Abstract
The process of pre-stack AVO(Amplitude Variation with Offset) elastic parameter inversion is the process of optimization, which can be solved by using genetic algorithm. However, when solving the problem, the traditional genetic algorithm converges speedily and is easily trapped into the problems like local optimum, etc., which leads to unsatisfactory inversion effect. To solve the above problems, this paper introduces simulated annealing algorithm into genetic algorithm and improves the genetic algorithm in the aspects of population initialization, selection strategy and generic manipulation. A hybrid intelligent algorithm is proposed in this paper which is more suitable for solving pre-stack AVO elastic parameter inversion problem. The experimental results show that the proposed hybrid intelligent algorithm in this paper can fully exert the global searching capability of genetic algorithm and prevent the algorithm from being trapped into local optimum by using simulated annealing algorithm. The Gardner relation is utilized to initialize population in order to make initialization of density correspond to the practical terrain conditions better and obviously improve the inversion accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Neidell, N.S.: Amplitude variation with offset. Lead. Edge 5(3), 47–51 (1986)
Li, S.: The Study and Application of AVO Seismic Parameter Inversion Method, China University of Petroleum (2009)
Chen, J.: Study of Three-term AVO Inversion Method, China University of Petroleum (2007)
Wang, J.: Lecture on nonlinear inverse methods in geophysics (1)-introduction to geophysical inverse problems. Chin. J. Eng. Geophys. 4(1), 1–3 (2007)
Wang, J.: Lecture on nonlinear inverse methods in geophysics (2)-Monte Carlo method. Chin. J. Eng. Geophys. 4(2), 81–85 (2007)
Shi, X., Wang, J.: Lecture on nonlinear inverse methods in geophysics (3)-simulated annealing method. Chin. J. Eng. Geophys. 4(3), 165–174 (2007)
Shi, X., Wang, J.: Lecture on nonlinear inverse methods in geophysics (4)-genetic algorithm. Chin. J. Eng. Geophys. 5(2), 129–140 (2008)
Wang, J.: Lecture on nonlinear inverse methods in geophysics (5)-the artificial neural network method. Chin. J. Eng. Geophys. 5(3), 255–265 (2008)
Zhu, P., Wang, J.: Lecture on nonlinear inverse methods in geophysical data (6)-conjugate gradient method. Chin. J. Eng. Geophys. 5(4), 381–386 (2008)
Wang, S., Liu, Y., Wang, J.: Lecture on nonlinear inverse methods in geophysical data (9)-ant colony optimization. Chin. J. Eng. Geophys. 2, 131–136 (2009)
Yi, Y., Wang, J.: Lecture on nonlinear inverse methods in geophysical data (10)-particle swarm optimization inversion method. Chin. J. Eng. Geophys. 6(4), 385–389 (2009)
Berg, E.: Simple convergent genetic algorithm for inversion of multiparameter data. In: SEG Annual Meeting. Society of Exploration Geophysicists (1990)
Mallick, S.: Model-based inversion of amplitude-variations-with-offset data using a genetic algorithm. Geophysics 60(4), 939–954 (1995)
Misra, S., Sacchi, M.D.: Global optimization with model-space preconditioning: application to AVO inversion. Geophysics 73(5), R71–R82 (2008)
Lu, P., Yang, C., Guo, A.: Modified simulated annealing algorithm and its applicationin pre-stack inversion of reservoir parameters. Prog. Geophys. 23(1), 104–109 (2008)
Tongzhu, X.L., Li, Y.: Seismic scalar wave equation inversion based on an improved particle swarm optimization algorithm. Chin. J. Eng. Geophys. 54(11), 2951–2959 (2011)
Stoffa, P.L., Sen, M.K., Chunduru, R., et al.: A combined genetic and linear inversion algorithm for seismic waveform inversion (1993)
Priezzhev, I., Shmaryan, L., Bejarano, G.: Nonlinear multitrace seismic inversion using neural network and genetic algorithm. In: EAGE Conference on Genetic Inversion Extended abstract, Saint Petersburg (2008)
Soupios, P., Akca, I., Mpogiatzis, P., et al.: Applications of hybrid genetic algorithms in seismic tomography. J. Appl. Geophys. 75(3), 479–489 (2011)
Junyu, B., Zilong, X., Yunfei, X.: Nonlinear hybrid optimization algorithm for seismic impedance inversion. In: Hao, H., Beijing, L.A., et al. (eds.) International Geophysical Conference and Exposition, Beijing, China, 21–24 April 2014, pp. 541–544. Society of Exploration Geophysicists and Chinese Petroleum Society (2014)
Wang, L.: Study on intelligent optimization algorithm with application to pre-stack AVO nonlinear inversion, China University of Petroleum (2015)
Acknowledgment
This paper is supported by Natural Science Foundation of China. (No. 61272470, 61305087, 61440060, 41404076, 61673354), the Provincial Natural Science Foundation of Hubei (No. 2015CFA065).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wu, Q., Hao, Y., Yan, X. (2016). Study on Hybrid Intelligent Algorithm with Solving Pre-stack AVO Elastic Parameter Inversion Problem. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-10-3614-9_14
Download citation
DOI: https://doi.org/10.1007/978-981-10-3614-9_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3613-2
Online ISBN: 978-981-10-3614-9
eBook Packages: Computer ScienceComputer Science (R0)