SR2 - Importance of noise estimates when solving inverse problems in the "real" world - Sean Walker
Channel: Software Underground
Date: 05/02/2021

This is the first Subsurface Rendezvous, a new series of online workshops from the Software Underground.

Importance of noise estimates when solving inverse problems in the "real" world - Sean Walker

Got questions for Sean? Join in the conversations on our slack here: https://swung.slack.com/archives/C01LS192J94

Sean made the discussion notes and notebooks available here: https://curvenote.com/@swalker/swung-rendez-vous-noise-estimates/discussion-notes


Deterministic inverse problems are commonly set up to recover a model with certain characteristics (determined by model constraints) that can produce a response that fits the observed data to a particular level (determined by data constraints). Many of the inversion algorithms described in papers and included in software packages provide numerous options for model constraints. However, the same level of attention is not afforded to data constraints. Most algorithms provide little guidance on how to determine or refine the error estimates used to calculate the data misfit. These values can cause portions of the data set to be either over or underfit and will have a significant impact on the recovered model. I have always found it unsatisfying to simply choose an arbitrary value and then refine it based on my feeling about the model. To bring some rigour to the process I have developed a semi-automated workflow to determine error estimates.