The inverse crime

TL;DR

The paper discusses the inverse crime, which occurs when the same models are used for data synthesis and inversion in inverse problems, highlighting its triviality and the importance of avoiding it.

Contribution

It clarifies the concept of the inverse crime and emphasizes its implications in inverse problem methodologies.

Findings

Using identical models for synthesis and inversion can lead to overly optimistic results.

Avoiding the inverse crime is crucial for realistic inverse problem analysis.

The paper underscores the importance of model discrepancy in inverse problem solutions.

Abstract

The inverse crime occurs when the same (or very nearly the same) theoretical ingredients are employed to synthesize as well as to invert data in an inverse problem. This act has been qualified as trivial and therefore to be avoided by Colton and Kress.