A hybrid monte carlo method based artificial neural networks approach for rock boundaries identification: A case study from the KTB bore hole

Pure and Applied Geophysics

Volumn 166, Issue 12, 2009, Pages 2059-2090

  Maiti, Saumen ^a   Tiwari, R K ^b  

^a INDIAN INSTITUTE OF GEOMAGNETISM   (India)

^b NATIONAL GEOPHYSICAL RESEARCH INSTITUTE   (India)

Author keywords

And uncertainty analysis; Hybrid Monte Carlo (HMC); KTB boreholes; Lithofacies; Petrophysics; Well log

Indexed keywords

ALGORITHM; ARTIFICIAL NEURAL NETWORK; BAYESIAN ANALYSIS; DATA INVERSION; DEEP DRILLING; KTB BOREHOLE; LITHOFACIES; MARKOV CHAIN; MODELING; MONTE CARLO ANALYSIS; UNCERTAINTY ANALYSIS; WELL LOGGING;

EID: 73649115660     PISSN: 00334553     EISSN: 14209136     Source Type: Journal    
DOI: 10.1007/s00024-009-0533-y     Document Type: Article

References (25)

1. BERCKHEMER, H., RAUEN, A., WINTER, H., KERN, H., KONTNY, A., LIENERT, M., NOVER, G., POHL, J., POPP, T., SCHULT, A., ZINKE, J., and SOFFEL, H.C. (1997), Petrophysical properties of the 9-km deep crustal selection at KTB, J. Geophys. Res. 102, B8, 18337-18361.
2. BISHOP, C.M., Neural Networks for Pattern Recognition. (Oxford University Press, 1995).
3. BACKUS, G.E. and GILBERT, J.F. (1967), Numerical applications of formalism for geophysical inverse problems, Geophys. J. R. Astron. Soc., 13, 247-276.
4. DUANE, S., KENNEDY, A.D., PENDLETON, B., and ROWETH, D. (1987), Hybrid Monte Carlo, Phys. Lett. B 195, 216-222.
5. HASTINGS, W.K. (1970), Monte Carlo sampling methods using Markov Chain and their applications, Biometrika 57, 97-109.
6. METROPOLIS, N., ROSENBLUTH, A. W., ROSENBLUTH, M.N., TELLER, A.H., and TELLER, E. (1953), Equations of state calculations by fast computing machines. J. Chem. Phys. 21, 1087-1091.
7. METROPOLIS, N. and ULAM, S.M., (1949), The Monte Carlo method, J. Am. Stat. Assoc. 44,335-341.
8. EMMERMANN, R. and LAUTERJUNG, J. (1997), The German Continental Deep Drilling Program KTB: Overview and major results, J. Geophys. Res. 102, 18179-18201.
9. FRANKE, W. (1989), The geological framework of the KTB drill site. In Emmermann, R., Wohlenberg, J., (Eds.), The German Continental Deep Drilling Program (KTB). (Springer, Berlin, 1989) pp. 38-54.
10. FULLER, W.A., Introduction to statistical time series (John Wiley and Sons, New York, 1976).
11. KHAN, M.S. and COULIBALY, P. (2006), Bayesian neural network for rainfall-runoff modeling, Water Resour. Res. 42, W07409, doi: 10.1029/2005WR003971.
12. LAMPINEN, J. and VEHTARI, A. (2001), Bayesian Approach for neural networks-review and case studies, Neural Networks 14(3), 7-24.
13. LEONARDI, S. and KUMPEL, H. (1998), Variability of geophysical log data and signature of crustal heterogeneities at the KTB, Geophys. J. Int. 135, 964-974.
14. LEONARDI, S. and KUMPEL, H. (1999), Fractal variability in super deep borehole-implications for the signature of crustal heterogeneities, Tectonophysics 301, 173-181.
15. MACKAY, D.J.C. (1992), A practical Bayesian framework for back-propagation networks, Neural Comput. 4(3), 448-472.
16. MAITI, S. and TIWARI, R.K. (2005), Identifying lithofacies boundaries using super self adaptive back propagation neural network (SSAB): A Case Study from the KTB Borehole, Petrotech, Delhi, Extended Abstract.
17. MAITI, S., TIWARI, R.K., and KUMPEL, H.J., (2007), Neural network modeling and classification of lithofacies using well log data: a case study from KTB borehole site, Geophys. J. Int. 169, 733-746.
18. th Biennial International Conference and Exposition on Petroleum Geophysics, SPG Hyderabad, Extended Abstract.
19. MOSEGAARD, K. and TARANTOLA, A., (1995), Monte Carlo sampling of solutions to inverse problem, Jr. Geophys. Res. 100, B7, 12,431-12,447.
20. NABNEY, I.T., Netlab Algorithms for Pattern Recognition (Springer, New York 2004).
21. NEAL, R.M, Bayesian learning via stochastic dynamics. In Advances in Neural Information Processing Systems 5, (eds) C.L.Giles et al.), pp 475-482, (Elsevier, New York 1993).
22. PECHNIG, P., HAVERKAMP, S., WOHLENBERG, J., ZIMMERMANN, G., and BURKHARDT, H., (1997), Integrated interpretation in the German Continental Deep Drilling Program: Lithology, porosity, and fracture zones. J. Geophys. Res., 102, 18363-18390.
23. POULTON, M., Ed., Computational Neural Networks for Geophysical Data Processing, (Pergamon 2001).
24. SAMBRIDGE, M. and MOSEGAARD, K. (2002), Monte Carlo methods in geophysical inverse problems, Rev. Geophys., 40, 3, 1-29.
25. TARANTOLA, A., Inverse Problem Theory (Elsevier, New York, 1987).