Probabilistic Evaluation of Geoscientific Hypotheses With Geophysical Data: Application to Electrical Resistivity Imaging of a Fractured Bedrock Zone

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.

Automated summary

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.