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首页> 外文期刊>Stochastic environmental research and risk assessment >Combining process-based and surface-based models to simulate subsurface heterogeneity in volcanic aquifers
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Combining process-based and surface-based models to simulate subsurface heterogeneity in volcanic aquifers

机译:结合基于过程和基于表面的模型来模拟火山含水层的地下非均质性

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摘要

Realistic models of lithologic structure are critical for predicting flow and transport through heterogeneous volcanic aquifers. Existing models of lava flows based on physical processes are able to realistically simulate flow geometry and lithology, but the computational intensity limits applicability in generating entire aquifers. Fast surface-based models have been developed for hazard mapping, but these do not incorporate 3D geometry or lithology critical for hydrogeologic applications. Here we develop a hybrid modeling method (HMM) based on a combination of a process-based model (PBM) and a surface-based model. The methodologies are presented and compared to a known single flow and to each other in a full aquifer simulation. Results indicate that both the PBM and HMM simulations reasonably reproduce the flow geometry (length, branching, thickness) of the 1984 eruption of Mauna Loa in Hawai'i. Simulations of a volcanic aquifer built from 100 flows with the PBM and HMM are similar in spatial distribution and overall proportions of lithology (aa, transitional, pahoehoe, ash), flow geometry, and aquifer geometry. Thus, the hybrid method is an efficient method to generate geologically realistic models of volcanic aquifer structure. Model realism and parameterization can be improved as more field data become available.
机译:现实的岩性结构模型对于预测通过非均质火山含水层的流量和运移至关重要。现有的基于物理过程的熔岩流模型能够现实地模拟流体的几何形状和岩性,但是计算强度限制了生成整个含水层的适用性。已经开发了基于快速基于表面的模型来进行危害图绘制,但是这些模型并未包含对水文地质应用至关重要的3D几何或岩性。在这里,我们基于过程模型(PBM)和基于表面的模型的组合,开发了一种混合建模方法(HMM)。提出了这些方法,并与已知的单流进行了比较,并在完整的含水层模拟中将它们进行了比较。结果表明,PBM和HMM模拟都合理地再现了1984年夏威夷莫纳罗亚火山喷发的流动几何形状(长度,分支,厚度)。用PBM和HMM从100个流中构建的火山含水层的模拟在空间分布和岩性(aa,过渡,泥炭,灰分),流动几何形状和含水层几何形状的总体比例方面相似。因此,混合方法是一种生成火山岩含水层结构地质现实模型的有效方法。随着更多现场数据的可用,可以改善模型的真实性和参数化。

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