ABSTRACT

Dr. Andrew J. Moodie has been awarded an NSF EAR Postdoctoral Fellowship to carry out research and education plans at The University of Texas at Austin and Stanford University in collaboration with mentors Dr. Paola Passalacqua and Dr. Jef Caers respectively. This study aims to investigate the properties of sedimentary rocks across multiple scales, to draw connections between large scale geological variability (~10 m) and smaller-scale variability (~10 cm). Geophysical imaging is unable to resolve small-scale geological variability, however, Dr. Moodie expects to constrain this variability using statistical models and observations of larger-scale variability. This research is critical to the sustainability of coastal river-deltas, because these environments are impacted by a limited understanding of subsurface geology and flow pathways that are controlled by small-scale geological variability. Improved understanding of fluid-flow pathways will influence many fields, in particular, results of this research will inform pollutant transport modeling, groundwater resource management, shallow geothermal and carbon sequestration operations, as well as hydrocarbon production. Dr. Moodie?s education plan includes mentoring students, organizing a reading seminar about machine learning in the geosciences, and developing active learning modules for topics in sedimentary rock stratigraphy.

The sustainability of coastal river-deltas is impacted by a multitude of natural and anthropogenic factors, including a limited understanding of subsurface flow patterns. Geological heterogeneity strongly influences flow pathways and thus rates of contaminant transport and groundwater aquifer recharge, which limits our ability to sustainably manage water resources and mitigate health risks in river-delta environments. Smaller-scale subsurface heterogeneity due to channel and bedform dynamics (of less than 1 m) is typically under-constrained, because it is below the resolution that can be imaged by existing geophysical techniques. Theory and some evidence suggest that stratigraphic sequences may be scale invariant, which opens a pathway to constrain smaller-scale heterogeneity via observation of larger-scale heterogeneity. This project directly addresses the question: can patterns and information gleaned from subsurface heterogeneity at one spatial scale be used to constrain uncertainty at another scale? The EAR Postdoctoral Fellowship, will allow Dr. Moodie to 1) rigorously investigate scale invariant properties of stratigraphy, and 2) integrate these findings into a quantitative method to constrain subsurface heterogeneity. He will use a combination of approaches, including field measurement, numerical modeling, statistical data analysis, and machine learning. Measures from information theory will quantify scale invariance in stratigraphy, and an existing generative adversarial neural network method will be modified to map heterogeneity across spatial scales. This project was co-funded by the Sedimentary Geology and Paleobiology program in the Earth Science division (EAR).

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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