Geological field data is essential for reconstructing historical conditions on Earth and Mars, finding and developing natural resources, and managing natural hazards. Sedimentary geology relies on a set of patterns in rock outcrops that provide information on where natural environments (like oceans, rivers, deserts, or lakes) existed in the past. This project will develop tools for automated interpretation of past environments from sedimentary outcrop data. Collaborative evaluation of outcrop datasets by geoscientists and computer scientists will yield new perspectives on how geological outcrop interpretation can be accomplished and provide a deeper understanding of the information required for automated interpretation of field data. Automating outcrop interpretation will broaden researchers' ability to rapidly mine existing and new digital datasets for information that will contribute new insights into the history of surface conditions on Earth and Mars. The tools developed as part of this project will be broadly disseminated and will integrate with existing digital platforms for field data. These tools will help facilitate collaborations among a broad range of geoscientists, including those who can't easily conduct fieldwork in remote locations. This project will strengthen workforce development by cross-training sedimentary geologists with advanced computer science skills and will provide examples of practical applications of machine-learning approaches for computer science students.
To accomplish these goals, this project will leverage existing digital outcrop datasets and collect a new targeted dataset to explore automated approaches for extracting sedimentary features from outcrop image and surface-topography data. The project will focus on extracting sedimentary features that are critical for paleoenvironmental reconstruction, including types of cross bedding, and investigate how outcrop quality, scale, and orientation influence the recoverability of sedimentary features. Additionally, this project will explore the degree to which the three-dimensional orientation of sedimentary features can be extrapolated from automated outcrop observations. The tools developed in this project will be tested with structural geology and geomorphology outcrop datasets to evaluate how workflows developed as part of this work could aid a broader range of geoscience disciplines. Results will be packaged as an accessible online tool that can be used in combination with digital field data repositories. Widespread dissemination of this tool and broad community participation will strengthen the algorithm and improve the community resource over time. Educational materials suitable for undergraduate sedimentary geology courses will be developed and will help expand undergraduate geosciences students' exposure to computer science.
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.
|Effective start/end date||6/1/20 → 5/31/23|
- National Science Foundation: $262,884.00