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• W3204198354 endingPage "100070" @default.
• W3204198354 startingPage "100070" @default.
• W3204198354 abstract "In this study, the potential and efficacy of adapting the bioinspired Manta ray foraging optimization algorithm (MRFO) as a tool for estimating model parameters of two-dimensional gravity profile anomalies is presented. The process of implementing the MRFO algorithm for accurate estimation of shape/depth defining parameters over geometric geologic structures was assessed. The experimental data comprised synthetically generated gravity anomalies that were later corrupted with white Gaussian noise at levels of 5, 10, and 15%, and then, case examples were taken from mining sites across different parts of the world. The algorithm was found to be fast, stable, and consistent in its search for the global best solution to each of the geophysical inverse problems. Its performance was excellent when confronted with constrained multi-parameter non-linear inversion problems and exhibited admirable stability even in the presence of noise. The consistency of the estimated results, when compared to actual values, affirmed the reliability of the procedure. It is, therefore, a stable and efficient tool for performing geophysical data inversion and is recommended for use in inverting geophysical data with higher complexities like seismic reflection, self-potential and magnetic data, which require many corrections to be performed before reliable geological interpretations can be made." @default.
• W3204198354 created "2021-10-11" @default.
• W3204198354 creator A5027793976 @default.
• W3204198354 creator A5038056885 @default.
• W3204198354 creator A5072175362 @default.
• W3204198354 creator A5082724502 @default.
• W3204198354 date "2021-12-01" @default.
• W3204198354 modified "2023-09-27" @default.
• W3204198354 title "Novel technique for the interpretation of gravity anomalies over geologic structures with idealized geometries using the Manta ray foraging optimization" @default.
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• W3204198354 doi "https://doi.org/10.1016/j.jaesx.2021.100070" @default.
• W3204198354 hasPublicationYear "2021" @default.
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