Particle swarm optimization and its application to seismic inversion of igneous rocks

Yang, Haijun; Xu, Yong; Peng, Gang; Yu, Guiping; Meng, Chen; Wensheng, Duan; Zhu, Yongfa; Cui, Yongfu; Wang, Xingjun

doi:10.1016/j.ijmst.2017.01.019

Cited by 25 publications

(5 citation statements)

References 4 publications

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“…PSO is quite recent in the framework of geophysical data inversion (Shaw and Srivastava, 2007;Yuan et al, 2009) and is not yet as widely as the other global optimization methods mentioned previously. However, it was successfully applied to surface-wave analysis (Song et al, 2012;Wilken and Rabbel, 2012), traveltime tomography (Tronicke et al, 2012;Luu et al, 2016), seismic refraction (Poormirzaee et al, 2014), seismic wave impedance inversion in igneous rock (Yang et al, 2017) and multifrequency GPR inversion (Salucci et al, 2017). Furthermore, Song et al (2012) have shown, in a comparative analysis, that PSO outperforms genetic algorithm and Monte-Carlo methods in terms of reliability and computational efforts.…”

Section: D Inversion Using Particle Swarm Optimizationmentioning

confidence: 99%

Assessment of Physical Properties of a Sea Dike Using Multichannel Analysis of Surface Waves and 3D Forward Modeling

Pageot¹,

Feuvre²,

Leparoux³

et al. 2017

Proceedings

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Seismic surface waves analysis methods have been widely developed and tested in the context of subsurface characterization and have demonstrated their effectiveness for sounding and monitoring purposes. Given their efficiency, surface waves methods have been used in a variety of contexts, including civil engineering applications. However, at this particular scale, many structures exhibit 3D geometries which drastically limit the efficiency of these methods since they are mostly developed under the assumption of a semi-infinite 1D layered medium without topography. Taking advantages of wave propagation modeling algorithm development and high-performance computing center accessibility, it is now possible to consider the use of a 3D elastic forward modeling algorithm for the inversion of surface wave dispersion. We use a parallelized 3D elastic modeling code based on the spectral element method which allows to obtain accurate synthetic data with very low numerical dispersion and a reasonable numerical cost. In this study, we choose a sea dike as a case example. We first show that their longitudinal geometry and structure may have a significant effect on dispersion diagrams of Rayleigh waves. Then, we investigate the sensitivity of the dispersion diagrams to small velocity and layer thickness perturbations, and show the limitations of the standard 1D surface wave methods approach. Finally, we demonstrate in this context the benefits of using both a 3D forward modeling engine and the whole dispersion diagram, instead of the dispersion curves only.

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Section: D Inversion Using Particle Swarm Optimizationmentioning

confidence: 99%

Assessment of Physical Properties of a Sea Dike Using Multichannel Analysis of Surface Waves and 3D Forward Modeling

Pageot¹,

Feuvre²,

Leparoux³

et al. 2017

Proceedings

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show abstract

“…In geophysical inversion applications, the PSO algorithm has shown good performance in terms of global optimization ability, convergence rate, and robustness. PSO has been applied to direct current (DC) resistivity methods [37,38], seismic [39][40][41], streaming-potential [27], magnetic [33,42], gravity [43][44][45], airborne EM [46], AMT and MT data [29], and self-potential [47]. Furthermore, it has also been successfully used for an artificial neural network [48], reservoir characterization [49], and for big data research [50].…”

Section: Introductionmentioning

confidence: 99%

Untitled

2021

J. Eng. Technol. Sci.

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The PSO inversion scheme was applied to the grounded wire TDEM method to infer subsurface resistivity models. The forward modeling part was expressed in the Laplace domain, while the transformation into the time-domain was conducted using the Gaver-Stehvest method. The inversion scheme was tested on noise-free and noisy synthetic data to evaluate the accuracy of the scheme and was then applied to field data recorded at two stations in a volcanic-geothermal region. The inversion scheme gave an excellent fit between the observed data and the calculated data.

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“…Yang et al . (2017) proposed the PSO's application to seismic wave impedance inversion. Grandis and Maulana (2017) introduced PSO into the inversion of synthetic MT data and obtained one‐dimensional resistivity–depth variation.…”

Section: Introductionmentioning

confidence: 99%

“…Shaw and Srivastava (2007) applied PSO to one-dimensional direct current (DC) and magnetotelluric (MT) sounding, to make theoretical geoelectric model data and measured geoelectric data inversion, respectively. Yang et al (2017) proposed the PSO's application to seismic wave impedance inversion. Grandis and Maulana (2017) introduced PSO into the inversion of synthetic MT data and obtained one-dimensional resistivity-depth variation.…”

Section: Introductionmentioning

confidence: 99%

Transient electromagnetic inversion based on particle swarm optimization and differential evolution algorithm

Zhuang

et al. 2020

Near Surface Geophysics

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For transient electromagnetic inversion, a gradient‐based algorithm is strongly dependent on the quality of the initial model, while any non‐gradient‐based algorithm often falls too easily into local optima. This paper proposes a joint differential‐evolution–particle‐swarm‐optimization inversion algorithm, which provides a better global optimization. A dual‐population evolution strategy and information exchange mechanism is presented. For verification, this is followed by adoption of a layered inversion model in the transient electromagnetic inversion with a central loop. The results show that the differential‐evolution–particle‐swarm‐optimization joint algorithm can reduce the probability of a premature phenomenon (i.e. falling into local optima) and improve the inversion accuracy, efficiency and stability, with a fast convergence occuring in the early stages. Furthermore, the proposed algorithm has a higher degree of fitting (prediction ability) for data inversion and is feasible for transient electromagnetic inversion.

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Particle swarm optimization and its application to seismic inversion of igneous rocks

Cited by 25 publications

References 4 publications

Assessment of Physical Properties of a Sea Dike Using Multichannel Analysis of Surface Waves and 3D Forward Modeling

Assessment of Physical Properties of a Sea Dike Using Multichannel Analysis of Surface Waves and 3D Forward Modeling

Untitled

Transient electromagnetic inversion based on particle swarm optimization and differential evolution algorithm

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