MAGNETIC INVERSION TO RECOVER COMPACT STRUCTURES BY AN ARCTANGENT STABILIZER

This study introduces a novel method for the inversion of magnetic data using a focusing inversion technique. The method utilizes an arctangent stabilizing functional, which is reformulated into a pseudo-quadratic form by a weighting matrix. To optimize the process, the reweighted regularized conjugate gradient (RRCG) method is employed. The proposed technique is effective in restoring compact structures in subsurface structures without the need for a focusing parameter. The inversion method involves constructing an objective function for minimization, which incorporates the discrepancy between observed and predicted data and the deviation of the model from expected characteristics, which is known as the stabilizing functional. The data fit component determines how closely the inversion results match the observed measurements, while the model regularization term influences specific desired properties of the reconstructed density distribution. The study demonstrates the effectiveness of the proposed technique through its application to a synthetic dataset and one real-world aeromagnetic dataset from the McFaulds Lake area in Ontario, Canada.