Semi‐auto horizon tracking guided by strata histograms generated with transdimensional Markov‐chain Monte Carlo

References

1. AkiK. and RichardsP.G.2002. Quantitative Seismology, 2nd edn. University Science Books.
   [Google Scholar]
2. AlbertsP., WarnerM. and ListerD.2000. Artificial neural networks for simultaneous multi horizon tracking across discontinuities. SEG Technical Program, Expanded Abstracts, 651–653.
3. BarnesA.E.2000. Weighted average seismic attributes. Geophysics65, 275–285.
   [Google Scholar]
4. BodinT. and SambridgeM.2009. Seismic tomography with the reversible jump algorithm. Geophysical Journal International178, 1411–1436.
   [Google Scholar]
5. BodinT., SambridgeM., TkalčićH., ArroucauP., GallagherK. and RawlinsonN.2012. Transdimensional inversion of receiver functions and surface wave dispersion. Journal of Geophysical Research: Solid Earth117, B02301.
   [Google Scholar]
6. BorgosH.G., SkovT., RandenT. and SonnelandL.2003. Automated geometry extraction from 3D seismic data. SEG Technical Program, Expanded Abstracts, 1541–1544.
7. ChoY., GibsonJr, R.L. and ZhuD.2018. Quasi 3D transdimensional Markov‐chain Monte Carlo for seismic impedance inversion and uncertainty analysis. Interpretation6, T613–T624.
   [Google Scholar]
8. DenisonD.G., HolmesC.C., MallickB.K. and SmithA.F.2002. Bayesian Methods for Nonlinear Classification and Regression. John Wiley & Sons.
   [Google Scholar]
9. DiH., GaoD. and AlRegibG.2018. 3D structural‐orientation vector guided autotracking for weak seismic reflections: a new tool for shale reservoir visualization and interpretation. Interpretation6, SN47–SN56.
   [Google Scholar]
10. DornG.A.1998. Modern 3‐D seismic interpretation. The Leading Edge17, 1262–1262.
    [Google Scholar]
11. FehmersG.C. and HöckerC.F.2003. Fast structural interpretation with structure‐oriented filtering. Geophysics68, 1286–1293.
    [Google Scholar]
12. GreenP.J.1995. Reversible jump Markov chain Monte Carlo computation and bayesian model determination. Biometrika82, 711–732.
    [Google Scholar]
13. GuptaR., CheretT., PaugetF. and LacazeS.2008. Automated geomodelling a Nigeria case study. Proceedings of the 70th EAGE Conference and Exhibition Incorporating SPE EUROPEC, June 2008.
14. HarriganE., KrohJ., SandhamW. and DurraniT.1992. Seismic horizon picking using an artificial neural network. Proceedings of the ICASSP‐92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, San Francisco, CA, March 1992, pp. 105–108.
15. HoyesJ. and CheretT.2011. A review of “global” interpretation methods for automated 3D horizon picking. The Leading Edge30, 38–47.
    [Google Scholar]
16. HuangY. and DiH.2017. Dip interpolation for improved multitrace seismic‐attribute analysis. SEG Technical Program Expanded Abstracts, 2091–2095.
17. JunH., ChoY. and NohJ.2019. Trans‐dimensional Markov chain Monte Carlo inversion of sound speed and temperature: application to yellow sea multichannel seismic data. Journal of Marine Systems197, 103180.
    [Google Scholar]
18. KempF., ThreetJ.R. and VeezhinathanJ.1992. A neural net branch and bound seismic horizon tracker. SEG Technical Program, Expanded Abstracts, 10–13.
19. LeggettM., SandhamW. and DurraniT.1994. 3D seismic horizon tracking using an artificial neural network. 56th EAGE meeting, June 1994.
20. LomaskJ. and GuittonA.2007. Volumetric flattening: an interpretation tool. The Leading Edge26, 888–897.
    [Google Scholar]
21. MalinvernoA.2002. Parsimonious Bayesian Markov chain Monte Carlo inversion in a nonlinear geophysical problem. Geophysical Journal International151, 675–688.
    [Google Scholar]
22. MarfurtK.J., KirlinR.L., FarmerS.L. and BahorichM.S.1998. 3‐D seismic attributes using a semblance‐based coherency algorithm. Geophysics63, 1150–1165.
    [Google Scholar]
23. PaugetF., LacazeS. and ValdingT.2009. A global interpretation based on cost function minimization. 79th SEG Annual International meeting, Expanded Abstracts, 2592–2596.
24. RonnebergerO., FischerP. and BroxT.2015. U‐net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer‐Assisted Intervention (eds N.Navab, J.Hornegger, W.Wells and A.Frangi), pp. 234–241. Springer.
    [Google Scholar]
25. SambridgeM., GallagherK., JacksonA. and RickwoodP.2006. Trans‐dimensional inverse problems, model comparison and the evidence. Geophysical Journal International167, 528–542.
    [Google Scholar]
26. WuH., ZhangB., LinT., CaoD. and LouY.2019. Semiautomated seismic horizon interpretation using the encoder‐decoder convolutional neural network. Geophysics84, B403–B417.
    [Google Scholar]
27. WuX. and HaleD.2015. Horizon volumes with interpreted constraints. Geophysics80, IM21–IM33.
    [Google Scholar]
28. WuX. and JansonX.2017. Directional structure tensors in estimating seismic structural and stratigraphic orientations. Geophysical Journal International210, 534–548.
    [Google Scholar]
29. YangL. and SunS.Z.2019. Seismic horizon tracking using a deep convolutional neural network. Journal of Petroleum Science and Engineering, 106709.
    [Google Scholar]
30. YuY., KelleyC. and MardanovaI.2011. Automatic horizon picking in 3d seismic data using optical filters and minimum spanning tree (patent pending). SEG Technical Program, Expanded Abstracts, 965–969.
31. ZhaoT.2018. Seismic facies classification using different deep convolutional neural networks. SEG Technical Program, Expanded Abstracts, 2046–2050.
32. ZhuD. and GibsonR.2018. Seismic inversion and uncertainty quantification using transdimensional Markov chain Monte Carlo method. Geophysics83, R321–R334.
    [Google Scholar]
33. Zoeppritz, K. (1919) Erdbebenwellen VII. VIIb. Über Reflexion und Durchgang seismischer Wellen durch Unstetigkeitsflächen. Nachrichten von der Königlichen Gesellschaft der Wissenschaften zu Göttingen, Mathematisch‐physikalische Klasse, 66–84.