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

Abstract

This work aims to examine the functionality of a new Augmented Iteratively Re-weighted and Refined Least Squares algorithm (AIRRLS) togenerate a 3D model of magnetic susceptibility property from a potentialfield magnetometry survey. Whereby this algorithm ameliorates an lpnorm Tikhonov regularization cost function through replacing a set ofweighted linear system of equations. It leads to constructing a magneticsusceptibility model that iteratively converges to an optimum solution,meanwhile the regularization parameter performs as a stopping criterionto finalize the iterations. To tackle and suppress the intrinsic tendency ofa sought target responsible for generating a magnetic anomaly and to notbe imaged at shallow depth in inverse modeling, a prior depth weightingfunction is imposed in the principle system of equations. The significanceof this research lies in improvement of the performance of the inversion,where the running time of an lp norm problem after incorporating apre-conditioner conjugate gradient solver (PCCG) in cases of large scalegeophysical dataset. Forasmuch as this study attempts to image a geological target with low magnetic susceptibility property, it is assumed thatthere is no remanent magnetization. The applicability of the algorithm istested for a synthetic multi-source data to demonstrate its performancein 3D modeling . Subsequently, a real case study in Semnan provinceof Iran, is investigated to image an embedded porphyry copper layerin a sequence of sediments. The sought target consists of a concealedarc-shaped porphyry andesite unit that may have potential of Cuoccurrences. Results prove that it extends down at depth, so exploratorydrilling is highly recommended to get insights about its potential forCu-bearing mineralization.