Statistics of porous media attributes and mixing processes state variables across scales

对多孔介质属性的典型观测来自于各种技术，这些技术具有自己的空间分辨率和相关的不确定性。因此，驱动多孔介质中传输过程的参数的关键统计数据因尺度而异，描述基本传输机制的科学基础需要了解有关长度和时间尺度上的所有相关过程。在这个项目中，我们开发了一个理论和计算框架来同化在一系列尺度上收集的与不同变量相关的数据，并结合这些数据来提供对溶质动力学和相关不确定性的预测。

Typical observations of porous media attributes arise from a variety of techniques which have their own spatial resolution and associated uncertainty. Therefore, key statistics of parameters driving transport processes in porous media vary across scales and a scientific foundation for the characterization of basic transport mechanisms requires understanding of all relevant processes across the relevant length and time scales. In this project, we develop a theoretical and computational framework to assimilate data associated with diverse variables collected at a range of scales and combine these to provide predictions of solute dynamics and associated uncertainties.