Abstract
This paper presents a new method to invert magnetic anomaly data in a variety of non-complex contexts when a priori information about the sources is not available. The region containing magnetic sources is discretized into a set of homogeneously magnetized rectangular prisms, polarized along a common direction. The magnetization distribution is calculated by solving an underdetermined linear system, and is accomplished through the simultaneous minimization of the norm of the solution and the misfit between the observed and the calculated field. Our algorithm makes use of a dipolar approximation to compute the magnetic field of the rectangular blocks. We show how this approximation, in conjunction with other correction factors, presents numerous advantages in terms of computing speed and depth resolution, and does not affect significantly the success of the inversion. The algorithm is tested on both synthetic and real magnetic datasets.
Highlights
It is well known how inversion applied to surface potential field data is a mathematical ill-posed problem which suffers from ambiguity of solutions (Blakely, 1995)
We have proposed a new method for the 3D inversion of magnetic anomalies when a priori knowledge on the sources and a multi-observation level dataset are not available
We have shown that it is possible to estimate the location and magnetization intensity of magnetic sources by means of a discretization of the subsoil into elementary parallelepipeds whose magnetic effect is approximated by a dipolar field
Summary
It is well known how inversion applied to surface potential field data is a mathematical ill-posed problem which suffers from ambiguity of solutions (Blakely, 1995). An inversion based on a simultaneous minimization of the misfit of the data and the Euclidean norm of the solution causes the lack of depth resolution To overcome this problem it is possible to use a weighting function (Li and Oldenburg, 1996; Portnianguine and Zhdanov, 2002). Fedi and Rapolla (1999) have proposed to use a multi-observation level dataset, arguing that this kind of data provide depth resolution This statement is still a subject of some debate (Oldenburg and Li, 2003). This approximation does not affect significantly the success of the inversion method This approach makes it possible, on the one hand, the choice of a weighting function valid for every magnetic source geometry and, on the other, to improve the calculations in terms of computing time. After a description of our inversion technique, we show its efficiency on both synthetic and real magnetic data sets
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