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Spark-based intelligent parameter inversion method for prestack seismic data

  • S.I. : Emergence in Human-like Intelligence towards Cyber-Physical Systems
  • Published:
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Abstract

Seismic exploration is an oil exploration method by utilizing seismic information. Useful reservoir parameter information can be gained through inversion of seismic information to effectively carry out exploration work. Prestack data are characterized by large data size and rich information. Rich reservoir parameter information can be obtained through inversion of prestack data. Due to mass prestack seismic data, existing single computer environment cannot satisfy computation requirement of huge data size. Thus, an efficient and fast method is urgently needed to solve the inversion problem of prestack seismic big data. Since local optimum may be easily caught when genetic algorithm is used to optimize elastic parameters, the inversion effect is not obvious. In particular, the optimization effect for the density parameters is not good. An intelligent optimization algorithm is proposed in this paper for elastic parameter inversion of prestack seismic data. The algorithm improves genetic manipulation. The improved algorithm has been used for model trial for log data, and good inversion effect has been achieved. The inverted elastic parameters well fit with the log curve of the theoretical model. The improved algorithm effectively improves the inversion accuracy of density parameters. In this paper, the algorithm has been implemented on Spark model, and the results show that the parallel model can effectively reduce operation time of the algorithm.

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References

  1. Neidell NS (1986) Amplitude variation with offset. The Leading Edge 5(3):47–51

    Article  Google Scholar 

  2. Li Shaopeng. The Study and Application of The Methods of AVO Seismic Parameter Inversion, Master thesis, China University of Petroleum, (in Chinese), China

  3. Chen Jianjiang (2007) Study of Three-term AVO Inversion Method. Ph.D. thesis, China University of Petroleum, (in Chinese), China.

  4. Berg E (1990) Simple convergent genetic algorithm for inversion of multiparameter data. In: Foster DJ, Keys RG (eds) SEG technical program expanded abstracts 1990. Society of Exploration Geophysicists, Tulsa, pp 1126–1128

    Chapter  Google Scholar 

  5. Mallick S (1995) Model-based inversion of amplitude-variations-with-offset data using a genetic algorithm. Geophysics 60(4):939–954

    Article  Google Scholar 

  6. Misra S, Sacchi MD (2008) Global optimization with model-space preconditioning: application to AVO inversion. Geophysics 73(5):R71–R82

    Article  Google Scholar 

  7. Pengfei Lu, Changchun Yang, Aihua Guo et al (2008) Modified simulated annealing algorithm and its application in pre-stack inversion of reservoir parameters. Prog Geophys 23(1):104–109

    Google Scholar 

  8. Zhu T, Li XF, Li YQ et al (2011) Seismic scalar wave equation inversion based on an improved particle swarm optimization algorithm. Chin J Geophys 54(11):2951–2959 (in Chinese)

    Google Scholar 

  9. Ma XQ (2002) Simultaneous inversion of prestack seismic data for rock properties using simulated annealing. Geophysics 67(6):1877–1885

    Article  Google Scholar 

  10. Buland A, Omre H (2003) Bayesian linearized AVO inversion. Geophysics 68(1):185–198

    Article  Google Scholar 

  11. Mogensen S (2001) Artificial neural networks solutions to AVO inversion problems. In: Calandra H, Khoury A, Bothorel F, Vezolles P (eds) SEG technical program expanded abstracts 2001. Society of Exploration Geophysicists, Tulsa, pp 316–319

    Chapter  Google Scholar 

  12. Agarwal A, Sain K, Shalivahan S (2016) Travel time and constrained AVO inversion using FDR PSO. In: Waters OT (ed) SEG technical program expanded abstracts 2016. Society of Exploration Geophysicists, Tulsa, pp 577–581

    Chapter  Google Scholar 

  13. Li G, You J, Liu X (2015) Support Vector Machine (SVM) based prestack AVO inversion and its applications. J Appl Geophys 120:60–68

    Article  Google Scholar 

  14. Soupios P, Akca I, Mpogiatzis P, Basokur AT, Papazachos C (2011) Applications of hybrid genetic algorithms in seismic tomography. J Appl Geophys 75(3):479–489

    Article  Google Scholar 

  15. Porsani MJ, Stoffa PL, Sen MK, Chunduru R, Wood WT (1993) A combined genetic and linear inversion algorithm for seismic waveform inversion. In: Kendall RR, Davis TL (eds) SEG technical program expanded abstracts 1993. Society of Exploration Geophysicists, Tulsa, pp 692–695

    Chapter  Google Scholar 

  16. Priezzhev I, Shmaryan L, Bejarano G (2008) Nonlinear multitrace seismic inversion using neural network and genetic algorithm-” Genetic Inversion. In: Extended abstract, EAGE conference, Saint Petersburg

