A two-dimensional magnetotelluric inversion assuming Gaussian static shift

Abstract

A data-adaptive 2-D inversion scheme for magnetotelluric data, which simultaneously finds the smoothest model with the smallest misfit whilst solving for galvanic static shift parameters, is described. Trade-off parameters between data misfit, model roughness, and static shift norm are determined so as to maximize the likelihood of the data on the assumption that static shifts have Gaussian distributions.