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Abstract

Summary

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 traveltime tomography) and their ability 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.

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/content/papers/10.3997/2214-4609.201901994
2019-06-03
2024-04-24

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References

  1. BotteroA, GesretA, RomaryT, NobleM and MaisonsC.
    , (2016). Stochastic seismic tomography by interacting Markov chains, Geophysical Journal International, 207, 374–92.
     [Google Scholar]
  2. HansenN, MüllerSD
    , Koumoutsakos P. (2003). Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES), Evolutionary computation, 11,1–18.
     [Google Scholar]
  3. KennedyJ, EberhartR.
    (1995). Particle swarm optimization. In.Proceedings of Icnn'95 - International Conference on Neural Networks. Vol 4. IEEE, 1995, 1942–8.
     [Google Scholar]
  4. LuuK, NobleM, GesretA, BelayouniN., RouxP. F.
    (2018) A parallel competitive Particle Swarm Optimization for non-linear first arrival traveltime tomography and uncertainty quantification, Computers & Geosciences, 113, 81–93.
     [Google Scholar]
  5. NobleM, GesretA, BelayouniN.
    (2014). Accurate 3-D finite difference computation of traveltimes in strongly heterogeneous media, Geophysical Journal International, 199, 1572–85.
     [Google Scholar]
  6. SenMK
    , Stoffa PL. (1996) Bayesian inference, Gibbs' sampler and uncertainty estimation in geophysical inversion, Geophysical Prospecting, 44, 313–50.
     [Google Scholar]
  7. StornR.
    (2017). Real-world applications in the communications industry – when do we resort to Differential Evolution? In. Ieee Congress on Evolutionary Computation (Cec)}. IEEE, 2017, 765–72.
     [Google Scholar]
  8. StornR, PriceK.
    (1997) Differential Evolution -- A Simple and Efficient Heuristic for global Optimization over Continuous Spaces, Journal of Global Optimization, 11, 341–59.
     [Google Scholar]
  9. TaillandierC, NobleM, ChaurisH, CalandraH
    (2009). First-arrival traveltime tomography based on the adjoint-state method, Geophysics, doi:10.1190/1.3250266.
     https://doi.org/10.1190/1.3250266 [Google Scholar]
  10. WhiteDJ.
    (1989). Two-Dimensional Seismic Refraction Tomography, Geophysical Journal International, 97, 223–45.
     [Google Scholar]
  11. ZhangJ, ToksözMN.
    (1989). Nonlinear refraction traveltime tomography, Geophysics, 63,1726–37.
     [Google Scholar]
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Evolutionary Algorithms: from optimization to uncertainties?
Conference Proceedings 2019, 1 (2019); https://doi.org/10.3997/2214-4609.201901994
/content/papers/10.3997/2214-4609.201901994
/content/papers/10.3997/2214-4609.201901994
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