Collaborative Research: Community tools for automated paleoenvironmental interpretation from sedimentary field data

地质现场数据对于重建地球和火星的历史条件、寻找和开发自然资源以及管理自然灾害至关重要。沉积地质学依赖于岩石露头的一系列模式，这些模式提供了过去自然环境（如海洋、河流、沙漠或湖泊）存在的信息。该项目将开发工具，根据沉积露头数据自动解释过去的环境。地球科学家和计算机科学家对露头数据集的协同评估将为如何完成地质露头解释提供新的视角，并为现场数据的自动解释提供更深入的信息理解。自动化的露头解释将扩大研究人员快速挖掘现有和新的数字数据集的能力，这些数据集将为了解地球和火星表面条件的历史提供新的见解。作为该项目的一部分开发的工具将广泛传播，并将与现有的现场数据数字平台集成。这些工具将有助于促进广泛的地球科学家之间的合作，包括那些无法在偏远地区进行实地考察的科学家。该项目将通过交叉培训具有先进计算机科学技能的沉积地质学家来加强劳动力发展，并将为计算机科学专业的学生提供机器学习方法的实际应用示例。为了实现这些目标，该项目将利用现有的数字露头数据集，收集一个新的目标数据集，探索从露头图像和地表地形数据中提取沉积特征的自动化方法。该项目将重点提取对古环境重建至关重要的沉积特征，包括交叉层理类型，并研究露头质量、规模和方向如何影响沉积特征的可恢复性。此外，该项目将探索从自动露头观测中推断沉积特征的三维方向的程度。该项目开发的工具将在构造地质学和地貌学露头数据集上进行测试，以评估作为该工作的一部分开发的工作流程如何能够帮助更广泛的地球科学学科。结果将打包为可访问的在线工具，可与数字现场数据存储库结合使用。随着时间的推移，该工具的广泛传播和广泛的社区参与将加强算法并改善社区资源。将开发适合本科沉积地质学课程的教育材料，并将帮助扩大地球科学本科学生对计算机科学的接触。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.