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Volume 68, Issue 2
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

The main goal of this study is to assess the potential of evolutionary algorithms to solve highly non‐linear and multi‐modal tomography problems (such as first arrival traveltime tomography) and their abilities to estimate reliable uncertainties. Classical tomography methods apply derivative‐based optimization algorithms that require the user to determine the value of several parameters (such as regularization level and initial model) prior to the inversion as they strongly affect the final inverted model. In addition, derivative‐based methods only perform a local search dependent on the chosen starting model. Global optimization methods based on Markov chain Monte Carlo that thoroughly sample the model parameter space are theoretically insensitive to the initial model but turn out to be computationally expensive. Evolutionary algorithms are population‐based global optimization methods and are thus intrinsically parallel, allowing these algorithms to fully handle available computer resources. We apply three evolutionary algorithms to solve a refraction traveltime tomography problem, namely the differential evolution, the competitive particle swarm optimization and the covariance matrix adaptation–evolution strategy. We apply these methodologies on a smoothed version of the Marmousi velocity model and compare their performances in terms of optimization and estimates of uncertainty. By performing scalability and statistical analysis over the results obtained with several runs, we assess the benefits and shortcomings of each algorithm.

© 2020 European Association of Geoscientists & Engineers © 2019 European Association of Geoscientists & Engineers
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2019-09-18
2024-04-20

