Prediction of Solar Eruptions with Machine-Learning Algorithms Combining Physical Models and Observations

利用结合物理模型和观测的机器学习算法预测太阳喷发

基本信息

批准号：
1922713
负责人：
Jon Hoeksema
金额：
$ 49.47万
依托单位：
Stanford University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-01 至 2024-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1922713&HistoricalAwards=false
关键词：
Prediction Solar Eruptions Machine Learning

项目摘要

Space-weather prediction has been around for many decades but is reaching new heights due to the advent of more data, sophisticated data processing techniques, and computing power. Modern satellites and ground-based telescopes designed to study space weather take more data than ever before. The Solar Dynamics Observatory (SDO), for example, acquires ~1.5 terabytes of data a day. As such, the time may be right to give an affirmative answer to the question: Can we harness the data revolution to effectively predict the onset of a major solar flare? The main purpose of this three-year project is to answer this question by bringing together an interdisciplinary team of computer scientists and solar physicists to analyze data taken by the SDO, Global Oscillation Network Group (GONG), and Geostationary Operational Environmental Satellite (GOES) observatories, as well as data products derived from numerical models, using machine-learning algorithms to characterize and understand which signatures indicate the imminent eruption of a solar flare. Previous research studies have attempted to predict solar flares using a subset of these components, but not all of them. Few space-weather studies have harnessed the data revolution to predict space-weather using machine learning algorithms despite the vast amount of data available. This three-year project addresses open questions in solar flare physics. First, the project aims to determine which features contribute to local and global pre-flare signatures. While interaction between active regions can trigger flaring behavior, hyper-local phenomena can also trigger flaring behavior. Second, the project team will explore how the rate of change of any given feature influences eruptive activity on the Sun. Finally, the project aims to identify the timescales on which each individual feature best predicts future flaring activity. To accomplish these goals, the project team will use the relevant features identified in these three tasks, along with interpretable machine learning algorithms, such as state-space models, to predict solar activity. Space weather prediction is a national priority, listed as a key goal of the most recent decadal survey and a prime focus of the National Space-Weather Action Plan. This three-year project aims to build an open-source, well-documented, unified, reproducible, and operational feature dataset and machine-learning model. The project team will use this dataset and machine-learning model to teach students both through summer internships and via their book entitled Statistics, Data Mining, and Machine Learning in Heliophysics. The research and EPO agenda of this project supports the Strategic Goals of the AGS Division in discovery, learning, diversity, and interdisciplinary research.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.

空间天气的预测已经存在了数十年，但是由于更多数据，复杂的数据处理技术和计算能力的出现，正达到新的高度。旨在研究太空天气的现代卫星和地面望远镜比以往任何时候都多。例如，太阳动力学天文台（SDO）每天获取约1.5吨数据。因此，时间可能是正确回答这个问题的肯定答案的时机：我们可以利用数据革命有效预测主要太阳耀斑的发作吗？这个为期三年项目的主要目的是通过将计算机科学家和太阳能物理学家组成的跨学科团队组合在一起，分析SDO，全球振荡网络组（GONG）和地球上操作环境卫星卫星（GOOS）观测产品的数据，并使用来自机器实现的Algorning Algorning Algorning Algorning Algorning Algorning Algorning Algorning Algorning Algorning Algorning Algorning Algorning Algorning Algorning Algorning Algorning Algorning Algorning Algorning Algorning Algorning Algorning Algorning Algorning Algorning Algorning Algorning Algorning Algorning Algorning Algorning Algorning a的数据，并太阳耀斑。先前的研究试图使用这些组件的子集预测太阳耀斑，但并非全部。尽管有大量数据可用，但很少有空间天气研究利用机器学习算法来预测使用机器学习算法的空间天气。这个为期三年的项目解决了太阳耀斑物理学的开放问题。首先，该项目旨在确定哪些功能有助于本地和全球范围的签名。虽然活动区域之间的相互作用可以触发耀斑的行为，但超本地现象也会触发耀斑的行为。其次，项目团队将探讨任何给定功能的变化速率如何影响太阳的爆发活动。最后，该项目旨在确定每个人最能预测未来耀斑活动的时间尺度。为了实现这些目标，项目团队将使用这三个任务中确定的相关功能以及可解释的机器学习算法（例如州空间模型）来预测太阳能活动。太空天气预测是国家优先事项，被列为最新衰落调查的主要目标，也是国家太空天气行动计划的主要重点。这个为期三年的项目旨在建立一个开源，有据可查的，统一的，可重复的和操作的功能数据集和机器学习模型。项目团队将使用此数据集和机器学习模型通过暑期实习以及他们的书籍中的统计数据，数据挖掘和Heliophyophics中的机器学习来教授学生。该项目的研究和EPO议程支持了AGS部门在发现，学习，多样性和跨学科研究方面的战略目标。该奖项反映了NSF的法定任务，并认为值得通过基金会的知识分子优点和更广泛影响的评估标准通过评估来获得支持。