Collaborative Research: The Use of Ground Penetrating Radar Data in the Development of Facies-Based Hydrogeologic Models

合作研究：利用探地雷达数据开发基于相的水文地质模型

• 批准号: 0229896
• 负责人: Rosemary Knight
• 金额: --
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-04-01 至 2008-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0229896&HistoricalAwards=false
• 关键词: Collaborative; Research; Use; Ground; Penetrating

0229896KnightIn order to predict the fate and transport of subsurface contaminants we require an accurate model of the subsurface hydrogeologic properties. The focus of our research is the use of ground penetrating radar data for the construction of facies-based hydrogeologic models. The approach we have adopted builds on the concept of radar facies analysis where the radar data are divided into regions that are similar in character or appearance. Our working hypothesis is that the spatial distribution of radar facies can be related to that of lithofacies; and, through the relationship between lithofacies and hydrofacies at a given site, to hydrofacies. The goal of our research is to determine the optimal way to obtain and use a subsurface model of radar facies to develop a subsurface model of hydrofacies.Our first objective is to develop a method for the classification and identification of radar-facies. The method will be based on the use of neural networks and will provide an efficient way of converting a large, complex radar data set to a simplified model of the facies-scale structure of the subsurface. Our second objective is to determine the relationship between radar facies and the lithofacies and hydrofacies at one well-characterized site. We have elected to work at the Borden groundwater research site in Ontario, Canada where collaboration with Richelle Allen-King and co-workers from Washington State University provides us with a unique and exciting opportunity to investigate the link between radar images and subsurface properties. The successful completion of this research will contribute to our fundamental understanding of the link between what is seen in a radar image, and the facies-scale sedimentary structure of the subsurface. The results of this research will be of value to all researchers currently addressing the issue of how best to use radar data as a basis for the development of hydrogeological models

0229896骑士为了预测地下污染物的命运和运输，我们需要一个精确的地下水文地质特性模型。 我们的研究重点是使用地质雷达数据的建设相为基础的水文地质模型。 我们采用的方法建立在雷达相分析的概念上，雷达数据被划分为特征或外观相似的区域。 我们的工作假设是，雷达相的空间分布可以与岩相，并通过在一个给定的网站，岩相和水文相之间的关系，水文相。 我们的研究目标是确定获得和使用雷达相的地下模型来开发水文相的地下模型的最佳方式。我们的第一个目标是开发用于雷达相的分类和识别的方法。 该方法将基于神经网络的使用，并将提供一种将大型、复杂的雷达数据集转换为地下相尺度结构的简化模型的有效方法。 我们的第二个目标是确定雷达相和岩相和水文相之间的关系，在一个良好的特点网站。我们选择在加拿大安大略的Borden地下水研究基地工作，与来自华盛顿州立大学的Richelle Allen-King和同事合作，为我们提供了一个独特而令人兴奋的机会来调查雷达图像和地下属性之间的联系。 这项研究的成功完成将有助于我们从根本上了解雷达图像中所看到的内容与地下相尺度沉积结构之间的联系。 这项研究的结果将对目前正在研究如何最好地利用雷达数据作为开发水文地质模型的基础的所有研究人员具有价值