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

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。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盖下的岩石类型。沉积盆地是过去沉积岩石聚集的大片区域，通常位于古代海洋之下。已经观察到，在冰下面有沉积物的地方，冰可以流动得更快。这个项目试图了解冰下面有什么，以及下面的岩石如何影响冰流。它是硬的，水晶般的岩石吗？它是沉积盆地吗？沉积物和冰流之间有什么关系？这些问题的答案将结合现有数据和地球物理方法加以解决。来自知名岩石类型的信息将被用来训练计算机，通过使用一种名为机器学习的人工智能应用程序来识别这些特征，这将有助于描述和识别冰下未知的沉积盆地。这个项目的结果将通过举办教师和学生研讨会向广大受众传播，解释我们在冰下的发现，并介绍机器学习技术。在这个项目中使用的开放源码将用于更高级别的教室以及进一步的研究。到目前为止，还没有开发出南极陆上和近海沉积盆地的全面分布。大型数据集的组合将被用来描述已知盆地的特征和确定新的沉积盆地，以制作第一张全大陆范围的沉积盆地图，并提供改进的基本参数条件，有可能支持更现实的冰盖模型。将使用现有的数据地球物理汇编和著名沉积盆地的位置来应用综合机器学习算法。机器学习算法将通过在一组随机决策树中投票来学习复杂的关系。通过计算反映密度不均匀的重力残差，将评估从其他(例如地震)技术中已知的与沉积盆地有关的重力信号，并从航空重力数据回归分析中提出未知盆地。通过回归分析确定的重力沉积盆地将与识别沉积分布的独立方法--估计磁源深度的Werner反褶积方法进行比较。这一假设是，沉积盆地与快速冰流行为相关，将通过利用现有的冰速观测将沉积盆地的位置与高冰流位置进行比较来检验这一假设。将定性和定量地定义沉积盆地和冰流之间的关系，目的是评估是否存在没有报告沉积盆地的冰流，或者是否存在与冰流无关的沉积盆地。这些项目的发现可以证实，丰富沉积物的存在是否是冰流的先决条件。分析以前已知的沉积盆地并在南极洲识别新的沉积盆地，是评估冰下沉积物对冰盖流动的影响的核心。这一奖项反映了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。

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.