, T.E. and Holt, J.W., 2019. Comparison of geophone andsurface-deployed distributed acoustic sensing seismic data. Geophysics, 84(2):A25-A29.
  42. Thomson, D.J., 1982. Spectrum estimation and harmonic analysis. Proceedings of theIEEE, 70: 1055-1096.
  43. Verdon, J.P., Horne, S.A., Clarke, A., Stock, A.L. and Kendall, J.M., 2020. Microseismicmonitoring using a fiber-optic distributed acoustic sensor array. Geophysics, 85(3):KS89-KS99.
  44. Wu, N., Li, Y. and Yang, B.J., 2011. Noise attenuation for 2-D seismic data byradial-trace time-frequency peak filtering. IEEE Geosci. Remote Sens. Lett., 8:874-878.
  45. Zhang, C., Li, Y., Lin, H. and Yang, B., 2015. Signal preserving and seismic randomnoise attenuation by Hurst exponent based time—frequency peak filtering. Geophys.J. Internat., 203: 901-909.
  46. Zhong, T., Li, Y., Wu, N., Nie, P. and Yang, B., 2015. Statistical analysis of backgroundnoise in seismic prospecting. Geophys. Prosp., 60: 1161-1174.
  47. Zhong, T., Li, Y., Wu, N., Nie, P. and Yang, B., 2015. A study on the stationarity and
  48. Gaussianity of the background noise in land seismic prospecting. Geophysics, 80(4):V67-V82.
  49. Zhong, T., Zhang, S., Li, Y. and Yang, B., 2019. Simulation of seismic-prospectingrandom noise in the desert by a Brownian-motion-based parametric modelingalgorithm. Compt. Rend. Geosci., 351: 10-16.
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Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing
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