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References

  1. AkimotoY., AugerA. and HansenN.2014. Comparison‐based natural gradient optimization in high dimension. In: Proceedings of the 2014 Conference on Genetic and Evolutionary Computation (Gecco '14), pp. 373–380. New York: ACM Press.
     [Google Scholar]
  2. AmdahlG.M.1967. Validity of the single processor approach to achieving large scale computing capabilities. In: Proceedings of the April 18–20, 1967, Spring Joint Computer Conference (Spring), p. 483. New York: ACM Press.
     [Google Scholar]
  3. AngelineP.J.1998. Evolutionary optimization versus particle swarm optimization: philosophy and performance differences. Lecture Notes in Computer Science: Evolutionary Programming VII1447, 601–610.
     [Google Scholar]
  4. BelhadjJ., RomaryT., GesretA., NobleM. and FigliuzziB.2018. New parameterizations for Bayesian seismic tomography. Inverse Problems34, 065007.
     [Google Scholar]
  5. BillingsS.D.1994. Simulated annealing for earthquake location. Geophysical Journal International118, 680–692.
     [Google Scholar]
  6. BodinT., SalmonM., KennettB.L.N. and SambridgeM.2012. Probabilistic surface reconstruction from multiple data sets: an example for the Australian Moho. Journal of Geophysical Research: Solid Earth117, 1–13.
     [Google Scholar]
  7. BodinT. and SambridgeM.2009. Seismic tomography with the reversible jump algorithm. Geophysical Journal International178, 1411–1436.
     [Google Scholar]
  8. de BoorC.1972. On calculating with B‐splines. Journal of Approximation Theory6, 50–62.
     [Google Scholar]
  9. BoschettiF., DentithM.C. and ListR.D.1996. Inversion of seismic refraction data using genetic algorithms. Geophysics61, 1715–1727.
     [Google Scholar]
  10. BotteroA., GesretA., RomaryT., NobleM., and MaisonsC.2016. Stochastic seismic tomography by interacting Markov chains. Geophysical Journal International207, 374–392.
     [Google Scholar]
  11. BunksC., SaleckF.M., ZaleskiS. and ChaventG.1995. Multiscale seismic waveform inversion. Geophysics60, 1457–1473.
     [Google Scholar]
  12. ChenS., MontgomeryJ. and Bolufé‐RöhlerA.2015. Measuring the curse of dimensionality and its effects on particle swarm optimization and differential evolution. Applied Intelligence42, 514–526.
     [Google Scholar]
  13. EberhartR. and ShiY.2000. Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512), Vol. 1, pp. 84–88. Piscataway, NJ: IEEE Press.
     [Google Scholar]
  14. EngelbrechtA.2012. Particle swarm optimization: velocity initialization. In: 2012 Ieee Congress on Evolutionary Computation, pp. 1–8. Piscataway, NJ: IEEE Press.
     [Google Scholar]
  15. Fernández MartínezJ.L.MukerjiT., García GonzaloE. and SumanA.2012. Reservoir characterization and inversion uncertainty via a family of particle swarm optimizers. Geophysics77, M1–M16.
     [Google Scholar]
  16. GoudieR.J.B., TurnerR.M., De AngelisD. and ThomasA.2017. MultiBUGS: a parallel implementation of the BUGS modelling framework for faster Bayesian inference. 1–19. arXiv:1704.03216 [stat.CO].
  17. HansenN.2016. The CMA evolution strategy: A tutorial. 102, 1–34. arXiv:1604.00772.
  18. HansenN., MüllerS.D. and KoumoutsakosP.2003. Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA‐ES). Evolutionary Computation11, 1–18.
     [Google Scholar]
  19. ImprotaL., ZolloA., HerreroA., FrattiniR., VirieuxJ. and Dell'AversanaP.2002. Seismic imaging of complex structures by non‐linear traveltime inversion of dense wide‐angle data: application to a thrust belt. Geophysical Journal International151, 264–278.
     [Google Scholar]
  20. IwanM., AkmeliawatiR., FaisalT. and Al‐AssadiH.M.A.A.2012. Performance comparison of differential evolution and particle swarm optimization in constrained optimization. Procedia Engineering41, 1323–1328.
     [Google Scholar]
  21. KennedyJ. and EberhartR.1995. Particle swarm optimization. In: Proceedings of International Conference on Neural Networks, ICNN'95, Vol 4, pp. 1942–1948. Piscataway, NJ: IEEE Press.
     [Google Scholar]
  22. LuuK., NobleM., GesretA., BelayouniN. and RouxP.‐F. 2018. A parallel competitive particle swarm optimization for non‐linear first arrival traveltime tomography and uncertainty quantification. Computers & Geosciences113, 81–93.
     [Google Scholar]
  23. MenkeW.2012. Geophysical Data Analysis: Discrete Inverse Theory. Elsevier/Academic Press, 293.
     [Google Scholar]
  24. MohamedL., CalderheadB., FilipponeM., and ChristieM., GirolamiM.2012. Population MCMC methods for history matching and uncertainty quantification. Computational Geosciences16, 423–436.
     [Google Scholar]
  25. MohamedL., ChristieM.A., DemyanovV., RobertE. and KachumaD.2010. Application of particle swarms for history matching in the Brugge reservoir. In: SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers.
     [Google Scholar]
  26. MohamedL., ChristieM. and DemyanovV.2010. Comparison of stochastic sampling algorithms for uncertainty quantification. SPE Journal15, 31–38.
     [Google Scholar]
  27. NeiswangerW., WangC. and XingE.2013. Asymptotically Exact, Embarrassingly Parallel MCMC. 1–16. arXiv:1311.4780.
  28. NobleM., GesretA. and BelayouniN.2014. Accurate 3‐D finite difference computation of traveltimes in strongly heterogeneous media. Geophysical Journal International199, 1572–1585.
     [Google Scholar]
  29. NobleM., ThierryP., TaillandierC. and CalandraH.2010. High‐performance 3D first‐arrival traveltime tomography. The Leading Edge29, 86–93.
     [Google Scholar]
  30. RechenbergI.1978. Evolutionsstrategien. In: Simulationsmethoden in der Medizin und Biologie, pp. 83–114. Springer, Berlin, Heidelberg.
     [Google Scholar]
  31. RumpfM. and TronickeJ.2015. Assessing uncertainty in refraction seismic traveltime inversion using a global inversion strategy. Geophysical Prospecting63, 1188–1197.
     [Google Scholar]
  32. RůžekB. and KvasničkaM.2001. Differential evolution algorithm in the earthquake hypocenter location. Pure and Applied Geophysics158, 667–693.
     [Google Scholar]
  33. RybergT. and HaberlandC.2018. Bayesian inversion of refraction seismic traveltime data. Geophysical Journal International212, 1645–1656.
     [Google Scholar]
  34. SambridgeM.2014. A parallel tempering algorithm for probabilistic sampling and multimodal optimization. Geophysical Journal International196, 357–374.
     [Google Scholar]
  35. SambridgeM. and GallagherK.1993. Earthquake hypocenter location using genetic algorithms. Bulletin of the Seismological Society of America83, 1467–1491.
     [Google Scholar]
  36. SenM.K. and StoffaP.L.1996. Bayesian inference, Gibbs' sampler and uncertainty estimation in geophysical inversion. Geophysical Prospecting44, 313–350.
     [Google Scholar]
  37. SoccoL.V. and BoieroD.2008. Improved Monte Carlo inversion of surface wave data. Geophysical Prospecting56, 357–371.
     [Google Scholar]
  38. SongX., TangL., LvX., FangH. and GuH.2012. Application of particle swarm optimization to interpret Rayleigh wave dispersion curves. Journal of Applied Geophysics84, 1–13.
     [Google Scholar]
  39. StornR.2017. Real‐world applications in the communications industry—when do we resort to differential evolution? In: 2017 IEEE Congress on Evolutionary Computation (CEC), 765–772. Piscataway, NJ: IEEE Press.
     [Google Scholar]
  40. StornR. and PriceK.1997. Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization11, 341–359.
     [Google Scholar]
  41. TaillandierC., NobleM., ChaurisH. and CalandraH.2009. First‐arrival traveltime tomography based on the adjoint‐state method. Geophysics, 74, 1ND–Z107.
     [Google Scholar]
  42. TarantolaA. and ValetteB.1982. Inverse problems = quest for information. Journal of Geophysics50, 159–170.
     [Google Scholar]
  43. TronickeJ., PaascheH. and BöigerU.2012. Crosshole traveltime tomography using particle swarm optimization: a near‐surface field example. Geophysics77, R19–R32.
     [Google Scholar]
  44. Van Den BerghF. and EngelbrechtA.P.2006. A study of particle swarm optimization particle trajectories. Information Sciences176, 937–971.
     [Google Scholar]
  45. VersteegR.1994. The Marmousi experience: Velocity model determination on a synthetic complex data set. The Leading Edge13, 927–936.
     [Google Scholar]
  46. WhiteD.J.1989. Two‐dimensional seismic refraction tomography. Geophysical Journal International97, 223–245.
     [Google Scholar]
  47. WilkenD. and RabbelW.2012. On the application of particle swarm optimization strategies on Scholte‐wave inversion. Geophysical Journal International190, 580–594.
     [Google Scholar]
  48. ZeltC.A. and BartonP.J.1998. Three‐dimensional seismic refraction tomography: a comparison of two methods applied to data from the Faeroe Basin. Journal of Geophysical Research: Solid Earth103, 7187–7210.
     [Google Scholar]
  49. ZhangJ. and ToksözM.N.1998. Nonlinear refraction traveltime tomography. Geophysics63, 1726–1737.
     [Google Scholar]
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Towards large‐scale stochastic refraction tomography: a comparison of three evolutionary algorithms
Geophysical Prospecting 68, 536 (2020); https://doi.org/10.1111/1365-2478.12866
/content/journals/10.1111/1365-2478.12866
/content/journals/10.1111/1365-2478.12866
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  • Article Type: Research Article
Keyword(s): Imaging; Inverse problem; Parameter estimation; Seismics; Tomography

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