Variational regularisation for inverse problems with imperfect forward operators and general noise models

We study variational regularisation methods for inverse problems with imperfect forward operators whose errors can be modelled by order intervals in a partial order of a Banach lattice. We carry out analysis with respect to existence and convex duality for general data fidelity terms and regularisation functionals. Both for <i>a priori</i> and <i>a posteriori</i> parameter choice rules, we obtain convergence rates of the regularised solutions in terms of Bregman distances. Our results apply to fidelity terms such as Wasserstein distances, <i>φ</i>-divergences, norms, as well as sums and infimal convolutions of those.