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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

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/FB185C51-19BE-4ED8-B426-1532957ADA4D-
Αναγνωριστικόhttps://doi.org/10.1007/s00024-022-03166-x-
Αναγνωριστικόhttps://link.springer.com/article/10.1007/s00024-022-03166-x-
Γλώσσαen-
Μέγεθος23 pagesen
ΤίτλοςA comparative analysis of three computational-intelligence metaheuristic methods for the optimization of TDEM dataen
ΔημιουργόςPace Francescaen
ΔημιουργόςRaftogianni Adamantiaen
ΔημιουργόςΡαυτογιαννη Αδαμαντιαel
ΔημιουργόςGodio Albertoen
ΕκδότηςSpringeren
ΠερίληψηWe 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
ΤύποςPeer-Reviewed Journal Publicationen
ΤύποςΔημοσίευση σε Περιοδικό με Κριτέςel
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2024-02-09-
Ημερομηνία Δημοσίευσης2022-
Θεματική ΚατηγορίαStochastic inverse modelingen
Θεματική ΚατηγορίαTime-domain electromagnetic dataen
Θεματική ΚατηγορίαParticle swarm optimizationen
Θεματική ΚατηγορίαGenetic algorithmen
Θεματική ΚατηγορίαGrey wolf optimizeren
Θεματική ΚατηγορίαComputational swarm intelligenceen
Βιβλιογραφική ΑναφοράF. 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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