Fast automated airborne electromagnetic data interpretation using parallelized particle swarm optimization

Desmarais, Jacques K.; Spiteri, Raymond J.

doi:10.1016/j.cageo.2017.09.007

Cited by 9 publications

(2 citation statements)

References 26 publications

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“…The particle swarm algorithm (PSO) is a stochastic optimization technique based on population, which utilizes a group of random initial solutions for finding the optimum solution through iterative processes [25]. PPSO enables information sharing on the optimal positions of all populations among multiple particle swarms [26][27][28][29]. After each iteration, the optimal position, generated from comparing all populations, becomes the optimal position of particles in all populations.…”

Section: Parallel Particle Swarm Algorithmmentioning

confidence: 99%

Optimization of G1 Micromixer Structure in Two-Fluid Mixing Based on CFD and Response Surface Methodology

Qin,

Lu,

et al. 2024

Processes

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Optimizing the structure of micromixers to improve the mixing efficiency is of great significance for chemical engineering and biology fields. In this study, an optimization of the microchannel in two liquids mixing is carried out based on computational fluid dynamics (CFD) and response surface methodology. Firstly, CFD simulations were performed to investigate the mixing flow field and mixing efficiency in the microchannel by considering different process and structure parameters (e.g., feed pressure p, microchannel width w). The response surface methodology was adopted to construct a fitting surface by CFD discrete working conditions. Then, an optimized microchannel width w was searched using the parallel particle swarm optimization (PPSO) algorithm from the response surface. Lastly, the searched optimum was validated by CFD simulation again, and the final result showed that the predicted mixing efficiency from the response surface model is well confirmed by CFD simulation. On average, the new optimized microchannel width of 1.634 mm performs higher flow flux and mixing efficiency than the original width of 1.5 mm, increasing 13.51% and 2.45%, respectively.

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Section: Parallel Particle Swarm Algorithmmentioning

confidence: 99%

Optimization of G1 Micromixer Structure in Two-Fluid Mixing Based on CFD and Response Surface Methodology

Qin,

Lu,

et al. 2024

Processes

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“…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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