One-Dimensional Inversion of Full Waveforms using a Genetic Algorithm

Tran, Khiem T.; Hiltunen, Dennis R.

doi:10.2113/jeeg17.4.197

Cited by 23 publications

(13 citation statements)

References 23 publications

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“…Esse processo simplifica bastante o problema, diminuindo a complexidade do algoritmo. Tran & Hiltunen (2012) utilizaram um número maior de parâmetros, o que é uma vantagem em relação à precisão, mas uma desvantagem em relação ao tempo computacional. Mesmo utilizando a equação de onda elástica em sua modelagem, tornando--a mais realista, pois incorporam os fenômenos da onda transmitida, ondas--S, a precisão ainda é inferior e o tempo computacional é 46 vezes maior.…”

Section: Resultados E Discussõesunclassified

“…Estes são métodos populacionais em que uma população inicial de pontos é melhorada de uma maneira iterativa. O processo concentra--se na exploração direta do espaço de busca, em que se procura a direção de melhor resultado para minimizar a função custo do problema (Tran, K. T. & Hiltunen, D, R., 2012). As técnicas DFO podem classificar--se como métodos de mínimo local, ou de mínimo global, dependendo da estratégia empregada para se encontrar o ponto ótimo.…”

Section: Problema De Otimizaçãounclassified

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Estratégia Híbrida De Otimização Sem Derivadas Para a Inversão Completa Da Forma Da Onda

Gomes

Neto

Bezerra

et al. 2017

HOLOS

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RESUMONa exploração sísmica, investiga-se as características de subsuperfície usando técnicas de inversão completa, da forma da onda (Full Waveform Inversion -FWI), a qual foi abordada como um problema de otimização não linear. A técnica FWI, tradicionalmente, usa métodos matemáticos baseados em derivadas e, portanto, falha quando a função objetiva é não diferenciável. Ademais, isso acarreta um alto custo computacional e uma precisão limitada a mínimos locais. Portanto, neste trabalho foi adotada uma metodologia sem derivadas, Derivative-Free Optimization (DFO), para encontrar o mínimo global. Neste tipo de abordagem, utiliza-se as técnicas Salto Aleatório (RJT), Busca Aleatória Controlada (CRS) e Simplex Adaptativo de Nelder-Mead (ANMS). Desenvolveu-se um algoritmo, FWI-DFO, que resolve numericamente a equação da onda acústica 2D pelo Método das Diferenças Finitas (FDM) e se utiliza de um método híbrido RJT-CRS-ANMS como técnica de otimização para a inversão sísmica. A estratégia é balancear automaticamente as buscas globais e locais iterativamente pelo CRS e ANMS, respectivamente. Aplicou-se a metodologia em cinco modelos reais de subsuperfície. Os resultados mostraram uma concordância significativa com os modelos reais. O tempo computacional apresentou valores razoáveis e a função objetivo mostrou ser bastante sensível a pequenas alterações nos parâmetros do modelo para os casos aqui analisados. Em síntese, a metodologia, FWI-DFO, empregada mostrou-se bastante promissora na inversão sísmica. DERIVATIVE-FREE OPTIMIZATION HYBRID STRATEGY FOR FULL WAVEFORM INVERSION ABSTRACTIn the seismic exploration, the subsurface characteristics have been investigated using Full Waveform Inversion (FWI) techniques which was approached as a nonlinear optimization problem. The FWI technique traditionally uses mathematical methods based on derivatives and therefore fails when an objective function is non differentiable. In addition, this entails a high computational cost and a precision limited to local minimum. Therefore, in this work, a Derivative-Free Optimization (DFO) methodology was adopted to find the global minimum. In this type of approach, the Random Jump (RJT), Controlled Random Search (CRS) and Adaptive Nelder-Mead Simplex (ANMS) techniques were used. A FWI-DFO algorithm which numerically solves the 2D acoustic wave equation by the Finite Differences Method (FDM) and uses a hybrid method RJT-CRS-ANMS as the optimization technique for the seismic inversion was developed. The strategy is to automatically balance global and local searches iteratively by CRS and ANMS, respectively. The methodology to five real subsurface models was applied. The results showed a significative agreement with the real models. The computational time presented reasonable values and the objective function showed to be very sensitive to small changes in the model parameters for the cases analyzed here. In summary, the FWI-DFO methodology proved to be very promising in the seismic inversion. KEYWORDS:

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Section: Resultados E Discussõesunclassified

Section: Problema De Otimizaçãounclassified

Estratégia Híbrida De Otimização Sem Derivadas Para a Inversão Completa Da Forma Da Onda

