### Abstract

- Top of page
- Abstract
- 1. Introduction
- 2. Data Classification and Anchor Definitions
- 3. Inverse Modeling With MAD
- 4. Measurement Errors, Parametric Errors, and Conceptual Modeling Errors
- 5. Placement of Anchors
- 6. Case Study
- 7. Updating With Multiple Data Sets
- 8. MAD vis à vis Other Inversion Methods
- 9. Summary
- Appendix A
- Acknowledgments
- References
- Supporting Information

[1] This paper addresses the inverse problem in spatially variable fields such as hydraulic conductivity in groundwater aquifers or rainfall intensity in hydrology. Common to all these problems is the existence of a complex pattern of spatial variability of the target variables and observations, the multiple sources of data available for characterizing the fields, the complex relations between the observed and target variables and the multiple scales and frequencies of the observations. The method of anchored distributions (MAD) that we propose here is a general Bayesian method of inverse modeling of spatial random fields that addresses this complexity. The central elements of MAD are a modular classification of all relevant data and a new concept called “anchors.” Data types are classified by the way they relate to the target variable, as either local or nonlocal and as either direct or indirect. Anchors are devices for localization of data: they are used to convert nonlocal, indirect data into local distributions of the target variables. The target of the inversion is the derivation of the joint distribution of the anchors and structural parameters, conditional to all measurements, regardless of scale or frequency of measurement. The structural parameters describe large-scale trends of the target variable fields, whereas the anchors capture local inhomogeneities. Following inversion, the joint distribution of anchors and structural parameters is used for generating random fields of the target variable(s) that are conditioned on the nonlocal, indirect data through their anchor representation. We demonstrate MAD through a detailed case study that assimilates point measurements of the conductivity with head measurements from natural gradient flow. The resulting statistical distributions of the parameters are non-Gaussian. Similarly, the moments of the estimates of the hydraulic head are non-Gaussian. We provide an extended discussion of MAD vis à vis other inversion methods, including maximum likelihood and maximum a posteriori with an emphasis on the differences between MAD and the pilot points method.