Nonlinear Geophysical Inversion Based on ACO with Hybrid Techniques

Yuan, Sanyi; Tian, Nan; Chen, Ye; Liu, Huafeng; Liu, Zhipeng

doi:10.1109/icnc.2008.528

Cited by 7 publications

(2 citation statements)

References 6 publications

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“…In recent years, swarm intelligence optimization algorithms have been gradually introduced into the field of geophysics [19], [11]. Yuan et al [11] state that swarm intelligence techniques have advantages, such as the absence of centralized control and an overall model.…”

Section: Revisionmentioning

confidence: 99%

Fast Seismic Inversion Methods Using Ant Colony Optimization Algorithm

Conti

Roisenberg

Neto

et al. 2013

IEEE Geosci. Remote Sensing Lett.

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This letter presents ACO R -V , a new computationally efficient ant-colony-optimization-based algorithm, tailored for continuous-domain problems. The ACO R -V algorithm is well suited for application in seismic inversion problems, owing to its intrinsic features, such as heuristics in generating the initial solution population and its facility to deal with multiobjective optimization problems. Here, we show how the ACO R -V algorithm can be applied in two methodologies to obtain 3-D impedance maps from poststack seismic amplitude data. The first methodology pertains to the traditional method of forward convolution of a reflectivity model with the estimated wavelet, where ACO R -V is used to guess the appropriate wavelet as the reflectivity model. In the second methodology, we propose an even faster inversion algorithm based on inverse filter optimization, where ACO R -V optimizes the inverse filter that is deconvolved with the seismic traces and results in a reflectivity model similar to that found in well logs. This modeled inverse filter is then deconvolved with the entire 3-D seismic volume. In experiments, both the methodologies are applied to a synthetic 3-D seismic volume. The results validate their feasibility and the suitability of ACO R -V as an optimization algorithm. The results also show that the second methodology has the advantages of a much higher convergence speed and effectiveness as a seismic inversion tool.

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Section: Revisionmentioning

confidence: 99%

Fast Seismic Inversion Methods Using Ant Colony Optimization Algorithm

Conti

Roisenberg

Neto

et al. 2013

IEEE Geosci. Remote Sensing Lett.

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“…This model uses the posterior probability density function (PDF) and the prior PDF to represent the seismic data and the prior knowledge of rock property and combines the posterior and prior PDF and builds a maximum likelihood estimation problem to represent the AI inversion problem. To solve this problem, many methods are proposed, such as the simulated annealing [17][18][19], the genetic algorithm [20][21][22], the ant-colony algorithms [23][24][25], and the particle swarm algorithms [26][27][28]. However, these methods use the data of impedance and ignore the distribution of impedance [29].…”

Section: Introductionmentioning

confidence: 99%

Acoustic Impedance Inversion Using Gaussian Metropolis–Hastings Sampling with Data Driving

Chen

Shu

et al. 2019

Energies

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The Markov chain Monte Carlo (MCMC) method based on Metropolis–Hastings (MH) sampling is a popular approach in solving seismic acoustic impedance (AI) inversion problem, as it can improve the inversion resolution by statistical prior information. However, the sampling function of the traditional MH sampling is a fixed parameter distribution. The parameter ignores the statistical information of AI that expands sampling range and reduces the inversion efficiency and resolution. To reduce the sampling range and improve the efficiency, we apply the statistical information of AI to the sampling function and build a Gaussian MH sampling with data driving (GMHDD) approach to the sampling function. Moreover, combining GMHDD and MCMC, we propose a novel Bayesian AI inversion method based on GMHDD. Finally, we use the Marmousi2 data and field data to test the proposed method based on GMHDD and other methods based on traditional MH. The results reveal that the proposed method can improve the efficiency and resolution of impedance inversion than other methods.

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