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First arrivals picking based on graph signal theory

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Abstract

Picking first arrivals is a fundamental but not easy task in the oil seismic exploration, especially in situations where the signal-to-noise ratios are very low. The paper proposed a new picking algorithm with the emerging new graph signal processing technology in the signal processing field. Fundamentally, first arrival picking is actually as a problem to recognize direct seismic waves among background noises. It is apparently that background noises and the direct waves are from different sources, and therefore, there is no connectivity among them, which formulates the fundamentals of the proposed method. With the help of graph signal processing theory, such non-connectivity is fully explored and cast into an optimization problem for picking the first arrivals in oil exploration. From simulations and real measurements results, the proposed method is validated for first arrival picking with good performances, especially in situations with low signal-to-noise ratios.

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References

  1. Ma M, Wang S, Yuan S, Wang J, Wen J (2017) Multichannel spatially correlated reflectivity inversion using block sparse bayesian learning. Geophysics 82(4):V191–V199. https://doi.org/10.1190/geo2016-0366.1

    Article  Google Scholar 

  2. Flagg UG, Yarman CE (2016) First-arrival travel time tomography foran isotropic media using the adjoint-state method. Geophysics 81(4):R147–R155. https://doi.org/10.1190/geo2015-0463.1

    Article  Google Scholar 

  3. de Melo FX, Borges GA, Soares JEP (2008) Automatic wave group identification on deep seismic refraction data using SMF clustering. IEEE Geosci Remote Sens Lett 5(4):687–690. https://doi.org/10.1109/LGRS.2008.2002643

    Article  Google Scholar 

  4. Galiana-Merino JJ, Rosa-Herranz JL, Parolai S (2008) Seismic phase picking using kurtosis-based criterion in the stationary wavelet domain. IEEE Trans Geosci Remote Sens 46(11):3815–3826. https://doi.org/10.1109/tgrs.2008.2002647

    Article  Google Scholar 

  5. Fesseha Woldearegay A, Jaiswal P, Simms AR, Alexander H, Bement LC, Carter BJ (2012) Ultrashallow depth imaging of a channel stratigraphy with first-arrival travel time inversion and prestack depth migration: a case history from bull creek, oklahoma. Geophysics 77(2):B87–B96. https://doi.org/10.1190/geo2011-0218.1

    Article  Google Scholar 

  6. Hatherly PJ (1982) A computer method for determining seismic first arrival times. Geophysics 47(10):1431–1436. https://doi.org/10.1190/1.1441291

    Article  Google Scholar 

  7. Luo S, Qian J, Zhao H (2012) Higher-order schemes for 3d first-arrival traveltimes and amplitudes. Geophysics 77(2):T47–T56. https://doi.org/10.1190/geo2010-0363.1

    Article  Google Scholar 

  8. Jiao L, Moon WM (2000) Detection of seismic refraction signals using a variance fractal dimension technique. Geophysics 65(1):286–292. https://doi.org/10.1190/1.1444719

    Article  Google Scholar 

  9. Sabbione JI, Velis D (2010) Automatic first-breaks picking: new strategies and algorithms. Geophysics 75(4):V67–V76. https://doi.org/10.1190/1.3463703

    Article  Google Scholar 

  10. Mousa WA, Al-Shuhail AA, Al-Lehyani A (2011) A new technique for first-arrival picking of refracted seismic data based on digital image segmentation. Geophysics 76(5):V79–V89. https://doi.org/10.1190/geo2010-0322.1

    Article  Google Scholar 

  11. Basler-Reeder K, Louie J, Pullammanappallil S, Kent G (2016) Joint optimization of vertica component gravity and p-wave first arrivals by simulated annealing. Geophysics 81(4):ID59–ID71. https://doi.org/10.1190/geo2015-0643.1

    Article  Google Scholar 

  12. Romero D, Ma M, Giannakis GB (2017) Kernel-based reconstruction of graph signals. IEEE Trans Signal Process 65(3):764–778. https://doi.org/10.1109/TSP.2016.2620116

    Article  MathSciNet  MATH  Google Scholar 

  13. Mei J, Moura MF (2016) Signal processing on graphs: causal modeling of unstructured data. IEEE Trans Signal Process PP(99):1. https://doi.org/10.1109/tsp.2016.2634543

