1887
  1. Home
  2. A-Z Publications
  3. Geophysical Prospecting
  4. Volume 68, Issue 5
  5. Article
Volume 68, Issue 5
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

A number of deblending methods and workflows have been reported in the past decades to eliminate the source interference noise recorded during a simultaneous shooting acquisition. It is common that denoising algorithms focusing on optimizing coherency and weighting down/ignoring outliers can be considered as deblending tools. Such algorithms are not only enforcing coherency but also handling outliers either explicitly or implicitly. In this paper, we present a novel approach based on detecting amplitude outliers and its application on deblending based on a local outlier factor that assigns an outlier‐ness (i.e. a degree of being an outlier) to each sample of the data. A local outlier factor algorithm quantifies outlier‐ness for an object in a data set based on the degree of isolation compared with its locally neighbouring objects. Assuming that the seismic pre‐stack data acquired by simultaneous shooting are composed of a set of non‐outliers and outliers, the local outlier factor algorithm evaluates the outlier‐ness of each object. Therefore, we can separate the data set into blending noise (i.e. outlier) and signal (i.e. non‐outlier) components. By applying a proper threshold, objects having high local outlier factors are labelled as outlier/blending noise, and the corresponding data sample could be replaced by zero or a statistically adequate value. Beginning with an explanation of parameter definitions and properties of local outlier factor, we investigate the feasibility of a local outlier factor application on seismic deblending by analysing the parameters of local outlier factor and suggesting specific deblending strategies. Field data examples recorded during simultaneous shooting acquisition show that the local outlier factor algorithm combined with a thresholding can detect and attenuate blending noise. Although the local outlier factor application on deblending shows a few shortcomings, it is consequently noted that the local outlier factor application in this paper obviously achieves benefits in terms of detecting and attenuating blending noise and paves the way for further geophysical applications.

© 2020 European Association of Geoscientists & Engineers © 2020 European Association of Geoscientists & Engineers
Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.12945
2020-04-21
2024-04-27

