A Prescaled Multiplicative Regularized Gauss-Newton Inversion

A prescaled multiplicative regularized Gauss-Newton inversion (GNI) algorithm is proposed which utilizes a priori information about the expected ratio between the average magnitude of the real and imaginary parts of the true contrast as well as the expected ratio between the average magnitude of the gradient of the real and imaginary parts of the true contrast. Using both synthetically and experimentally collected data sets, we show that this prescaled inversion algorithm is successful in reconstructing both real and imaginary parts of the contrast when there is a large imbalance between the average magnitude of these two parts where the standard multiplicative regularized Gauss-Newton inversion algorithm fails. We further show that the proposed prescaled inversion algorithm is robust and does not require the a priori information to be exact.