  17. Junyu B, Zilong X, Yunfei X, Tianshou X (2014) Nonlinear hybrid optimization algorithm for seismic impedance inversion. In: Beijing 2014 international geophysical conference & exposition, Beijing, China, 21–24 April 2014, (pp 541–544). Society of Exploration Geophysicists and Chinese Petroleum Society

  18. Wang L P. Study on intelligent optimization algorithm with application to prestack AVO nonlinear inversion. Ph.D. thesis, China University of Geosciences, (in Chinese), China

  19. Yan Z, Gu HM, Zhao XM (2009) Non-linear AVO inversion based on ant colony algorithm. Oil Geophys Prospect 44(6):700–702 (in Chinese)

    Google Scholar 

  20. Yin C, Xie GS (2001) Seismic inversion and non-linear stochastic optimistic algorithms. Comput Tech Geophys Geochem Explor 23(1):6–10 (in Chinese)

    Google Scholar 

  21. Dean J, Ghemawat S (2008) MapReduce: simplified data processing on large clusters. Commun ACM 51(1):107–113

    Article  Google Scholar 

  22. Cui L (2014) Parallel PSO in Spark (Master’s thesis, University of Stavanger, Norway)

  23. Wang ZY, Wang HJ et al (2015) Ant colony optimization algorithm based on Spark. J Comput Appl 35(10):2777–2780 (in Chinese)

    Google Scholar 

  24. Liu ZS, Pang ZS (2016) Research on parallel SVM algorithm based on Spark. Comput Sci 43(5):238–242 (in Chinese)

    Google Scholar 

  25. Xu ZH, Zhao JW et al (2017) Study of parallel genetic algorithm using travelling salesman problem. Appl Res Comput 34(7):2080–2083 (in Chinese)

    Google Scholar 

  26. Huang JL, Li QC et al (2010) Relative wave impedances inversion based on distributed parallel genetic algorithm. J Northwest University (Nat Sci Ed) 40(5):855–859 (in Chinese)

    Google Scholar 

  27. ZoeppritzK E (1919) On the reflection and propagation of seismic waves. Gottinger Nachrichten 1:66–84

    Google Scholar 

  28. Liu B, Wang L, Jin YH (2007) An effective PSO-based memetic algorithm for flow shop scheduling. IEEE Trans Syst Man Cybern Part B (Cybern) 37(1):18–27

    Article  Google Scholar 

  29. Wang L, Pan QK, Suganthan PN, Wang WH, Wang YM (2010) A novel hybrid discrete differential evolution algorithm for blocking flow shop scheduling problems. Comput Op Res 37(3):509–520

    Article  MathSciNet  Google Scholar 

  30. Zhou A, Qu BY, Li H, Zhao SZ, Suganthan PN, Zhang Q (2011) Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm Evolut Comput 1(1):32–49

    Article  Google Scholar 

  31. Gong W, Cai Z (2013) Parameter extraction of solar cell models using repaired adaptive differential evolution. Solar Energy 94:209–220

    Article  Google Scholar 

  32. Gong M, Cai Q, Chen X, Ma L (2014) Complex network clustering by multiobjective discrete particle swarm optimization based on decomposition. IEEE Trans Evolut Comput 18(1):82–97

    Article  Google Scholar 

  33. Tang K, Peng F, Chen G, Yao X (2014) Population-based algorithm portfolios with automated constituent algorithms selection. Inf Sci 279:94–104

    Article  Google Scholar 

  34. Gong W, Zhou A, Cai Z (2015) A multioperator search strategy based on cheap surrogate models for evolutionary optimization. IEEE Trans Evolut Comput 19(5):746–758

    Article  Google Scholar 

  35. Zhou A, Sun J, Zhang Q (2015) An estimation of distribution algorithm with cheap and expensive local search methods. IEEE Trans Evolut Comput 19(6):807–822

    Article  Google Scholar 

  36. Gong W, Yan X, Liu X, Cai Z (2015) Parameter extraction of different fuel cell models with transferred adaptive differential evolution. Energy 86:139–151

    Article  Google Scholar 

  37. Wang L, Ni H, Yang R, Pardalos PM, Du X, Fei M (2015) An adaptive simplified human learning optimization algorithm. Inf Sci 320:126–139

    Article  MathSciNet  Google Scholar 

  38. Gong M, Liu J, Li H, Cai Q, Su L (2015) A multiobjective sparse feature learning model for deep neural networks. IEEE Trans Neural Netw Learn Syst 26(12):3263–3277

