EAR-PF: Quantifying heterogeneity in stratigraphy across scales

安德鲁·J·穆迪博士获得了NSF EAR博士后奖学金，将分别与导师Paola Passalacqua博士和Jef Caers博士合作，在德克萨斯大学奥斯汀分校和斯坦福大学开展研究和教育计划。本研究旨在研究沉积岩在多个尺度上的性质，找出大尺度(~10m)地质变异性与小尺度(~10 cm)变异性之间的联系。地球物理成像无法解决小尺度的地质变异性，然而，穆迪博士希望通过统计模型和对更大尺度变异性的观察来限制这种变异性。这项研究对沿海河流三角洲的可持续性至关重要，因为这些环境受到对地下地质和小规模地质变异性控制的流动路径的有限了解的影响。更好地了解流体流动路径将影响许多领域，特别是这项研究的结果将为污染物传输模拟、地下水资源管理、浅层地热和碳汇作业以及碳氢化合物生产提供信息。穆迪博士的教育计划包括指导学生，组织一次关于地球科学中机器学习的阅读研讨会，以及为沉积岩地层学主题开发积极的学习模块。沿海河流三角洲的可持续性受到多种自然和人为因素的影响，包括对地下水流模式的有限了解。地质异质性严重影响水流路径，从而影响污染物运移和地下水含水层补给的速度，这限制了我们在河流-三角洲环境中可持续管理水资源和减轻健康风险的能力。由于河道和河床动力学(不到1米)造成的较小尺度的地下非均质性通常是约束不足的，因为它低于现有地球物理技术可以成像的分辨率。理论和一些证据表明，地层层序可能具有尺度不变性，这为通过观察较大尺度的非均质性来约束较小尺度的非均质性开辟了一条途径。这个项目直接解决了这个问题：从一个空间尺度上的地下异质性收集的模式和信息能否被用来限制另一个尺度上的不确定性？EAR博士后奖学金将使Moodie博士能够1)严格研究地层学的尺度不变性，2)将这些发现整合到一种量化方法中，以限制地下的非均质性。他将使用多种方法，包括现场测量、数值建模、统计数据分析和机器学习。信息论的方法将量化地层学中的尺度不变性，现有的生成性对抗神经网络方法将被修改以映射空间尺度上的异质性。该项目由地球科学部(EAR)的沉积地质学和古生物学项目共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.