2023
DOI: 10.1016/j.geothermics.2022.102597
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Joint interpretation of gravity and airborne magnetic data along the Calama-Olacapato-Toro fault system (Central Puna, NW Argentina): Structural and geothermal significance

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Cited by 6 publications
(4 citation statements)
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“…Further, two point five dimensional man-machine interactive inversion technology is a perfect compromise between quantitative interpretation of the complicated anomalies and varying the theoretical parameters (Pinto and Casas, 1996 ). It is used to decipher the four elements of deep geothermal resource (heat source, geothermal reservoir, caprock, and geothermal migration pathway) (Ahumada et al, 2022 ). The first step for the prediction model was established according to the estimated geological bodies.…”
Section: Geophysical Analytical Techniquesmentioning
confidence: 99%
“…Further, two point five dimensional man-machine interactive inversion technology is a perfect compromise between quantitative interpretation of the complicated anomalies and varying the theoretical parameters (Pinto and Casas, 1996 ). It is used to decipher the four elements of deep geothermal resource (heat source, geothermal reservoir, caprock, and geothermal migration pathway) (Ahumada et al, 2022 ). The first step for the prediction model was established according to the estimated geological bodies.…”
Section: Geophysical Analytical Techniquesmentioning
confidence: 99%
“…Furthermore, another new method using variance analysis has been developed by Essa et al [ 60 ] for the magnetic anomaly profile interpretation employing idealized geometrical bodies [ 59 , 60 ]. Deep learning neural networks (DNNs) were used to retrieve the physical properties' distribution of buried magnetic geobodies from the airborne and surface magnetic anomaly data [ 34 , [61] , [62] , [63] , [64] ]. The application of the reduced-to-pole and intensity magnetic maps, analytic signal, power spectrum, local wavenumber maps, and tilt angle were used to describe and allocate the mineralization zones and structural elements' description like as sulfide, gold, and uranium in lineament features, recognizing magnetic sources distribution, and mineral zones [ 16 , 17 , 25 , [65] , [66] , [67] ].…”
Section: Introductionmentioning
confidence: 99%
“…Deep learning neural networks (DNNs) were used to recover the distribution of the physical properties of buried magnetic orebodies from the surface and airborne magnetic anomaly data. Compared with the conventional method, the predicted distribution of magnetization intensity obtained using train a DNN model was more concentrated and had a better resolution to determine the boundary of the magnetic body (Ahumada et al, 2023;Hu et al, 2021). The application of the total intensity magnetic and reduced-to-pole maps, power spectrum, analytic signal, tilt-angle, and local wavenumber maps were used to allocate and describe the structural elements and mineralization zones such as uranium, gold, and sulfide in recognizing magnetic sources distribution, lineament features, and mineral zones (Essa et al, 2022;Hosseini et al, 2023).…”
Section: Introductionmentioning
confidence: 99%