Estimation of Deep-Seated Faults Parameters from Gravity Data Using the Cuckoo Search Algorithm

Cuckoo Search Algorithm (CSA) is a nature-inspired metaheuristic optimization algorithm. The optimization algorithm was inspired by the life story of a family of birds called the Cuckoo. In this paper, an application of the CSA, is implemented to estimate model parameters of deep-seated faults by us...

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Veröffentlicht in:Pure and applied geophysics Jg. 180; H. 12; S. 4147 - 4173
Hauptverfasser: Özyalın, Şenol, Tunçel, Aykut
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 01.12.2023
Springer Nature B.V
Schlagworte:
Algorithms
Compatibility
Earth and Environmental Science
Earth Sciences
Function analysis
Geophysics/Geodesy
Gravity anomalies
Gravity data
Heuristic methods
Mathematical models
Modelling
Optimization
Optimization algorithms
Parameters
Probability density function
Probability density functions
Probability theory
Search algorithms
Synthetic data
Egypt
Cuckoo Search Algorithm
Deep-seated faults
Gravity inversion
Parameter tuning
ISSN:0033-4553, 1420-9136
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Zusammenfassung:Cuckoo Search Algorithm (CSA) is a nature-inspired metaheuristic optimization algorithm. The optimization algorithm was inspired by the life story of a family of birds called the Cuckoo. In this paper, an application of the CSA, is implemented to estimate model parameters of deep-seated faults by using gravity anomalies. To increase the efficiency of the algorithm, parameter tuning studies are carried out for the optimum algorithm-based control parameters. In addition, the reliability and possible uncertainties of the obtained solutions were examined using probability density function analysis. The model parameters ( z 1 , z 2 , α, x 0 and ρ) for noisy and noise-free synthetic models were obtained by using CSA. These obtained parameters are very compatible with the true model parameters for noise-free data, similarly well-nigh compatible results for noisy data. As a result we can say that the method works successfully when applied to synthetic data. The technique is tested on three widely studied benchmark field data including gravity anomaly [Garber Fault (USA), Gazal Fault (Egypt), Mersa Matruh Fault (Egypt)]. In previous studies, various solution techniques have been used for the inversion of these field data and the compatibility of the model parameters obtained in this study with the values obtained from previous studies was compared. The results demonstrate that the CSA is a very effective and robust approach for the optimization of gravity anomalies.
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ISSN:0033-4553
1420-9136
DOI:10.1007/s00024-023-03368-x