Nonlinear magnetic inversion using a random search method

A robust magnetic interpretation technique is described. It is based on the minimization of an objective function by a random search algorithm. Instead of just one estimate, this algorithm yields several points in parameter space, all producing objective function values below an assumed noise level. The centroid of the search points is in general a better estimate than each individual search point, especially when data are corrupted by noise. Since no derivatives are employed, even nondifferentiable objective functions, such as the l1 norm of residuals, can be used. Computation of parameter covariance matrices and confidence ellipses is possible using simple formulas. The usefulness of R‐ and Q‐mode factor analysis applied to the correlation matrix is demonstrated. The R‐mode factor analysis can be used in parameter variance control, and the Q‐mode factor analysis provides means to find models which are extreme in some sense. The technique was applied to theoretical data and to field data from three diffe...