Quo Vadis? Inakeyaa! Inferring Flow Directions Using a Bayesian Approach

Randal J. Barnes, Richard Soule

Research output: Contribution to journalConference articlepeer-review

Abstract

Before we build a predictive groundwater flow model, we first identify candidate conceptual models. To cull our collection of candidate models, we ask the seemingly simple but key question: Where is the water coming from? We argue for the use of a Bayesian framework to address this question. We start with an uninformed prior distribution that says all directions are equally likely. We then incorporate the available information (usually error-prone, noisy information of varied quality) and appropriately update the characterization of the uncertain direction. When the added information is extensive and internally consistent, a clear flow direction emerges. On the other hand, if the added information is minimal or internally inconsistent, the uninformed prior is only slightly modified.

Original languageEnglish (US)
Pages (from-to)78-86
Number of pages9
JournalGeotechnical Special Publication
Volume2020-February
Issue numberGSP 321
DOIs
StatePublished - Jan 1 2020
EventGeo-Congress 2020: University of Minnesota 68th Annual Geotechnical Engineering Conference - Minneapolis, United States
Duration: Feb 25 2020Feb 28 2020

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