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• W3010998214 abstract "The presented work examines application of an Augmented Iteratively Re-weighted and Refined Least Squares method (AIRRLS) to construct a 3D magnetic susceptibility property from potential field magnetic anomalies. This algorithm replaces an lp minimization problem by a sequence of weighted linear systems in which the retrieved magnetic susceptibility model is successively converged to an optimum solution, while the regularization parameter is the stopping iteration numbers. To avoid the natural tendency of causative magnetic sources to concentrate at shallow depth, a prior depth weighting function is incorporated in the original formulation of the objective function. The speed of lp minimization problem is increased by inserting a pre-conditioner conjugate gradient method (PCCG) to solve the central system of equation in cases of large scale magnetic field data. It is assumed that there is no remanent magnetization since this study focuses on inversion of a geological structure with low magnetic susceptibility property. The method is applied on a multi-source noise-corrupted synthetic magnetic field data to demonstrate its suitability for 3D inversion, and then is applied to a real data pertaining to a geologically plausible porphyry copper unit. The real case study located in Semnan province of Iran consists of an arc-shaped porphyry andesite covered by sedimentary units which may have potential of mineral occurrences, especially porphyry copper. It is demonstrated that such structure extends down at depth, and consequently exploratory drilling is highly recommended for acquiring more pieces of information about its potential for ore-bearing mineralization." @default.
• W3010998214 created "2020-03-23" @default.
• W3010998214 creator A5066522553 @default.
• W3010998214 date "2020-03-08" @default.
• W3010998214 modified "2023-09-25" @default.
• W3010998214 title "An Augmented Iteratively Re-weighted and Refined Least Squares Method for lp Minimization Problem in Inverse Modeling of Potential Field Magnetic Data" @default.
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• W3010998214 doi "https://doi.org/10.30564/jgr.v1i3.1316" @default.
• W3010998214 hasPublicationYear "2020" @default.
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