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A comparison of sequential and integrated data fusion for estimating hydrologic properties during a synthetic GPR monitored infiltration event.

机译:顺序和综合数据融合的比较,用于估计合成GPR监测的入渗事件期间的水文特性。

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Constraining parameters that govern variably saturated flow is important for applications ranging from quantifying water availability for ecosystems to constraining recharge rates and contaminant fluxes to groundwater. In this study I explore the effectiveness of sequential versus integrated data fusion for estimating unsaturated flow parameters using ground penetrating radar (GPR) data. In Sequential Data Fusion (SDF), geophysical imaging is used to create a map of the geophysical properties of the subsurface. These properties are then transformed to hydrologic properties that can be used to constrain an independent hydrologic inverse problem. In contrast, Integrated Data Fusion (IDF) uses the geophysical data to directly constrain hydrologic properties of interest without performing the intermediate geophysical imaging step. The comparison of SDF and IDF is performed for a synthetic study of 2D infiltration into a homogeneous soil from a constant flux point source located at the ground surface. The focus is on results for the estimation of intrinsic permeability (k) from cross-borehole GPR traveltimes collected throughout the duration of the infiltration event. The target permeability (k=7.4x10-12m 2) is uniform over the 20 meter by 20 meter area modeled in this study; though the soil is homogeneous, water content is both spatially variable and transient. I use TOUGH2 to simulate infiltration, MATLAB to simulate GPR traveltimes, and PEST to perform the parameter estimation. To quantitatively compare SDF and IDF, I calculate the normalized error in estimated permeability for each method. In my study, I investigated the performance of the data fusion methods under varying survey geometries by changing the antenna spacing. In all cases I have found that IDF significantly outperforms SDF. For large antenna separations (1.7--6.7m) SDF produces an average error in estimated permeability of 73% while IDF errors are only 6%. As ray density is increased for antenna separations of 1.0--1.5m, average estimation error for SDF drops to 72%, but is reduced to only 3% for IDF. Also, SDF estimates are consistently biased lower than the target value, while IDF results are unbiased. My results suggest the IDF is a powerful new approach for hydrologic characterization of the subsurface using geophysical measurements.
机译:约束可变的饱和流量的参数对于从量化生态系统可用水量到约束补给率和污染物向地下水的通量等应用非常重要。在这项研究中,我探索了顺序与综合数据融合在使用探地雷达(GPR)数据估算非饱和流动参数方面的有效性。在顺序数据融合(SDF)中,地球物理成像用于创建地下地球物理属性的地图。然后将这些属性转换为可用于约束独立水文逆问题的水文属性。相反,集成数据融合(IDF)使用地球物理数据直接约束感兴趣的水文特性,而无需执行中间地球物理成像步骤。 SDF和IDF的比较是对位于地表的恒定流量点源对2D渗透入均质土壤的综合研究。重点是根据渗透事件持续时间内收集的跨孔GPR行进时间估算固有渗透率(k)的结果。在本研究中建模的20米x 20米区域内,目标渗透率(k = 7.4x10-12m 2)是均匀的;尽管土壤是均质的,但是水分在空间上是可变的并且是瞬时的。我使用TOUGH2来模拟渗透,使用MATLAB来模拟GPR传播时间,并使用PEST来执行参数估计。为了定量比较SDF和IDF,我计算了每种方法的估计渗透率的归一化误差。在我的研究中,我通过改变天线间距研究了数据融合方法在不同测量几何形状下的性能。在所有情况下,我都发现IDF明显优于SDF。对于较大的天线间隔(1.7--6.7m),SDF的估计磁导率平均误差为73%,而IDF误差仅为6%。当天线间距为1.0--1.5m时,随着射线密度的增加,SDF的平均估计误差降至72%,而IDF的平均估计误差降至3%。同样,SDF估计值始终偏向低于目标值,而IDF结果则没有偏见。我的结果表明,IDF是一种利用地球物理测量方法对地下水文进行表征的强大新方法。

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