Pan-Antarctic Assessment of Sedimentary Basins and the Onset of Streaming Ice Flow from Machine Learning and Aerogravity Regression Analyses

通过机器学习和航空重力回归分析对沉积盆地和流冰流的发生进行泛南极评估

• 批准号: 2114502
• 负责人: Renata Constantino
• 金额: $ 38.22万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2114502&HistoricalAwards=false
• 关键词: Pan; Antarctic; Assessment; Sedimentary; Basins

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).An important part of understanding future climate change is predicting changes in how fast the ice in Antarctica is moving. If ice flows more quickly towards the ocean, it will have a direct impact on sea level rise. One of the things that can influence the ice flow is the type of rock below the ice coverage in Antarctica. Sedimentary basins are large regions where sedimentary rocks accumulated in the past, often under ancient seas. It has been observed that where there are sediments below the ice, the ice can flow faster. This project seeks to understand what is below the ice and how the underlying rock influences the ice flow. Is it hard, crystalline rock? Is it a sedimentary basin? What is the relationship between sediments and ice flow? The answers to these questions will be addressed by using a combination of available data and geophysical methods. Information from well-known rock-types will be used to train the computer to recognize these features by using an application of artificial intelligence known as machine learning, which will help the characterization and identification of unknown sedimentary basins beneath the ice. The results of this project will be disseminated to a broad audience by holding workshops for teacher and students to explain our findings under the ice and to introduce the machine learning technique. Open-source codes used during this project will be made available for use in higher-level classrooms as well as in further studies.To date, no comprehensive distribution of onshore and offshore sedimentary basins over Antarctica has been developed. A combination of large-scale datasets will be used to characterize known basins and identify new sedimentary basins to produce the first continent-wide mapping of sedimentary basins and provide improved basal parametrizations conditions that have the potential to support more realistic ice sheet models. Available geophysical compilations of data and the location of well-known sedimentary basins will be used to apply an ensemble machine learning algorithm. The machine learning algorithm will learn complex relationships by voting among a collection of randomized decision trees. The gravity signal related to sedimentary basins known from other (e.g. seismic) techniques will be evaluated and unknown basins from aerogravity data regression analyses will be proposed by calculating a gravity residual that reflects density inhomogeneities. The gravimetric sedimentary basins identified from the regression analyses will be compared with an independent method of identifying sedimentary distribution, the Werner deconvolution method of estimating depth to magnetic sources. The hypothesis, which is sedimentary basins are correlated to fast ice flow behavior, will be tested by comparing the location of the sedimentary basins with locations of high ice flow by using available ice velocity observations. A relationship between sedimentary basins and ice streams will be defined qualitatively and quantitatively, aiming to evaluate if there are ice streams where no sedimentary basins are reported, or sedimentary basins with no ice streams related. The findings of these project can confirm if the presence of abundant sediments is a pre-requisite for ice streaming. Analyzing previously known sedimentary basins and identifying new ones in Antarctica is central to evaluating the influence of subglacial sediments on the ice sheet flow.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.

该奖项的全部或部分资金根据《2021 年美国救援计划法案》（公法 117-2）提供。了解未来气候变化的一个重要部分是预测南极洲冰层移动速度的变化。 如果冰更快地流向海洋，将对海平面上升产生直接影响。影响冰流的因素之一是南极洲冰层下方的岩石类型。沉积盆地是过去沉积岩堆积的大片区域，通常位于古代海洋之下。据观察，冰下方有沉积物的地方，冰可以流动得更快。该项目旨在了解冰层下方的情况以及底层岩石如何影响冰流。它是坚硬的结晶岩石吗？是沉积盆地吗？沉积物和冰流之间有什么关系？这些问题的答案将通过结合现有数据和地球物理方法来解决。来自已知岩石类型的信息将用于训练计算机通过使用称为机器学习的人工智能应用来识别这些特征，这将有助于表征和识别冰下未知的沉积盆地。该项目的成果将通过为教师和学生举办研讨会来向广大受众传播，以解释我们在冰下的发现并介绍机器学习技术。该项目中使用的开源代码将可供更高级别的课堂以及进一步的研究使用。迄今为止，尚未开发出南极洲陆上和近海沉积盆地的全面分布。大规模数据集的组合将用于描述已知盆地的特征并识别新的沉积盆地，以生成第一个全大陆沉积盆地绘图，并提供改进的基础参数化条件，有可能支持更现实的冰盖模型。可用的地球物理数据汇编和著名沉积盆地的位置将用于应用集成机器学习算法。 机器学习算法将通过在随机决策树集合中投票来学习复杂的关系。将评估与其他（例如地震）技术已知的沉积盆地相关的重力信号，并通过计算反映密度不均匀性的重力残差来提出来自航空重力数据回归分析的未知盆地。通过回归分析确定的重力沉积盆地将与确定沉积分布的独立方法（估计磁源深度的维尔纳反卷积方法）进行比较。 沉积盆地与快速冰流行为相关的假设将通过使用现有的冰速观测结果将沉积盆地的位置与高冰流的位置进行比较来检验。将定性和定量地定义沉积盆地和冰流之间的关系，旨在评估是否存在未报告沉积盆地的冰流，或没有相关冰流的沉积盆地。 这些项目的发现可以证实丰富的沉积物的存在是否是冰流的先决条件。分析先前已知的沉积盆地并确定南极洲的新沉积盆地对于评估冰下沉积物对冰盖流动的影响至关重要。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。