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A comparative analysis of three computational-intelligence metaheuristic methods for the optimization of TDEM data

Pace Francesca, Raftogianni Adamantia, Godio Alberto

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URIhttp://purl.tuc.gr/dl/dias/FB185C51-19BE-4ED8-B426-1532957ADA4D-
Identifierhttps://doi.org/10.1007/s00024-022-03166-x-
Identifierhttps://link.springer.com/article/10.1007/s00024-022-03166-x-
Languageen-
Extent23 pagesen
TitleA comparative analysis of three computational-intelligence metaheuristic methods for the optimization of TDEM dataen
CreatorPace Francescaen
CreatorRaftogianni Adamantiaen
CreatorΡαυτογιαννη Αδαμαντιαel
CreatorGodio Albertoen
PublisherSpringeren
Content SummaryWe focus on the performances of three nature-inspired metaheuristic methods for the optimization of time-domain electromagnetic (TDEM) data: the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO) and the Grey Wolf Optimizer (GWO) algorithms. While GA and PSO have been used in a plethora of geophysical applications, GWO has received little attention in the literature so far, despite promising outcomes. This study directly and quantitatively compares GA, PSO and GWO applied to TDEM data. To date, these three algorithms have only been compared in pairs. The methods were first applied to a synthetic example of noise-corrupted data and then to two field surveys carried out in Italy. Real data from the first survey refer to a TDEM sounding acquired for groundwater prospection over a known stratigraphy. The data set from the second survey deals with the characterization of a geothermal reservoir. The resulting resistivity models are quantitatively compared to provide a thorough overview of the performances of the algorithms. The comparative analysis reveals that PSO and GWO perform better than GA. GA yields the highest data misfit and an ineffective minimization of the objective function. PSO and GWO provide similar outcomes in terms of both resistivity distribution and data misfits, thus providing compelling evidence that both the emerging GWO and the established PSO are highly valid tools for stochastic inverse modeling in geophysics.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2024-02-09-
Date of Publication2022-
SubjectStochastic inverse modelingen
SubjectTime-domain electromagnetic dataen
SubjectParticle swarm optimizationen
SubjectGenetic algorithmen
SubjectGrey wolf optimizeren
SubjectComputational swarm intelligenceen
Bibliographic CitationF. Pace, A. Raftogianni and A. Godio, “A comparative analysis of three computational-intelligence metaheuristic methods for the optimization of TDEM data,” Pure Appl. Geophys., vol. 179, no. 10, pp. 3727–3749, Oct. 2022, doi: 10.1007/s00024-022-03166-x.en

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