Gomes

Neto

Bezerra

et al. 2017

HOLOS

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“…Detailed description is referred to Davis (1987), Goldberg (1989), Buckles and Petry (1992) and Forrest (1993). GA was first used by geophysicists in the early 1990s Gallagher et al, 1991;Smith et al, 1992;Scales et al, 1992) and has been used mainly in the area of seismic inversion Sen and Stoffa, 1992;Sambridge and Drijkoningen, 1992;Gallagher and Sambridge, 1994;Mallick, 1995Mallick, , 1999Boschetti et al, 1995Boschetti et al, , 1996Morgan et al, 2012;Tran and Hiltunen, 2012a;Padhi andMallick, 2013, 2014;Li and Mallick, 2015;Fang and Yang, 2015).…”

Section: Brief Overview Of Conventional Algorithmsmentioning

confidence: 99%

Seismic inversion and uncertainty analysis using a transdimensional Markov chain Monte Carlo method

Zhu

Gibson

2016

SEG Technical Program Expanded Abstracts 2016

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We use a transdimensional inversion algorithm, reversible jump MCMC (rjMCMC), in the seismic waveform inversion of post-stack and prestack data to characterize reservoir properties such as seismic wave velocity, density as well as impedance and then estimate uncertainty. Each seismic trace is inverted independently based on a layered earth model. The model dimensionality is defined as the number of the layers multiplied with the number of model parameters per layer. The rjMCMC is able to infer the number of model parameters from data itself by allowing it to vary in the iterative inversion process, converge to proper parameterization and prevent underparameterization and overparameterization. We also use rjMCMC to enhance uncertainty estimation since it can transdimensionally sample different model spaces of different dimensionalities and can prevent a biased sampling in only one space which may have a different dimensionality than that of the true model space. An ensemble of solutions from difference spaces can statistically reduce the bias for parameter estimation and uncertainty quantification. Inversion uncertainty is comprised of property uncertainty and location uncertainty. Our study revealed that the inversion uncertainty is correlated with the discontinuity of property in such a way that 1) a smaller discontinuity will induce a lower uncertainty in property at the discontinuity but also a higher uncertainty of the location of that discontinuity and 2) a larger discontinuity will induce a higher uncertainty in property at the discontinuity but also a higher ''certainty'' of the location of that discontinuity. Therefore, there is a trade-off between the property uncertainty and the location uncertainty. To our surprise, there is a lot of hidden information in the uncertainty result that we can actually take advantage of due to this trade-off effect. On the basis of our study ii using rjMCMC, we propose to use the inversion uncertainty as a novel attribute in an optimistic way to characterize the magnitude and the location of subsurface discontinuities and reflectors. important it is to estimate the uncertainty, and I used another method for two years but it turned out that method was unsuccessful for solving my research problems because it couldn't answer the question for uncertainty assessment. This is one of the biggest mistakes that I have ever made because I wasn't aware of the importance of my advisor's words and questions. Indeed, research is not always successful and smooth and I did encounter failures, but I didn't give up.

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“…To mitigate the local minima, several researchers made great efforts and achieved considerable improvements Asnaashari et al, 2013;van Leeuwen and Herrmann, 2013). Global optimization algorithms such as simulated annealing (SA) (Sen and Stoffa, 1991), genetic algorithms (GA) (Tran and Hiltunen, 2012) and particle swarm optimization algorithm (PSO) (Zhu et al, 2011) have already been used in full waveform inversion. Global optimization schemes have advantages over local optimization approaches in that the former can utilize the nonlinear of the inversion problem to find the global minima (Shin and Ha, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…Sen and Stoffa (1991) performed M Â N forward calculations in each iteration, where N referred to the number of model parameters and M meant all the possible values that one model parameter could take, with at least 30 iterations, sometimes 100 iterations needed to obtain a high crosscorrelation value between observed data and modeled data, which results in its applicability to only 1-D models with fewer than 10 parameters. Similarly, Tran and Hiltunen (2012) employed genetic algorithms to 1-D waveform inversion with only one source.…”

Section: Introductionmentioning

confidence: 99%

An adaptive subspace trust-region method for frequency-domain seismic full waveform inversion

Zhang

Song

et al. 2015

Computers & Geosciences

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One-Dimensional Inversion of Full Waveforms using a Genetic Algorithm

Cited by 23 publications

References 23 publications

Estratégia Híbrida De Otimização Sem Derivadas Para a Inversão Completa Da Forma Da Onda

Estratégia Híbrida De Otimização Sem Derivadas Para a Inversão Completa Da Forma Da Onda

Seismic inversion and uncertainty analysis using a transdimensional Markov chain Monte Carlo method

An adaptive subspace trust-region method for frequency-domain seismic full waveform inversion

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