Abstract
By simulating how our human brain solves complex and conceptual problems, cognitive systems have been successfully applied in a wide range of applications. In this paper, a cognitive modelling based inversion method, the direction guided differential evolution with cooperative coevolutionary mutation operator (DG-DECCM) algorithm, is proposed to trace the ray path of the seismic waves. The DE algorithm seeks optimal solutions by simulating the natural species evolution processes and makes the individuals become optimal. Classical ray tracing methods were time consuming and inefficiency. The proposed algorithm is suitable for the high and super high dimensional separable model space. It treats the emergent angles of the reflection points as genes of an individual. We introduce a sign function to guide the direction of the mutation and propose two kinds of stopping criteria for effective iteration to speed up the computation. For the complex velocity model, the local optimization methods based on gradient are time consuming to converge or may converge to local minimum but not the optimal value. The proposed global DE algorithm, however, will obtain a global optimum solution more efficiently and has higher convergence rate.
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References
Huang, J.L., Li, Y., Wu, R.: The wave-front ray tracing method for image reconstruction. Chin. J. Geophys. 35, 223–232 (1992)
Moser, T.J.: Shortest path calculation of seismic ray. Geophysics 56(1), 59–67 (1991)
Schneider, W.A., Ranzinger, K.A., Balch, A.H., Kruse, C.: A dynamic programming approach to first arrival traveltime computation in media with arbitrarily distributed velocities. Geophysics 57(1), 39–50 (1992)
Zhao, L.F.: Study on crosswell seismic tomography combining velocity and attenuation. Ph.D. Thesis, Chengdu University of Technology (2002)
Gao, E.G., Xu, G.M.: A new kind of step by step iterative ray-tracing method. Chin. J. Geophys. 39, 302–308 (1996)
Dwornik, M., Pieta, A.: Efficient algorithm for 3D ray tracing in 3D anisotropic medium. In: EAGE Conference, p. 138 (2009)
Virieux, J., Operto, S.: An overview of full-waveform inversion in exploration geophysics. Geophysics 74, WCC1–WCC26 (2009)
Price, K.V.: Differential evolution: a fast and simple numerical optimizer. In: Fuzzy Information Processing Society. NAFIPS, Biennial Conference of the North American, pp. 524–527 (1996)
Corana, A., Marchesi, M., et al.: Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithm Corrigenda for this article is available here. ACM Trans. Math. Soft. (TOMS) 13, 262–280 (1987)
Press, F.: Earth models obtained by Monte Carlo inversion. J. Geophys. Res. 73, 5223–5234 (1968)
Pan, Z., Wu, J., Gao, Z., Gao, J.: Adaptive differential evolution by adjusting subcomponent crossover rate for high-dimensional waveform inversion. IEEE Geosci. Remote Sens. Lett. 12, 1327–1331 (2015)
Wang, C., Gao, J.: A new differential evolution algorithm with cooperative coevolutionary selection operator for waveform inversion. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 688–690 (2010)
Wang, C., Gao, J.: High-dimensional waveform inversion with cooperative coevolutionary differential evolution algorithm. IEEE Geosci. Remote Sens. Lett. 9, 297–301 (2012)
Chandra, R., Ong, Y.S., Goh, C.K.: Co-evolutionary multi-task learning with predictive recurrence for multi-step chaotic time series prediction. Neurocomputing 243, 21–34 (2017)
Gao, Z., Pan, Z., Gao, J.: A new highly efficient differential evolution scheme and its application to waveform inversion. IEEE Geosci. Remote Sens. Soc. 11, 1702–1706 (2014)
Gao, Z., Pan, Z., Gao, J.: Multimutation differential evolution algorithm and its application to seismic inversion. IEEE Trans. Geosci. Remote Sens. 54(6), 3626 (2016)
Cui, X.F., Gao, J.H., Zhang, B., Wang, Z.: Poststack impedance inversion using improved particle swarm optimization. SEG Technical Program Expanded Abstracts, pp. 3809–3813 (2016)
Mahdavi, S., Shiri, M.E., Rahnamayan, S.: Metaheuristics in large-scale global continues optimization: a survey. Inf. Sci. 295, 407–428 (2015)
Govindan, R., Kumar, R., Basu, S., Sarkar, A.: Altimeter-derived ocean wave period using genetic algorithm. IEEE Geosci. Remote Sens. Lett. 8, 354–358 (2011)
Gomes, J., Mariano, P., Lyhne, A.: Novelty-driven cooperative coevolution. Evol. Comput. 25, 275–307 (2017)
Zhao, J., Gao, J.H., Wang, D.X., Zhang M.L.: Estimation of quality factor Q from pre-stack CMP records using EPIFVO analysis. In: SEG and 81st Annual Meeting, pp. 1835–1839 (2011)
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We thank National Natural Science Foundation of China (41604113, E070101) and National Nature Science Foundation Project of International Cooperation (41711530128) for their support.
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Zhao, J., Ren, J., Wang, C., Li, K., Zhao, Y. (2018). Direction Guided Cooperative Coevolutionary Differential Evolution Algorithm for Cognitive Modelling of Ray Tracing in Separable High Dimensional Space. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2018. Lecture Notes in Computer Science(), vol 10989. Springer, Cham. https://doi.org/10.1007/978-3-030-00563-4_17
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