    Article  MathSciNet  Google Scholar 

  39. Gong W, Cai Z, Liang D (2015) Adaptive ranking mutation operator based differential evolution for constrained optimization. IEEE Trans Cybern 45(4):716–727

    Article  Google Scholar 

  40. Zhou A, Zhang Q (2016) Are all the subproblems equally important? Resource allocation in decomposition-based multiobjective evolutionary algorithms. IEEE Trans Evolut Comput 20(1):52–64

    Article  Google Scholar 

  41. Yan X, Wu Q, Sheng VS (2016) A double weighted Naive Bayes with niching cultural algorithm for multi-label classification. Int J Pattern Recognit Artif Intell 30(06):1650013

    Article  Google Scholar 

  42. Tang K, Yang P, Yao X (2016) Negatively correlated search. IEEE J Sel Areas Commun 34(3):542–550

    Article  Google Scholar 

  43. Wu Q, Liu H, Yan X (2016) Multi-label classification algorithm research based on swarm intelligence. Clust Comput 19(4):2075–2085

    Article  Google Scholar 

  44. Deng J, Wang L (2017) A competitive memetic algorithm for multi-objective distributed permutation flow shop scheduling problem. Swarm Evolut Comput 32:121–131

    Article  Google Scholar 

  45. Yan X, Liu H, Zhu Z, Wu Q (2017) Hybrid genetic algorithm for engineering design problems. Clust Comput 20(1):263–275

    Article  Google Scholar 

  46. Tang K, Wang J, Li X, Yao X (2017) A scalable approach to capacitated arc routing problems based on hierarchical decomposition. IEEE Trans Cybern 47(11):3928–3940

    Article  Google Scholar 

  47. Yan X, Sun J, Hu C (2017) Research on contaminant sources identification of uncertainty water demand using genetic algorithm. Clust Comput 20(2):1007–1016

    Article  Google Scholar 

  48. Gong W, Wang Y, Cai Z, Yang S (2017) A weighted biobjective transformation technique for locating multiple optimal solutions of nonlinear equation systems. IEEE Trans Evolut Comput 21(5):697–713

    Article  Google Scholar 

  49. Wu Q, Wang L, Zhu Z (2017) Research of pre-stack AVO elastic parameter inversion problem based on hybrid genetic algorithm. Clust Comput 20(4):3173–3783

    Article  Google Scholar 

  50. Yan X, Song T, Wu Q (2017) An improved cultural algorithm and its application in image matching. Multimed Tools Appl 76(13):14951–14968

    Article  Google Scholar 

  51. Wu Q, Zhu Z, Yan X (2017) Research on the parameter inversion problem of prestack seismic data based on improved differential evolution algorithm. Clust Comput 20(4):2881–2890

    Article  Google Scholar 

  52. Yan X, Zhao J, Hu C, Zeng D (2017) Multimodal optimization problem in contamination source determination of water supply networks. Swarm Evolut Comput. https://doi.org/10.1016/j.swevo.2017.05.010

    Article  Google Scholar 

  53. Yan X, Gong W, Wu Q (2017) Contaminant source identification of water distribution networks using cultural algorithm. Concur Comput Pract Exp. https://doi.org/10.1002/cpe.4230

    Article  Google Scholar 

  54. Yan X, Zhu Z, Li T (2017) Pollution source localization in an urban water supply network based on dynamic water demand. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-017-0516-y

    Article  Google Scholar 

  55. Yan X, Zhu Z, Wu Q (2018) Intelligent inversion method for pre-stack seismic big data based on MapReduce. Comput Geosci 110:81–89

    Article  Google Scholar 

  56. Zaharia M, Chowdhury M, Das T, Dave A, Ma J, McCauley M, Stoica I (2012) Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: Proceedings of the 9th USENIX conference on networked systems design and implementation. USENIX Association, pp 2–2

  57. Zaharia M, Chowdhury M, Das T, Dave A, Ma J, Mccauley M, Stoica I (2012) Fast and interactive analytics over Hadoop data with Spark. USENIX Login 37(4):45–51

    Google Scholar 

Download references

Acknowledgments

This paper is supported by Natural Science Foundation of China. (Nos. 61673354 and 61573324), the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan), the State Key Lab of Digital Manufacturing Equipment and Technology (DMETKF2018020) and the State Key Laboratory of Intelligent Control and Decision of Complex Systems.

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Correspondence to Qinghua Wu.

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Yan, X., Zhu, Z., Hu, C. et al. Spark-based intelligent parameter inversion method for prestack seismic data. Neural Comput & Applic 31, 4577–4593 (2019). https://doi.org/10.1007/s00521-018-3457-6

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