Collaborative Research: SEI (EAR): Adaptive Fusion of Stochastic Information for Imaging Fractured Vadose Zones

合作研究：SEI (EAR)：随机信息的自适应融合，用于破裂渗流区成像

• 批准号: 0430826
• 负责人: Manish Parashar
• 金额: $ 27.81万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-15 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0430826&HistoricalAwards=false
• 关键词: Collaborative; Research; SEI; EAR; Adaptive

A stochastic information fusion methodology is developed to assimilate electrical resistivity tomography, high-frequency ground penetrating radar, mid-range-frequency radar, pneumatic/gas tracer tomography, and hydraulic/tracer tomography to image fractures, characterize hydrogeophysical properties, and monitor natural processes in the vadose zone. The information technology research will develop: (1) mechanisms and algorithms for fusion of large data volumes; (2) parallel adaptive computational engines supporting parallel adaptive algorithms and multi-physics/multi-model computations; (3) adaptive runtime mechanisms for proactive and reactive runtime adaptation and optimization of geophysical and hydrological models of the subsurface; and (4) technologies and infrastructure for remote (pervasive) and collaborative access to computational capabilities for monitoring subsurface processes through interactive visualization tools.The combination of the stochastic fusion approach and information technology can lead to a new level of capability for both hydrologists and geophysicists enabling them to "see" into the earth at greater depths and resolutions than is possible today. Furthermore, the new computing strategies will make high resolution and large-scale hydrological and geophysical modeling feasible for the private sector, scientists, and engineers who are unable to access supercomputers, i.e., it is an effective paradigm for technology transfer.

发展了一种随机信息融合方法，以同化电阻率层析成像、高频探地雷达、中频雷达、气动/气体示踪层析成像和水力/示踪层析成像，以成像裂缝，表征水文地球物理性质，并监测包气带的自然过程。信息技术研究将发展：(1)大数据量融合的机制和算法；(2)支持并行自适应算法和多物理/多模型计算的并行自适应计算引擎；(3)用于地下地球物理和水文模型的主动和反应性运行时适应和优化的自适应运行时机制；以及(4)通过交互式可视化工具远程(普遍)和协作获取监测地下过程的计算能力的技术和基础设施。随机融合方法和信息技术的结合可使水文学家和地球物理学家的能力达到一个新的水平，使他们能够以比今天更深和更高的分辨率“看到”地球。此外，新的计算战略将使无法使用超级计算机的私营部门、科学家和工程师能够进行高分辨率和大规模的水文和地球物理建模，也就是说，这是一种有效的技术转让范例。