Cg. Farquharson et Dw. Oldenburg, NONLINEAR INVERSION USING GENERAL MEASURES OF DATA MISFIT AND MODEL STRUCTURE, Geophysical journal international, 134(1), 1998, pp. 213-227
We investigate the use of general, non-l(2), measures of data misfit a
nd model structure in the solution of the non-linear inverse problem.
Of particular interest are robust measures of data misfit, and measure
s of model structure which enable piecewise-constant models to be cons
tructed. General measures can be incorporated into traditional lineari
zed, iterative solutions to the non-linear problem through the use of
an iteratively reweighted least-squares (IRLS) algorithm. We show how
such an algorithm can be used to solve the linear inverse problem when
general measures of misfit and structure are considered. The magnetic
stripe example of Parker (1994) is used as an illustration. This exam
ple also emphasizes the benefits of using a robust measure of misfit w
hen outliers are present in the data. We then show how the IRLS algori
thm can be used within a linearized, iterative solution to the non-lin
ear problem. The relevant procedure contains two iterative loops which
can be combined in a number of ways. We present two possibilities. Th
e first involves a line search to determine the most appropriate value
of the trade-off parameter and the complete solution, via the IRLS al
gorithm, of the linearized inverse problem for each value of the trade
-off parameter. In the second approach, a schedule of prescribed value
s for the trade-off parameter is used and the iterations required by t
he IRLS algorithm are combined with those for the linearized, iterativ
e inversion procedure. These two variations are then applied to the 1-
D inversion of both synthetic and field time-domain electromagnetic da
ta.