Understanding the Geospace Phenomena Connected to Localized Perturbations in Earth’s Magnetic Field

了解与地球磁场局部扰动相关的地球空间现象

• 批准号: 2331527
• 负责人: Amy Keesee
• 金额: $ 57.77万
• 依托单位: University of New Hampshire
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2331527&HistoricalAwards=false
• 关键词: Understanding; Geospace; Phenomena; Connected; Localized

During intervals of increased geomagnetic activity, increased currents in geospace can induce currents in the ground or in long, manmade conductors, such as power lines. These geomagnetically induced currents (GICs) can drive power outages and damage power components while also affecting pipelines and train systems. Developing the ability to predict GICs is important to protecting infrastructure and limiting the impact of geomagnetic storms on public safety and the economy. This project addresses GIC prediction by seeking to understand the connection between localized temporal changes in magnetic field measurements (dB/dt) and the magnetospheric and ionospheric phenomena causing them. This work will support the training of two graduate students that will be prepared to enter the STEM workforce with knowledge of space science, space weather, and machine learning and support the career of a woman PI leading the project. This project is jointly funded by the Magnetospheric Physics program, the Established Program to Stimulate Competitive Research (EPSCoR), and the Aeronomy program.Several studies have shown that peaks in dB/dt can be very localized, on the scales of hundreds of km. Thus, forecasting is needed at a localized level to provide power companies with actionable warnings. The current physics-based models used by the Space Weather Prediction Center lack the resolution needed to include physical phenomena at the spatial scales of the dB/dt peaks. Higher resolution models are being used for scientific studies, but are computationally expensive and take longer to run, making them more challenging to use for timely forecasting. An advantage of machine learning (ML) models is that once trained, the runtime to make predictions is significantly lower than physics based direct modeling. ML networks’ ability to model nonlinear relationships in datasets can allow us to better understand the phenomena that result in localized dB/dt through the use of model explainability techniques. The following science questions will be addressed: (1) What are the spatial characteristics of localized magnetic perturbations? (2) What magnetosphere and ionosphere phenomena correlate with localized magnetic perturbations and why? The first question will be tackled using two methods that take advantage of the NSF-funded SuperMAG database: by characterizing the Region to Specific Difference (a parameter that compares the value at one location to the average values within a defined region) and by interpolation with spherical elementary current systems, and then comparing the results. The second question will utilize explainable machine learning techniques to explore magnetosphere and ionosphere phenomena that correlate with the spatially localized perturbations, incorporating datasets from satellites (e.g., DMSP, AMPERE, TWINS, and THEMIS). The results of this work will improve our understanding of geomagnetically active intervals, magnetosphere-ionosphere coupling, and the causes of localized perturbations in the ground magnetic field.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.

在地磁活动增加期间，地球空间中的电流增加会在地面或长的人造导体（例如电力线）中感应出电流，这些地磁感应电流 (GIC) 会导致停电并损坏电力组件，同时还会影响管道和火车。发展预测 GIC 的能力对于保护基础设施和限制地磁风暴对公共安全和经济的影响非常重要。该项目通过寻求了解局部时间之间的联系来解决 GIC 预测问题。这项工作将支持培训两名研究生，他们将具备空间科学、空间天气和机器学习知识，准备进入 STEM 队伍。并支持领导该项目的女性 PI 的职业生涯。该项目由磁层物理项目、刺激竞争性研究既定项目 (EPSCoR) 和航空学项目联合资助。多项研究表明， dB/dt 峰值可能非常局部化，在数百公里的范围内，因此，需要在局部水平上进行预测，以便为电力公司提供可操作的警报。分辨率需要包括 dB/dt 峰值空间尺度的物理现象。更高分辨率的模型正在用于科学研究，但计算成本较高且运行时间较长，这使得它们在及时预测方面更具挑战性。机器学习 (ML) 模型的优点是，一旦经过训练，进行预测的运行时间明显低于基于物理的直接建模，ML 网络对数据集中的非线性关系进行建模的能力可以让我们更好地理解导致局部 dB/ 的现象。通过使用模型可解释性技术，将解决以下科学问题：（1）局部磁扰动的空间特征是什么？（2）什么磁层和电离层现象与局部磁扰动相关？将使用两种利用 NSF 资助的 SuperMAG 数据库的方法来解决：通过表征区域到特定差异（将一个位置的值与定义区域内的平均值进行比较的参数）以及通过球面插值第二个问题将利用可解释的机器学习技术来探索与空间局部扰动相关的磁层和电离层现象，并结合来自卫星的数据集（例如 DMSP、 AMPERE、TWINS 和 THEMIS）。这项工作的结果将提高我们对地磁活动区间、磁层-电离层耦合以及地面磁场局部扰动原因的理解。该奖项反映了 NSF 的法定使命，并被认为是值得的。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来获得支持。