Traveltime and constrained AVO inversion using FDR PSO

Agarwal, Akhil; Sain, Kalachand; Shalivahan, Shalivahan

doi:10.1190/segam2016-13959236.1

Cited by 11 publications

(3 citation statements)

References 8 publications

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“…In the mid-1980s, nonlinear global intelligent optimisation inversion techniques began to receive the attention of researchers in the geophysical field. New ideas and methods in other areas have been continuously introduced since that time and have many successful applications [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36].…”

Section: Pre-stack Avo Elastic Parameter Inversion Problemmentioning

confidence: 99%

An Improved Pigeon-Inspired Optimisation Algorithm and Its Application in Parameter Inversion

Liu

Yan

2019

Symmetry

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Pre-stack amplitude variation with offset (AVO) elastic parameter inversion is a nonlinear, multi-solution optimisation problem. The techniques that combine intelligent optimisation algorithms and AVO inversion provide an effective identification method for oil and gas exploration. However, these techniques also have shortcomings in solving nonlinear geophysical inversion problems. The evolutionary optimisation algorithms have recognised disadvantages, such as the tendency of convergence to a local optimum resulting in poor local optimisation performance when dealing with multimodal search problems, decreasing diversity and leading to the prematurity of the population as the number of evolutionary iterations increases. The pre-stack AVO elastic parameter inversion is nonlinear with slow convergence, while the pigeon-inspired optimisation (PIO) algorithm has the advantage of fast convergence and better optimisation characteristics. In this study, based on the characteristics of the pre-stack AVO elastic parameter inversion problem, an improved PIO algorithm (IPIO) is proposed by introducing the particle swarm optimisation (PSO) algorithm, an inverse factor, and a Gaussian factor into the PIO algorithm. The experimental comparisons indicate that the proposed IPIO algorithm can achieve better inversion results.

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Section: Pre-stack Avo Elastic Parameter Inversion Problemmentioning

confidence: 99%

An Improved Pigeon-Inspired Optimisation Algorithm and Its Application in Parameter Inversion

Liu

Yan

2019

Symmetry

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“…Based on the neural network theory, Mogensen and Link performed an AVO inversion for calculating the velocity of longitudinal and transverse waves along with Poisson's ratio, and the results show that the inversion error stays below 10% . Agarwal et al proposed a novel AVO inversion method based on PSO and obtained the good experimental results . In 2012, Sun et al proposed a nonlinear prestack inversion method based PSO for reservoir prediction, and compared with the traditional methods .…”

Section: Introductionmentioning

confidence: 99%

“…11 Agarwal et al proposed a novel AVO inversion method based on PSO and obtained the good experimental results. 12 In 2012, Sun et al proposed a nonlinear prestack inversion method based PSO for reservoir prediction, and compared with the traditional methods. 13 Sun and Liu achieved good results using a chaotic quantum PSO algorithm to solve the prestack AVO elastic parameter inversion.…”

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confidence: 99%

An improved particle swarm optimization algorithm for AVO elastic parameter inversion problem

Zhu

Yan

Gong

2018

Concurrency and Computation

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Summary The elastic parameter inversion technique for prestack seismic data, which combines the intelligent optimization algorithms with Amplitude Variation with Offset (AVO) technology, is an effective method for oil and gas exploration. However, when certain biological‐evolution–based optimization algorithms, eg, genetic algorithms, are used to solve this problem, the computation exhibits fast convergence and a strong tendency to be trapped to a local optimum, thereby leading to unsatisfactory inversion results. To address this issue, this paper proposes a swarm‐intelligence‐based method‐Particle Swarm Optimization (PSO) algorithm to handle the elastic parameter inversion problem. Based on the Aki‐Richards approximation to the Zoeppritz equations, the improved PSO algorithm adopts a special initialization strategy, which can enhance the smoothness of the initialization parametric curves. Extensive experimental research confirms the superiority of the proposed algorithm. Specifically, the improved PSO algorithm is able to not only markedly enhance inversion precision but also render remarkably high correlation coefficients associated with the elastic parameters.

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