Abstract
Due to the ill-posed nature of inverse problems, the solutions are ambiguous and unstable. There are always many solutions that will fit the observed noisy data practically with the same data misfit. One of the most important questions, arising in the solution of the inverse problem, is the resolution of the regularized inversion. This chapter presents a method for resolution analysis based on evaluating the spatial distribution of the upper bounds of the model variations and introduces an important characteristic of the inversion, resolution density, as an inverse of these upper bounds. We derive an efficient numerical technique to compute the resolution density based on the spectral Lanczos decomposition method.
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