AIRRLS: An Augmented Iteratively Re-weighted and Refined Least Squares Algorithm for Inverse Modeling of Magnetometry Data

Authors

  • Maysam Abedi Geo-Exploration Targeting Lab (GET-Lab), School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran

DOI:

https://doi.org/10.30564/jgr.v1i3.1316

Abstract

This work aims to examine the functionality of a new Augmented Iteratively Re-weighted and Refined Least Squares algorithm (AIRRLS) to generate a 3D model of magnetic susceptibility property from a potential field magnetometry survey. Whereby this algorithm ameliorates an lpnorm Tikhonov regularization cost function through replacing a set of weighted linear system of equations. It leads to constructing a magnetic susceptibility model that iteratively converges to an optimum solution, meanwhile the regularization parameter performs as a stopping criterion to finalize the iterations. To tackle and suppress the intrinsic tendency of a sought target responsible for generating a magnetic anomaly and to not be imaged at shallow depth in inverse modeling, a prior depth weighting function is imposed in the principle system of equations. The significance of this research lies in improvement of the performance of the inversion, where the running time of an lp norm problem after incorporating a pre-conditioner conjugate gradient solver (PCCG) in cases of large scale geophysical dataset. Forasmuch as this study attempts to image a geological target with low magnetic susceptibility property, it is assumed that there is no remanent magnetization. The applicability of the algorithm is tested for a synthetic multi-source data to demonstrate its performance in 3D modeling . Subsequently, a real case study in Semnan province of Iran, is investigated to image an embedded porphyry copper layer in a sequence of sediments. The sought target consists of a concealed arc-shaped porphyry andesite unit that may have potential of Cuoccurrences. Results prove that it extends down at depth, so exploratory drilling is highly recommended to get insights about its potential for Cu-bearing mineralization.

Keywords:

lp norm problem; AIRRLS algorithm; 3D inversion; Magnetic anomaly; Porphyry mineralization

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How to Cite

Abedi, M. (2019). AIRRLS: An Augmented Iteratively Re-weighted and Refined Least Squares Algorithm for Inverse Modeling of Magnetometry Data. Journal of Geological Research, 1(3), 16–27. https://doi.org/10.30564/jgr.v1i3.1316

Issue

Vol. 1 , Iss. 3 (October 2019)

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