Fast level-set methods for large-scale geospatial-information based inverse gravimetry problems and applications to threats detection

基于大规模地理空间信息的反重力问题的快速水平集方法及其在威胁检测中的应用

• 批准号: 1222368
• 负责人: Jianliang Qian
• 金额: $ 38.7万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-15 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1222368&HistoricalAwards=false
• 关键词: Fast; level; set; methods; large

The principal investigator and his colleagues will develop fast level-set methods for large-scale geospatial-information based inverse gravimetry problems and apply these tools for threats detection. They aim at addressing some challenging inverse problems arising from geospatial-intelligence applications and remote sensing from both theoretical and computational perspectives: fast stable numerical continuation of gravitational potentials and fast high-resolution reconstruction of subsurface attracting-source inclusions. First, their proposed fast algorithms for numerical continuation of gravity fields will overcome significant, long-standing difficulties in inverse gravimetry problems because the new methods can be applied to gravitational and magnetic potentials in arbitrary computational domain in any dimension. Second, their proposed fast level-set methods for large-scale geospatial-information based inverse gravimetry problems will be the first of its kind which is able to recover both a domain and its potential density from geospatial-information based gravitational measurements. Third, the new fast method will be developed into efficient algorithms and codes on streaming-architecture based high performance computing platforms. Fourth, the resulting codes will be tested on synthetic data sets and GRACE satellite data sets. Consequently, this new set of powerful computational tools will be developed for the first time for large-scale geospatial-information based inverse gravimetry problems. The project will address some fundamental issues in geospatial sciences, remote sensing, geosciences and scientific computing. The US government has been developing remote sensing technology by deploying advanced radars and satellites to collect geospatial information for many years. Solving the inverse gravimetry problem is one of the many essential tasks to analyze and classify the collected surveillance data. This research endeavor will enable further advances and breakthroughs in the Defense and Homeland Security sector in the form of fast and efficient algorithms for the detection of underground tunnels and caves, including man-made underground structures. This is an example of what this work concerns. Such underground tunnels can exist along the US border. The problem is to determine the existence and location of such underground tunnels. The research done here is relevant to analyzing geospatial information collected by all such applications in threats detection. Students from the PI's institution are involved in this innovative interdisciplinary research project.

首席研究员和他的同事将为基于大规模地理空间信息的反重力问题开发快速水平集方法，并将这些工具应用于威胁检测。他们的目标是从理论和计算的角度解决地理空间情报应用和遥感中产生的一些具有挑战性的反问题：重力势的快速稳定数值延拓和地下吸引源包裹体的快速高分辨率重建。首先，他们提出的重力场数值连续的快速算法将克服反重力问题中长期存在的重大困难，因为新方法可以应用于任何维度的任意计算域中的重力和磁势。其次，他们提出的用于基于大规模地理空间信息的反重力测量问题的快速水平集方法将是同类中的第一个，能够从基于地理空间信息的重力测量中恢复域及其潜在密度。第三，新的快速方法将被开发为基于流式架构的高性能计算平台上的高效算法和代码。第四，生成的代码将在合成数据集和 GRACE 卫星数据集上进行测试。因此，这套新的强大计算工具将首次针对基于大规模地理空间信息的反重力问题而开发。 该项目将解决地理空间科学、遥感、地球科学和科学计算方面的一些基本问题。美国政府多年来一直通过部署先进雷达和卫星来收集地理空间信息来开发遥感技术。解决反重力问题是对收集的监测数据进行分析和分类的众多基本任务之一。这项研究工作将以快速有效的算法检测地下隧道和洞穴（包括人造地下结构）的形式，推动国防和国土安全部门取得进一步的进步和突破。这是这项工作所关注的一个例子。这种地下隧道可以沿着美国边境存在。问题是确定此类地下隧道的存在和位置。这里完成的研究与分析威胁检测中所有此类应用程序收集的地理空间信息相关。 PI 所在机构的学生参与了这一创新的跨学科研究项目。