  • From This Site

    /content/journals/10.1111/1365-2478.12945
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. AbmaR.L., ManningT., TanisM., YuJ. and FosterM.2010. High quality separation of simultaneous sources by sparse inversion. 72nd EAGE Annual International Meeting Extended AbstractsB003.
  2. AbmaR.L. and YanJ.2009. Separating simultaneous sources by inversion. 71st EAGE Annual International Meeting Extended AbstractsV002.
  3. AkerbergP., HampsonD., RickettJ., MartinH. and ColeJ.2008. Simultaneous source separation by sparse radon transform. SEG Technical Program Expanded Abstracts2801–2805.
  4. BarnesE. and LaughlinK.J.2002. Investigation of methods for unsupervised classification of seismic data. SEG Technical Program Expanded Abstracts2221–2224.
  5. BeasleyC.J., ChambersR.E. and JiangZ.1998. A new look at simultaneous sources. SEG Technical Program Expanded Abstracts133–135.
  6. BerkhoutA.J.2008. Changing the mindset in seismic data acquisition. The Leading Edge27, 824–938.
     [Google Scholar]
  7. BerkhoutA.J.2012. Blended acquisition with dispersed source arrays. Geophysics77, A19–A23.
     [Google Scholar]
  8. BreunigM.M., KriegelH.‐P., NgR.T. and SanderJ.2000. LOF: identifying density based local outliers. Proc. ACM SIGMOD Conf427–438.
  9. CandèsE.J., LiX., MaY. and WrightJ.2011. Robust principal component analysis?. Journal of Association for Computing Machinery58, 11:1–11:37.
     [Google Scholar]
  10. ChenK. and SacchiM.D.2015. Robust reduced‐rank filtering for erratic seismic noise attenuation. Geophysics80, V1–V11.
     [Google Scholar]
  11. ChenY., FomelS. and HuJ.2014. Iterative deblending of simultaneous‐source seismic data using seislet‐domain shaping regularization. Geophysics79, V179–V189.
     [Google Scholar]
  12. ChenY., ZuS., WangY. and ChenX.2019. Deblending of simultaneous source data using a structure‐oriented space‐varying median filter. Geophysical Journal International216, 1214–1232.
     [Google Scholar]
  13. ChengJ. and SacchiM.D.2015. Separation and reconstruction of simultaneous source data via iterative rank reduction. Geophysics80, V57–V66.
     [Google Scholar]
  14. ChengJ. and SacchiM.D.2016. Fast dual‐domain reduced‐rank algorithm for 3D deblending via randomized QR decomposition. Geophysics81, V89–V101.
     [Google Scholar]
  15. ColèuoT., PouponM. and AzbelK.2003. Unsupervised seismic facies classification: A review and comparison of techniques and implementation. The Leading Edge22, 942–953.
     [Google Scholar]
  16. DiH., ShafiqM. and AlRegibG.2018. Multi‐attribute k‐means clustering for salt‐boundary delineation from three‐dimensional seismic data. Geophysical Journal International215, 1999–2007.
     [Google Scholar]
  17. FomelS.2007. Local seismic attributes. Geophysics72, A29‐A33.
     [Google Scholar]
  18. GalvisI.S., VillaY., DuarteC., SieeraD. and AgudeloW.2017. Seismic attribute selection and clustering to detect and classify surface waves in multicomponent seismic data by using k‐mean algorithm. The Leading Edge36, 239–248.
     [Google Scholar]
  19. HampsonG., StefaniJ. and HerkenhoffF.2008. Acquisition using simultaneous sources. The Leading Edge27, 918–923.
     [Google Scholar]
  20. HawkinsD.1980. Identification of outliers. Chapman and Hall.
     [Google Scholar]
  21. HuangW., WangR., GongX. and ChenY.2018. Iterative deblending of simultaneous‐source seismic data with structuring median constraint. IEEE Geoscience and Remote Sensing Letters15, 58–62.
     [Google Scholar]
  22. HuoS., LuoY. and KelamisP.G.2012. Simultaneous source separation via multidirectional vector‐median filtering. Geophysics77, V123–V131.
     [Google Scholar]
  23. JiH., HuangS., ShenZ. and XuY.2010. Robust video restoration by joint sparse and low rank approximation. SIAM Journal on Imaging Sciences4, 1122–1142.
     [Google Scholar]
  24. KriegelH.‐P., KrögerP., SchubertE. and ZimekA.2009. LoOP: local outlier probabilities. Proceedings of the 18th ACM Conference on Information and Knowledge Management1649–1652.
  25. MahdadA., DoulgerisP. and BlacquiereG.2011. Separation of blended data by iterative estimation and subtraction of blending interference noise. Geophysics76, Q9–Q17.
     [Google Scholar]
  26. MahdadA., DoulgerisP. and BlacquiereG.2012. Iterative method for the separation of blended seismic data: discussion on the algorithmic aspects. Geophysical Prospecting60, 782–801.
     [Google Scholar]
  27. MarroquínI.D., BraultJ.‐J. and HartB.S.2009a. A visual data‐mining methodology for seismic facies analysis: Part 1—Testing and comparison with other unsupervised clustering methods. Geophysics74, P1–P11.
     [Google Scholar]
  28. MarroquínI.D., BraultJ.‐J. and HartB.S.2009b. A visual data‐mining methodology for seismic facies analysis: Part 2—Application to 3D seismic data. Geophysics74, P13–P23.
     [Google Scholar]
  29. MooreI.2010. Simultaneous sources: processing and applications. 72nd Conference and Exhibition, EAGE, Extended AbstractsB001.
  30. SternfelsR., ViguierG., GondoinR. and MeurD.L.2015. Multidimensional simultaneous random plus erratic noise attenuation and interpolation for seismic data by joint low‐rank and sparse inversion. Geophysics80, WD129–WD141.
     [Google Scholar]
  31. TrickettS.R., BurroughsL. and MiltonA.2012. Robust rank‐reduction filtering for erratic noise. SEG Technical Program Expanded Abstracts1–5.
  32. TsingasC., KimY.S. and YooJ.2016. Broadband acquisition, deblending, and imaging employing dispersed source arrays. The Leading Edge35, 354–360.
     [Google Scholar]
  33. WronaT., PanI., GawthorpeR.L. and FossenH.2018. Seismic facies analysis using machine learning. Geophysics83, O83–O95.
     [Google Scholar]
  34. XiaK., HiltermanF. and HuH.2018. Unsupervised machine learning algorithm for detecting and outlining surface waves on seismic shot gathers. Journal of Applied Geophysics157, 73–86.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1111/1365-2478.12945
Loading
Local outlier factor as part of a workflow for detecting and attenuating blending noise in simultaneously acquired data
Geophysical Prospecting 68, 1523 (2020); https://doi.org/10.1111/1365-2478.12945
/content/journals/10.1111/1365-2478.12945
/content/journals/10.1111/1365-2478.12945
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): Deblending; Erratic noise; Local outlier factor; Signal processing

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error