3D inversion of QMAGT airborne magnetic gradiometry data for susceptibility and magnetization vector models of the Thompson Nickel Belt in Manitoba, Canada

Jorgensen, Matthew R.; Zhdanov, Michael S.; Parsons, Brian

doi:10.1190/image2022-3739311.1

Cited by 1 publication

(1 citation statement)

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“…Inverting the data obtained from full tensor magnetic gradiometry into a magnetization vector through 3D inversion remains a challenging problem [4,5]. In this paper, we present recent advances in FTMG data modeling and inversion and illustrate the developed methods using the results of a helicopter-borne QMAGT FTMG survey conducted over the Thompson Nickel Belt near Thompson, Manitoba, Canada.…”

Section: Introductionmentioning

confidence: 99%

3D Focusing Inversion of Full Tensor Magnetic Gradiometry Data with Gramian Regularization

2023

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Full tensor magnetic gradiometry (FTMG) is becoming a practical method for exploration due to recent advancements in superconducting quantum interference device (SQUID) technology. This paper introduces an efficient method of 3D modeling and inversion of FTMG data. The forward modeling uses single-point Gaussian integration with pulse basis functions to compute the volume integrals representing the second spatial derivatives of the magnetic potential. The inversion is aimed at recovering both the magnetic susceptibility and magnetization vectors. We have introduced a 3D regularized focusing inversion technique that utilizes Gramian regularization and a moving sensitivity domain approach. We have also developed a new method of magnetization vector decomposition into induced and remanent parts. The case study includes applying the developed inversion method and computer code to interpret a helicopter-borne FTMG survey carried out over the Thompson Nickel Belt. We have analyzed and separately inverted the observed FTMG and total magnetic intensity (TMI) data using the developed 3D inversion methods to obtain the subsurface susceptibility and magnetization vector models. Furthermore, we present a comparison of the inversions utilizing the FTMG data and the TMI data.

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Section: Introductionmentioning

confidence: 99%