QN inversion of large-scale MT data

A limited memory quasi-Newton (QN) method with simple bounds is applied to a 1-D magnetotelluric (MT) problem. The method is used to invert a realistic synthetic MT impedance dataset, calculated for a layered earth model. An adjoint method is employed to calculate the gradients and to speed up the inverse problem solution. In addition, it is shown that regularization stabilizes the QN inversion result. We demonstrate that only a few correction pairs are enough to produce reasonable results. Comparison with inversion based on known L-BFGS-B optimization algorithm shows similar convergence rates. The study presented is a first step towards the solution of large-scale electromagnetic problems with a full 3-D conductivity structure of the Earth.