🤖 AI Summary
This work addresses the challenge of coexisting structural ambiguity—arising from non-injectivity of the forward model—and probabilistic ambiguity—stemming from parameter uncertainty—in stochastic inverse problems involving nonlinear parameter dependencies and observational uncertainties. To tackle this, the authors propose a unified modeling framework that couples both types of ambiguity for the first time by representing parameter uncertainty through a mixture of probability densities and formulating a Bayesian inversion-based posterior inference algorithm. The approach is validated on one- and two-dimensional quadratic forward models, demonstrating its ability to accurately resolve probabilistic ambiguity, visualize residual structural ambiguity, and handle both finite and infinite solution sets. Furthermore, it reveals the interaction mechanisms between the two forms of ambiguity within the posterior distribution.
📝 Abstract
In this concise paper, we investigate a computational class of random inverse problems that incorporates model uncertainties through random variable parameters nonlinearly in the forward model as well as additive observational uncertainty. Random inverse problems with nonlinear parameter dependencies may arise in engineering, geophysics, image processing or uncertainty quantification. We introduce a new perspective on structural ambiguities due to the non-injectivity of the forward model together with probabilistic ambiguities by assigning mixture model densities with separate components to the parameters, which leads to a possibly complex forward model, observation model and posterior. As a result, the mixture-model parameters in the forward model can be interpreted as simultaneously describing aspects of both the nonlinear ambiguity and the uncertainty of the inverse problem. The underlying solution algorithm is presented based on Bayesian inversion for three observation scenarios leading to posterior densities for the input for given output samples or an observed output density. By applying the derived algorithms to 1D and 2D quadratic models, we numerically demonstrate in which scenarios the proposed algorithm can resolve probabilistic ambiguities in the solution of the random inverse problem. It is demonstrated that making the residual structural ambiguities visible in the posterior and showing the interplay with probabilistic ambiguities is a relevant perspective, including cases with a finite and an infinite number of solutions.