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

为了预测地下污染物的去向和迁移，我们需要一个准确的地下水文地质特性模型。我们的研究重点是利用探地雷达数据建立基于相的水文地质模型。我们所采用的方法建立在雷达相分析的概念上，即将雷达数据划分为在性质或外观上相似的区域。我们的工作假设是，雷达相的空间分布可以与岩相的空间分布相关联，并通过给定位置的岩相与水相之间的关系来关联到水相。我们研究的目的是确定获取雷达相地下模型的最佳方法，并使用该方法来开发水下水面模型。我们的首要目标是开发一种雷达相分类和识别的方法。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在一个特征良好的地点确定雷达相与岩相和水相的关系。我们选择在加拿大安大略省的Borden地下水研究地点工作，在那里，与Richelle Allen-King和来自华盛顿州立大学的同事的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。

0314360Allen-KingIn 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.