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

随机信息融合方法的开发同化电阻率层析成像，高频探地雷达，中频雷达，气动/气体示踪剂层析成像，水力/示踪剂层析成像裂缝，表征水文地球物理特性，并监测包气带的自然过程。 信息技术研究将开发：（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场/多模型计算的并行自适应计算引擎;（3）用于主动和被动运行时适应和优化地下地球物理和水文模型的自适应运行时机制;以及（4）远程通信的技术和基础设施随机融合方法和信息技术的结合可以使水文学家和水文学家的能力达到一个新的水平，使他们能够““以比今天更大的深度和分辨率观察地球。 此外，新的计算策略将使无法访问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，它是技术转让的有效范例。

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.