Inversion modeling is basically the process of modifying the model (model parameters, m) in order to obtain a fit solution with the observed or calculated data (d). Thus, why this method is often referred to as data fitting. Linear inversion represents the linearity between m and d as shown as d = G.m where G the kernel matrix.