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Quantitative integration of high-resolution hydrogeophysical data:a novel approach to Monte-Carlo-type conditional stochastic simulations and implications for hydrological predictions

机译:高分辨率水文地球物理数据的定量整合:蒙特卡洛型条件随机模拟的一种新方法及其对水文预测的启示

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Geophysical techniques can help to bridge the gap that exists with regard to spatial resolution and coverage for classical hydrological methods.This has lead to the emergence of new and rapidly growing research domain generally referred to as hydrogeophysics.Given the differing sensitivities of various geophysical techniques to hydrologically relevant parameters and their inherent trade-off between resolution and range as well as the notoriously site-specific nature of petrophysical parameter relations,the fundamental usefulness of multi-method hydrogeophysical surveys for reducing uncertainties in data analysis and interpretation is widely accepted.A major challenge arising from such endeavors is the quantitative integration of the resulting generally vast and often diverse database in order to obtain a unified model of the probed subsurface region that is internally consistent with all available data.In this contribution,we present a novel approach towards hydrogeophysical data integration based on Monte-Carlo-type conditional stochastic simulation that we consider to be particularly suitable for high-resolution and high-quality datasets.Monte-Carlo-based optimization techniques are immensely flexible and versatile,allow for accounting for a wide variety of data and constraints of vastly differing resolution and hardness and thus have the potential of providing,in a geostatistical sense,highly detailed and realistic models of the pertinent target parameter distributions.Compared to more conventional approaches of this kind,our novel approach provides significant advancements in the way that large-scale structural information from the hydrogeophysical data can be accounted for,which represents an inherently problematic,and as of yet unresolved,aspect of Monte-Carlo-type conditional simulation techniques.We present the results of applying our algorithm to the integration of porosity log and tomographic crosshole georadar data to generate stochastic realizations of the local-scale porosity structure.Our procedure is first tested on pertinent synthetic data,and then applied to a field dataset collected at the Boise Hydrogeophysical Research Site near Boise,Idaho,USA.Finally,we compare the performance our approach to hydrogeophysical data integration to that of more conventional methods with regard to the prediction of flow and transport phenomena in highly heterogeneous media.
机译:地球物理技术可以帮助弥合经典水文方法在空间分辨率和覆盖范围方面存在的差距,这导致出现了新的且迅速增长的研究领域,通常被称为水文地球物理学。水文相关参数及其在分辨率和范围之间的固有权衡,以及岩石物理参数关系众所周知的特定地点性质,多方法水文地球物理调查对于减少数据分析和解释中的不确定性的基本有用性已被广泛接受。此类工作带来的挑战是对所得的通常庞大且通常不同的数据库进行定量集成,以便获得与所有可用数据内部一致的被探测地下区域的统一模型。在此贡献中,我们提出了一种针对水文地球物理的新颖方法数据完整性基于Monte-Carlo类型的条件随机模拟的比率,我们认为它特别适合于高分辨率和高质量数据集。基于Monte-Carlo的优化技术非常灵活和通用,可以处理各种数据以及分辨率和硬度差异很大的约束条件,因此有可能在地统计学意义上提供有关目标参数分布的高度详细和逼真的模型。与这种更常规的方法相比,我们的新颖方法在可以解释水文地球物理数据中的大规模结构信息的方法,这代表了固有的问题,并且尚未解决,这是蒙特卡洛型条件模拟技术的一个方面。我们介绍了将我们的算法应用于整合的结果孔隙度测井和断层层析成像的跨孔地雷达数据生成t的随机实现我们首先对相关合成数据进行了测试,然后将其应用到在美国爱达荷州博伊西附近的博伊西水文地球物理研究中心收集的现场数据集。最后,我们比较了我们的水文地球物理数据集成方法的性能。在预测高度异构介质中的流动和传输现象方面,与传统方法相比有所不同。

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