Journal
JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
Volume 126, Issue 9, Pages -Publisher
AMER GEOPHYSICAL UNION
DOI: 10.1029/2021JB021767
Keywords
electrical resistivity; fractures; groundwater; hypothesis testing; uncertainty quantification
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Funding
- U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research [DE-AC02-05CH11231]
- U.S. Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists, Office of Science Graduate Student Research (SCGSR) program
- Oak Ridge Institute for Science and Education [DE-SC0014664]
- Stanford Center for Earth Resources Forecasting and the Dean (Prof. S. Graham) of the Stanford School of Earth, Energy and Environmental Sciences
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As climate changes and populations grow, groundwater sustainability becomes more crucial. Hydrogeologic models are important tools for decision-making, but a common issue arises when multiple geological phenomena could explain a single subsurface parameterization.
As climate changes and populations grow, groundwater sustainability is becoming increasingly important. Hydrogeologic models, which are based on a conceptual understanding of the subsurface, are crucial tools for informing decisions. Conceptual models of the subsurface incorporate knowledge of geological processes, and, frequently, observations from geophysical data into a common subsurface parameterization where the parameters may still be uncertain. Many methods exist to test how different conceptual subsurface parameterizations compare to geophysical data, but a frequent problem in hydrogeologic model development occurs when multiple geological phenomena could explain a single subsurface parameterization. In this work, we present a framework for testing geological hypotheses in conditions where a geological feature is observed in geophysical data, but its physical characteristics are uncertain. The framework uses Popper-Bayes methods developed in previous studies, and is applied to study a fractured bedrock zone in a mountainous watershed in southwest Colorado. First, we propose six hypotheses based on the geological history of the watershed. Then, using the proposed Popper-Bayes approach, we demonstrate that two of the hypotheses are inconsistent with the electrical resistivity tomography data. Finally, we discuss the importance of the prior model, and in what other scenarios the framework can be applied to.
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