    Article  Google Scholar 

  14. Segarra S, Marques AG, Leus G, Ribeiro A (2016) Reconstruction of graph signals through percolation from seeding nodes. IEEE Trans Signal Process 64(16):4363–4378. https://doi.org/10.1109/TSP

    Article  MathSciNet  MATH  Google Scholar 

  15. Behjat H, Richter U, Ville DVD, Sornmo L (2016) Signal-adapted tight frames on graphs. IEEE Trans Signal Process 64(22):6017–6029. https://doi.org/10.1109/TSP.2016.2591513

    Article  MathSciNet  MATH  Google Scholar 

  16. Huang W, Goldsberry L, Wymbs NF, Grafton ST, Bassett DS, Ribeiro A (2016) Graph frequency analysis of brain signals. IEEE J Sel Top Signal Process 10(7):1189–1203. https://doi.org/10.1109/JSTSP.2016.2600859

    Article  Google Scholar 

  17. Zheng X, Tang YY, Pan J, Zhou J (2016) Adaptive multiscale decomposition of graph signals. IEEE Signal Process Lett 23(10):1389–1393. https://doi.org/10.1109/LSP.2016.2598750

    Article  Google Scholar 

  18. He K, Stankovic L, Liao J, Stankovic V (2016) Non-intrusive load disaggregation using graph signal processing. IEEE Trans Smart Grid PP(99):1. https://doi.org/10.1109/tsg.2016.2598872

    Article  Google Scholar 

  19. Bera D, Chakrabarti I, Pathak S, Karagiannidis G (2016) Another look in the analysis of cooperative spectrum sensing over nakagami-m fading channels. IEEE Trans Wirel Commun PP(99):1. https://doi.org/10.1109/twc.2016.2633259

    Article  Google Scholar 

  20. Tremblay N, Borgnat P (2016) Subgraph-based filterbanks for graph signals. IEEE Trans Signal Process 64(15):3827–3840. https://doi.org/10.1109/TSP.2016.2544747

    Article  MathSciNet  MATH  Google Scholar 

  21. Zhang D, Liang J (2017) Graph-based transform for 2-d piecewise smooth signals with random discontinuity locations. IEEE Trans Image Process PP(99):1. https://doi.org/10.1109/tip.2017.2661399

    Article  MathSciNet  Google Scholar 

  22. Fracastoro G, Magli E (2017) Steerable discrete fourier transform. IEEE Signal Process Lett PP(99):1. https://doi.org/10.1109/lsp.2017.2657889

    Article  MATH  Google Scholar 

  23. Sandryhaila A, Moura JMF (2014) Discrete signal processing on graphs: frequency analysis. IEEE Trans Signal Process 62(12):3042–3054. https://doi.org/10.1109/TSP.2014.2321121

    Article  MathSciNet  MATH  Google Scholar 

  24. Xu X, He L, Lu H, Shimada A, Taniguchi Ri (2016) Non-linear matrix completion for social image tagging. IEEE Access PP(99):1. https://doi.org/10.1109/access.2016.2624267

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computer and Information Engineering, Guangdong University Of Petrochemical Technology, Maoming, China

    Qiu-Jing Zhang & Ming-Yue Zhai

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  1. Qiu-Jing Zhang
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  2. Ming-Yue Zhai
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Contributions

QJZ and MYZ contributed to conceptualization; QJZ contributed to methodology; QJZ contributed to software; MYZ was involved in validation; MYZ contributed to formal analysis; QJZ was involved in investigation; MYZ contributed to resources; MYZ was involved in data curation; QJZ was involved in writing—original draft preparation; MYZ contributed to writing, review and editing; QJZ was involved in visualization; MYZ was involved in supervision.

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Correspondence to Ming-Yue Zhai.

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Zhang, QJ., Zhai, MY. First arrivals picking based on graph signal theory. Neural Comput & Applic 32, 1629–1637 (2020). https://doi.org/10.1007/s00521-019-04209-6

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  • DOI: https://doi.org/10.1007/s00521-019-04209-6

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