PSO: A powerful algorithm to solve geophysical inverse problems

Martínez, Jorge Coque; García–Gonzalo, Esperanza; Fernández-Álvarez, José Paulino; Kuzma, Heidi Anderson; Pérez, C. O. Menéndez

doi:10.1016/j.jappgeo.2010.02.001

Cited by 171 publications

(87 citation statements)

References 24 publications

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“…where ω is a real constant called inertia weight, whose role is to increase exploration and avoid elitism (Fernández Martínez et al, 2010a). The inertia weight is either kept constant or decreased linearly as the search progresses (Schutte and Groenwold, 2005).…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…However, selection of these parameters depends on the corresponding physical problems we need to solve. Fernández Martínez et al (2010a) have shown that there are no magic PSO tuning points that exist. There are four different regions that oscillation of the parameters is different in each region.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…There are four different regions that oscillation of the parameters is different in each region. According to Fernández Martínez et al (2010a), the best parameters may be picked up close to (or in the neighborhood of) the secondorder stability region. These parameters can also be used for controlling exploitation or exploration.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…The different PSO versions have been tested for different wellknown benchmark functions showing a degree of ill-posedness similar to that found in many geophysical inverse problems having their global minimum located on a very narrow flat valley and/or surrounded by multiple local minima (Clerc and Kennedy, 2002;Eberhart and Shi, 2000;, 2009, 2011, 2010aMontes de Oca et al, 2006;Shelokar et al, 2007;Trelea, 2003). In geophysics, there have recently been emerged several significant PSO applications.…”

Section: Introductionmentioning

confidence: 96%

See 3 more Smart Citations

Application of particle swarm optimization to interpret Rayleigh wave dispersion curves

Xiao

Tang

et al. 2012

Journal of Applied Geophysics

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

See 2 more Smart Citations

Application of particle swarm optimization to interpret Rayleigh wave dispersion curves

Xiao

Tang

et al. 2012

Journal of Applied Geophysics

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“…It has several advantages including fast convergence, strong capability of global optimization and simple parameter adjustment, and has been successfully applied to many fields [6]. In recent years it has also been introduced into the geophysical inversion [7][8][9][10][11]. Although the disadvantage, such as local optimum, of geophysical inversion based on PSO has been improved by employing the adaptive inertial weight, it still has the problem of heavy computation time when the forward modeling becomes more complex [2].…”

Section: Introductionmentioning

confidence: 99%

Improved Parallel PSO Inversion for the MT Sounding Data

Xiong¹,

Liu²

2017

IJEACS

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Abstract-The magnetotelluric (MT) method has become more widely used in hydrocarbon exploration. The inversion of MT data, which can determine the electrical structure of subsurface, is a nonlinear and multimodal optimization problem. Particle swarm optimization (PSO) algorithm is a good solver for this geophysical inversion problem, whereas it has a shortage of heavy computation time. An improved parallel adaptive PSO inversion algorithm for MT data is proposed in order to decrease the computation time. The performance of the proposed algorithm was evaluated on the Dawn 4000L supercomputer using the synthetic MT data of 1D layered geo-electrical models of three and four layers. The numeric results show that the proposed algorithm can obtain the as good solution as the serious PSO inversion algorithm, and can reduce the computation time obviously when more computing nodes been employed. This result indicates that proposed improved parallel inversion algorithm can deal with the computation time problem and provide theory and technology support the MT data non-linear inversion based on PSO.

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Stratified Crustal Anisotropy From Receiver Function and Its Particle Swarm Inversion

Shao-Hua

Liu

Chen

et al. 2017

Chinese J of Geophysics

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Crustal anisotropy at multiple depths is essential for studying the vertical variation of crustal deformation. Due to the complexity of the crust, mapping the crustal anisotropy at different depth from teleseismic receiver function (RF) is an ongoing subject that requires further studies. Based on the previous works, this paper goes further to discuss the RF wavefield pattern of stratified crustal anisotropy by means of the generalized reflection and transmission coefficients method, which provides us a new theoretical basis for understanding the RF data with stratified anisotropic crustal models. In addition, we develop a global inversion method for extracting stratified anisotropic models based on the particle swarm algorithm. The test results from both numerical and real data show that, our method can correctly recover the depth-dependent anisotropic parameters from the RF data and distinguish different anisotropic layers of the crust, as long as isotropic velocity model is well determined. Noise suppression in data processing via curvelet transform is useful for the analysis of crustal anisotropy.

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PSO: A powerful algorithm to solve geophysical inverse problems

Cited by 171 publications

References 24 publications

Application of particle swarm optimization to interpret Rayleigh wave dispersion curves

Application of particle swarm optimization to interpret Rayleigh wave dispersion curves

Improved Parallel PSO Inversion for the MT Sounding Data

Stratified Crustal Anisotropy From Receiver Function and Its Particle Swarm Inversion

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