✦ LIBER ✦

Data and model uncertainty estimation for linear inversion

✍ Scribed by Kasper van Wijk; John A. Scales; William Navidi; Luis Tenorio

Publisher: John Wiley and Sons
Year: 2002
Tongue: English
Weight: 158 KB
Volume: 149
Category: Article
ISSN: 0956-540X
DOI: 10.1046/j.1365-246x.2002.01660.x

No coin nor oath required. For personal study only.

✦ Synopsis

Summary

Inverse theory concerns the problem of making inferences about physical systems from indirect noisy measurements. Information about the errors in the observations is essential to solve any inverse problem, otherwise it is impossible to say when a feature ‘fits the data’. In practice, however, one seldom has a direct estimate of the data errors. We exploit the trade-off between data prediction and model or data structure to determine both model-independent and modelbased estimates of the noise characteristics from a single realization of the data. Noise estimates are then used to characterize the set of reasonable models that fit the data, for example, by bintersecting prior model parameter constraints with the set of data fitting models. This prior information can also be used to set bounds on the bias.We illustrate our methods with synthetic examples of vertical seismic profiling and cross-well tomography.

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