Non‐linear prestack seismic inversion with global optimization using an edge‐preserving smoothing filter

Yan, Zhe; Gu, Hanming

doi:10.1111/1365-2478.12001

Cited by 21 publications

(5 citation statements)

References 16 publications

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“…In the PSO, the models, called particles, are navigated in the model space by following the current optimal model as well as their individual best location in the moving history. This method is relatively newer than the above methods, and it is now used to invert geophysical data and characterize reservoir (Shaw and Srivastava, 2007;Fernández-Martínez et al, 2008Zhe and Gu, 2013).…”

Section: Brief Overview Of Conventional Algorithmsmentioning

confidence: 99%

“…It has also been implemented by a variety of stochastic inversion algorithms, including Simulated Annealing (SA) Ma, 2001bMa, , 2002Varela et al, 2006;Srivastava and Sen, 2010), Markov chain Monte Carlo method (MCMC) (Eidsvik et al, 2004;van der Burg et al, 2009;Chen and Glinsky, 2014), Genetic Algorithm (GA) (Mallick, 1995(Mallick, , 1999Padhi andMallick, 2013, 2014;Li and Mallick, 2015), Particle Swarm Optimization (PSO) (Zhe and Gu, 2013), Neural Network (NN) (Mohamed et al, 2015). Some of these works are waveform-based inversion and some are amplitude-based.…”

Section: Prestack Seismic Inversion Can Be Performed With Deterministmentioning

confidence: 99%

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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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Section: Brief Overview Of Conventional Algorithmsmentioning

confidence: 99%

Section: Prestack Seismic Inversion Can Be Performed With Deterministmentioning

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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“…The most classic regularization method is the Tikhonov (1963) regularization. This method has been widely used in seismic inversion (Zhang et al, 2007;DeFigueiredo et al, 2017). To enhance the anti-noise ability and stability of the inversion, a second-order difference matrix is added to the traditional objective function to obtain a constructed L2 norm constraint (Li et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Prestack AVO Inversion of Exact Zoeppritz Equation Using Adaptive Edge Preserving Filter

Cui

et al. 2023

Pure Appl. Geophys.

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Since traditional pre-stack inversion via amplitude variation with offset (AVO) uses an approximate formula, it is insufficiently accurate at large angles. To overcome this shortcoming, we used the exact Zoeppritz equation for nonlinear AVO inversion. A secondorder difference matrix is added to suppress the disturbance of the inversion in the case of noise.We used an adaptive edge preservation (Ad-EPS) filter to process the iterative model. An Ad-EPS filter can find the optimal window size for each filtered sample so that the formation boundary is sharper and more accurate in the inversion. For nonlinear optimization problems, we used the Levenberg-Marquardt method. The LM method adds a damping factor to the iterative equation to reduce the ill-posedness of the inversion and can add a secondorder-difference L2-norm-regularization term directly to the algorithm to make the inversion data closer to the model data. The LM method combines the exact Zoeppritz equation, L2 regularization constraints, and Ad-EPS filter into a nonlinear AVO inversion method called EZL2AEPS. The EZL2AEPS method is more accurate than the generally accurate Zoeppritz pre-stack inversion (EZPI) with respect to details such as formation boundaries and overall disturbances. The EZL2AEPS method can also suppress noise to a certain extent, reduce the effect of noise disturbance on the inversion, and produce blocky inversion results. Actual data show that the EZL2AEPS method achieves better inversion results and is a very advantageous AVO inversion method.

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“…Other global optimisation methods have been proposed and applied, e.g., simulated annealing (Mosegaard and Vestergaard ; Ma ), particle swarm optimisation (Martínez et al . ; Zhe and Hanming ), and the artificial bee colony. Recently, Xue et al .…”

Section: Introductionmentioning

confidence: 99%

“…However, this method involves forward modelling at each random attempt of the model parameters; it is very time consuming when applied to high-dimensional problems. Other global optimisation methods have been proposed and applied, e.g., simulated annealing (Mosegaard and Vestergaard 1991;Ma 2002), particle swarm optimisation (Martínez et al 2010;Zhe and Hanming 2013), and the artificial bee colony. Recently, Xue et al (2017) have proposed a self-adaptive bee colony algorithm based on global best candidate to achieve a better balance between exploration and exploitation.…”

Section: Introductionmentioning

confidence: 99%

Differential evolution with subpopulations for high‐dimensional seismic inversion

Pan

Liang

Gao

et al. 2018

Geophysical Prospecting

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Seismic inversion has drawn the attention of researchers due to its capability of building an accurate earth model. Such a model will need to be discretised finely, and the dimensions of the inversion problem will be very high. In this paper, we propose an efficient differential evolution algorithm and apply it to high‐dimensional seismic inversion. Our method takes into account the differences among individuals, which are disregarded in conventional differential evolution methods, resulting to a better balance between exploration and exploitation. We divide the entire population into three subpopulations and propose a novel mutation strategy with two phases. Furthermore, we optimise the crossover operator by applying the components having the best objective function values into the crossover operator. We embed this strategy into a cooperative coevolutionary differential evolution and propose a new differential evolution algorithm referred to as a differential evolution with subpopulations. Then, we apply our scheme to both synthetic and field data; the results of high‐dimensional seismic inversion have shown that the proposed differential evolution with subpopulations achieves faster convergence and a higher‐quality solution for seismic inversion.

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Non‐linear prestack seismic inversion with global optimization using an edge‐preserving smoothing filter

Cited by 21 publications

References 16 publications

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

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

Prestack AVO Inversion of Exact Zoeppritz Equation Using Adaptive Edge Preserving Filter

Differential evolution with subpopulations for high‐dimensional seismic inversion

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