Articles | Volume 17, issue 23
https://doi.org/10.5194/gmd-17-8853-2024
https://doi.org/10.5194/gmd-17-8853-2024
Development and technical paper
 | 
12 Dec 2024
Development and technical paper |  | 12 Dec 2024

A joint reconstruction and model selection approach for large-scale linear inverse modeling (msHyBR v2)

Malena Sabaté Landman, Julianne Chung, Jiahua Jiang, Scot M. Miller, and Arvind K. Saibaba

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Short summary
Making an informed decision about what prior information to incorporate or discard in an inverse model is important yet very challenging, as it is often not straightforward to distinguish between informative and non-informative variables. In this study, we develop a new approach for incorporating prior information in an inverse model using predictor variables, while simultaneously selecting the relevant predictor variables for the estimation of the unknown quantity of interest. 
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