The effect of remanence has long been recognized as an obstacle for the interpretation and modeling of magnetic data. In this paper, we propose a Cooperative Magnetic Inversion (CMI) algorithm for the 3-D inversion of magnetic data affected by remanent magnetization. The CMI algorithm incorporates advantages from two inversion strategies. Magnetic amplitude data are first inverted to recover an effective susceptibility model, providing information about the geometry and extent of the magnetic anomaly. The effective susceptibility model is then used to constrain a Magnetic Vector Inversion (MVI), recovering the orientation and magnitude of magnetization. We test the CMI algorithm on a ground magnetic survey over the Osborne Cu-Au deposit, Queensland. In both the case study and the synthetic experiments, the cooperative approach improves the resolution of magnetized bodies over each of the inversion methods used separately.

Presentation Date: Monday, October 17, 2016

Start Time: 3:20:00 PM

Location: 161

Presentation Type: ORAL

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