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• W2777218503 abstract "We present an efficient inversion tool for parameter estimation from horizontal loop electromagnetic (HLEM) data using Differential Search Algorithm (DSA) which is a swarm-intelligence-based metaheuristic proposed recently. The depth, dip, and origin of a thin subsurface conductor causing the anomaly are the parameters estimated by the HLEM method commonly known as Slingram. The applicability of the developed scheme was firstly tested on two synthetically generated anomalies with and without noise content. Two control parameters affecting the convergence characteristic to the solution of the algorithm were tuned for the so-called anomalies including one and two conductive bodies, respectively. Tuned control parameters yielded more successful statistical results compared to widely used parameter couples in DSA applications. Two field anomalies measured over a dipping graphitic shale from Northern Australia were then considered, and the algorithm provided the depth estimations being in good agreement with those of previous studies and drilling information. Furthermore, the efficiency and reliability of the results obtained were investigated via probability density function. Considering the results obtained, we can conclude that DSA characterized by the simple algorithmic structure is an efficient and promising metaheuristic for the other relatively low-dimensional geophysical inverse problems. Finally, the researchers after being familiar with the content of developed scheme displaying an easy to use and flexible characteristic can easily modify and expand it for their scientific optimization problems." @default.
• W2777218503 created "2018-01-05" @default.
• W2777218503 creator A5033981937 @default.
• W2777218503 creator A5037727561 @default.
• W2777218503 date "2018-02-01" @default.
• W2777218503 modified "2023-09-24" @default.
• W2777218503 title "Parameter estimation by Differential Search Algorithm from horizontal loop electromagnetic (HLEM) data" @default.
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• W2777218503 doi "https://doi.org/10.1016/j.jappgeo.2017.12.016" @default.
• W2777218503 hasPublicationYear "2018" @default.
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