...
首页> 外文期刊>Hydrology and Earth System Sciences >Multi-offset ground-penetrating radar imaging of a lab-scale infiltration test
【24h】

Multi-offset ground-penetrating radar imaging of a lab-scale infiltration test

机译:实验室规模渗透测试的多偏移量探地雷达成像

获取原文
获取原文并翻译 | 示例

摘要

A lab scale infiltration experiment was conducted in a sand tank to evaluate the use of time-lapse multi-offset ground-penetrating radar (GPR) data for monitoring dynamic hydrologic events in the vadose zone. Sets of 21 GPR traces at offsets between 0.44-0.9 m were recorded every 30 s during a 3 h infiltration experiment to produce a data cube that can be viewed as multi-offset gathers at unique times or common offset images, tracking changes in arrivals through time. Specifically, we investigated whether this data can be used to estimate changes in average soil water content during wetting and drying and to track the migration of the wetting front during an infiltration event. For the first problem we found that normal-moveout (NMO) analysis of the GPR reflection from the bottom of the sand layer provided water content estimates ranging between 0.10-0.30 volumetric water content, which underestimated the value determined by depth averaging a vertical array of six moisture probes by 0.03-0.05 volumetric water content. Relative errors in the estimated depth to the bottom of the 0.6 m thick sand layer were typically on the order of 2%, though increased as high as 25% as the wetting front approached the bottom of the tank. NMO analysis of the wetting front reflection during the infiltration event generally underestimated the depth of the front with discrepancies between GPR and moisture probe estimates approaching 0.15 m. The analysis also resulted in underestimates of water content in the wetted zone on the order of 0.06 volumetric water content and a wetting front velocity equal to about half the rate inferred from the probe measurements. In a parallel modeling effort we found that HYDRUS-1D also underestimates the observed average tank water content determined from the probes by approximately 0.01-0.03 volumetric water content, despite the fact that the model was calibrated to the probe data. This error suggests that the assumed conceptual model of laterally uniform, one-dimensional vertical flow in a homogenous material may not be fully appropriate for the experiment. Full-waveform modeling and subsequent NMO analysis of the simulated GPR response resulted in water content errors on the order of 0.01-0.03 volumetric water content, which are roughly 30-50% of the discrepancy between GPR and probe results observed in the experiment. The model shows that interference between wave arrivals affects data interpretation and the estimation of traveltimes. This is an important source of error in the NMO analysis, but it does not fully account for the discrepancies between GPR and the moisture probes observed in the experiment. The remaining discrepancy may be related to conceptual errors underlying the GPR analysis, such as the assumption of uniform one-dimensional flow, a lack of a sharply defined wetting front in the experiment, and errors in the petrophysical model used to convert dielectric constant to water content.
机译:在沙罐中进行了实验室规模的入渗实验,以评估使用时移多偏移距地面穿透雷达(GPR)数据来监测渗流带中动态水文事件的情况。在3小时的渗透实验中,每30 s记录一组偏移量在0.44-0.9 m之间的21条GPR迹线,以生成一个数据立方体,可以将其视为在独特时间的多偏移量收集或通用偏移量图像,从而跟踪到达的变化时间。具体来说,我们调查了该数据是否可用于估计在润湿和干燥过程中平均土壤含水量的变化以及在浸润事件中跟踪润湿前沿的迁移。对于第一个问题,我们发现从砂层底部进行的GPR反射的法向运动(NMO)分析提供的含水量估计值介于0.10-0.30体积含水量之间,这低估了通过深度平均的垂直阵列确定的值。六个湿度探头按0.03-0.05的体积含水量。到0.6 m厚的沙层底部的估计深度的相对误差通常在2%的数量级上,但随着润湿前沿接近储罐底部而增加的误差高达25%。在渗透过程中,对湿润前反射的NMO分析通常低估了前部的深度,GPR和湿度探头估计之间的差异接近0.15 m。该分析还导致湿区含水量低估了约0.06体积水含量,润湿前沿速度约等于探针测量推断速率的一半。在并行建模工作中,我们发现HYDRUS-1D还低估了从探头测得的平均储罐含水量约0.01-0.03体积水含量,尽管已根据探头数据对模型进行了校准。该错误表明,假设的均质材料中横向均匀,一维垂直流的概念模型可能并不完全适合该实验。对模拟的GPR响应进行全波形建模和随后的NMO分析会导致含水量误差在0.01-0.03体积水含量的数量级,大约是实验中观察到的GPR和探针结果差异的30-50%。该模型表明,波到达之间的干扰会影响数据解释和传播时间的估计。这是NMO分析中一个重要的错误来源,但它不能完全解决GPR和实验中观察到的水分探头之间的差异。剩余的差异可能与GPR分析背后的概念错误有关,例如假设一维均匀流动,实验中缺乏明确定义的润湿前沿以及用于将介电常数转换为水的岩石物理模型中的错误内容。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号