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Constraining GPR data inversion using hydrodynamic laws for noninvasive soil hydraulic and electric property determination

机译:使用流体力学定律约束GPR数据反演以测定非侵入性土壤水力和电特性

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We constrain full-wave inversion of time-lapse radar data using hydrodynamic modeling to simultaneously identify the shallow subsurface hydraulic properties and continuous vertical electric profiles. Radar data are acquired in the frequency domain using a vector network analyzer combined with an off-ground monostatic antenna. This permits to accurately filter antenna effects and to derive Green functions from which the inversion is initiated. In order to demonstrate that enough information is contained in the radar data so as to ensure unique estimates, hydrodynamic events were simulated for three different textured soils, namely, coarse, medium, and fine. The corresponding time-lapse radar data were subsequently computed and inverted to find back key soil hydraulic parameters, i.e., 驴, n, and Ks in Mualem-van Genuchten''s model. For the three scenarios considered, the three hydraulic parameters were exactly retrieved, and hence, the corresponding time-dependent electric profiles as well. Provided that the soil-specific relations between the soil water content and its electric properties and the hydrodynamic initial and boundary conditions are known, the proposed method appears to be promising for proximal mapping of the shallow subsurface hydraulic properties and monitoring of the water dynamics at the field scale.
机译:我们使用流体动力学建模约束时间流逝雷达数据的全波反转,同时识别浅地下液压性能和连续垂直电谱。使用矢量网络分析仪与偏离地单色天线组合的频域中获取雷达数据。这允许准确地过滤天线效应并导出从中启动反转的绿色函数。为了证明在雷达数据中包含足够的信息以确保独特的估计,模拟了三种不同纹理的土壤的流体动力事件,即粗糙,培养基和细。随后计算并反转相应的时间流逝雷达数据以在Mualem-Van Genuchten的模型中查找背部键土液压参数,即驴,n和ks。对于考虑的三种场景,完全检索了三个液压参数,因此也是相应的时间依赖的电谱。此外,已知土壤含水量及其电性能与水动力学初始和边界条件之间的土壤特异性关系,所提出的方法似乎是对浅层地下液压特性的近端映射以及对水动力学的监测场比例。

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