ISSN:
1365-246X
Source:
Blackwell Publishing Journal Backfiles 1879-2005
Topics:
Geosciences
Notes:
There are many techniques for modelling the geomagnetic field, any one of which may be suitable for a particular application depending on its associated modelling goals. Each method combines a choice of functions and an approach to fitting data so that, in general, it is best suited to a particular type of field modelling, e.g. core versus crustal, regional versus global, downward continuation versus interpolation. Those few approaches such as spherical cap harmonic analysis (Haines 1985a) that possess any true flexibility in this respect suffer from mathematical and computational complexity. In addition, regularization is still a somewhat overlooked issue. Regularization is essential for downward continuing geomagnetic data because shorter wavelength field components and their errors blow up in this process. Approaches such as harmonic spline modelling (Shure, Parker & Backus 1982) which include regularization do so while significantly complicating the task of inversion. We present a new regularized modelling scheme which employs magnetic monopoles as representing functions. We apply regularizing norms of the type introduced by Shure et al. (1982). Owing to the mathematical simplicity of the monopoles, the expressions for the norms are themselves very simple and flexible, and the monopole models very easy to compute. Moreover, the conceptual simplicity of this representation allows for easy modification to accommodate most geomagnetic modelling problems. We apply the technique to problems on three different length scales, each application having distinctly different modelling goals: globally we model the radial core field at the core-mantle boundary (CMB) from satellite data; on a large regional scale we model the radial crustal field at the earth's surface from satellite data; on a small regional scale we model the radial crustal field at the earth's surface from surface data. For each of these varied applications we are able to generate monopole models which produce smooth, plausible fields that fit the data.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1111/j.1365-246X.1994.tb03985.x
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