Comment on ‘Magnetic appraisal using simulated annealing’ by S. E. Dosso and D. W. Oldenburg

During the last few years the electromagnetic community has enthusiastically adopted extremal inversion as a tool for constructing models from data. The concept is simple: one minimizes or maximizes some penalty on the model, m, whilst demanding that the model fit the observed data to within some reasonable level of misfit, x+. A Lagrange multiplier formulation is usually employed to realize this concept, so that an unconstrained functional U U=R(m) + p-’x(m), (1) is minimized. Dosso & Oldenburg (1991, this issue) use this approach, as can be seen by inspection of their equation (9). The first term, R(m) is a functional of the model which returns a property which one wishes to penalize; Dosso & Oldenburg choose a boxcar average of the model. The term x(m) measures the misfit obtained by the model. The relative importance of the model penalty and the misfit is