Abstract
The well-known non-uniqueness in modeling of potential-field data results in an infinite number of models that fit the data almost equally. This non-uniqueness concept is exploited to devise a method to transform the magnetic data based on their equivalent-source. The unconstrained 3D magnetic inversion modeling is used to obtain the anomalous sources, i.e. 3D magnetization distribution in the subsurface. Although the 3D model fitting the data is not geologically feasible, it can serve as an equivalent-source. The transformations, which are commonly applied to magnetic data (reduction to the pole, reduction to the equator, upward and downward continuation), are the response of the equivalent-source with appropriate kernel functions. The application of the method to both synthetic and field data showed that the transformation of magnetic data using the 3D equivalent-source gave satisfactory results. The method is relatively more stable than the filtering technique, with respect to the noise present in the data.
Highlights
The concept of equivalent-source exploits the ambiguity or non-uniqueness in the modeling of potential-field data
This paper describes the application of 3D magnetic inversion to obtain 3D equivalent-source
In this paper we focus on the reduction to the pole (RTP), reduction to the equator (RTE), and upward continuation of magnetic data
Summary
The concept of equivalent-source exploits the ambiguity or non-uniqueness in the modeling of potential-field (gravity and magnetic) data. The equivalent-source can be used to interpolate data at a homogeneous grid [1,2,3] or to obtain the field at a different height as in the upward or downward continuation transformation [4]. The purely under-determined inversion with minimum-norm solution [7] may result in an unrealistic geological model of magnetization distribution. Such model can be used as an equivalent-source for calculating transformations commonly applied to magnetic data. The method is relatively more stable than the filtering technique, with respect to the noise present in the data
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