Three Algorithms of Magnetization Vector Inversion for Magnetic Data

Abstract:

In the magnetic exploration, it is meaningful to recover the distributions of total magnetization vector (TMV) since the distortions of remanence and self-demagnetization produce the similar responses that alter their magnitude and direction. We evaluate three approaches of magnetization vector inversion (MVI): (1) simultaneously inverting the TMV's three orthogonal components (MMM); (2) the magnitude, inclination and declination (MID); (3) orderly inverting the magnetization intensity, inclination and declination based on the transformed magnitude magnetic anomaly (M-ID). The primary implementation of MVI is to establish the symmetric positive definite matrix equations on the corrections of the model parameters and observed data sets. Then the optimal solutions are iteratively computed by use of the preconditioned conjugate gradient algorithm. We use the synthetic and real data sets to test these methods and the tests reveal that the isochronous MMM inversion aggravates the geophysical non-uniqueness problem and MID performs low stability of convergence due to the strong dependence on the starting models. While the sequential M-ID shows superior stability and precision of inverting the magnetization intensity and direction by making successive use of the amplitude and phase information of the magnetic anomaly. Finally, the achieved TMV distributions help us to investigate the influence of remanent magnetization and to recover the physical property distributions for high susceptibility when the self-demagnetization effect is not negligible.