| 基于三维随机水文地质结构模型的地下水流数值模拟: 以西辽河平原为例 |
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| 引用本文： | 孙倩,邵景力,崔亚莉,王玉龙,薛俊环,马涛.基于三维随机水文地质结构模型的地下水流数值模拟: 以西辽河平原为例[J].现代地质,2019,33(2):451-460. |
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| 作者姓名： | 孙倩 邵景力 崔亚莉 王玉龙 薛俊环 马涛 |
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| 作者单位： | 1.中国地质大学(北京) 水资源与环境学院,北京 1000832.内蒙古自治区第四水文地质工程地质勘察院,内蒙古 通辽 0280003.中国地质调查局 水文地质环境地质调查中心,河北 保定 071051 |
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| 基金项目： | 国家重点研发计划资助项目(2017YFC0406106) |
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| 摘 要： | 为了探究水文地质结构对地下水流数值模拟的不确定性,可以运用随机模拟建立地下水位的预测模型。根据转移概率地质统计方法模拟多孔介质岩性分布,利用非线性规划的思路计算岩性与水文地质参数之间的关系,从而建立相对精确的随机水文地质参数场。将不同的水文地质参数场运用到MODFLOW中,得到不同的随机模拟结果。通过比较随机模型和确定模型的末流场拟合情况以及水位动态拟合图,发现确定模型和随机模型具有相似趋势,都能与实测流场拟合较好,但是随机模型更能体现真实的水文地质特征。对随机模型预测10年后的地下水水位做不确定性分析,得到水位平均变幅介于-5～5 m之间,且95%置信度水位变幅的平均上限线约为0.146 m。研究结果为决策者提供科学依据。
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| 关 键 词： | TPROGS 水文地质参数 随机 非线性规划 不确定性 |
| 收稿时间： | 2018-05-06 |
| 修稿时间： | 2018-12-12 |
Numerical Simulations of Groundwater Based on Three-dimensional Stochastic Hydrogeologic Structure Model: A Case Study from West Liaohe Plain |
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| SUN Qian,SHAO Jingli,CUI Yali,WANG Yulong,XUE Junhuan,MA Tao.Numerical Simulations of Groundwater Based on Three-dimensional Stochastic Hydrogeologic Structure Model: A Case Study from West Liaohe Plain[J].Geoscience——Journal of Graduate School,China University of Geosciences,2019,33(2):451-460. |
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| Authors: | SUN Qian SHAO Jingli CUI Yali WANG Yulong XUE Junhuan MA Tao |
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| Institution: | 1. School of Water Resources and Environment, China University of Geosciences, Beijing 100083, China2. The Fourth Hydrogeological and Engineering Geological Prospecting Institute of Inner Mongolia Autonomous Region, Tongliao, Inner Mongolia 028000, China3. Center for Hydrogeology and Environmental Geology Survey, China Geological Survey, Baoding, Hebei 071051, China |
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| Abstract: | To explore the uncertainty of the hydrogeological structure on the groundwater numerical simulation the West Liaohe Plain, the groundwater level was predicted using stochastic simulation. In this paper, lithologic distribution of porous media was simulated by the transition probability geostatistical method, and the relationship between lithology and hydrogeological parameters were determined by nonlinear programming. The many accurate stochastic hydrogeological parameters obtained were fed into MODFLOW, which generated the stochastic simulation. By comparing the fitting of the groundwater terminal flow field and the water level dynamic process of the stochastic and deterministic models, we found that the stochastic and deterministic models have similar trends, and are well-fitted to the measured data. The stochastic model can better reflect the real hydrogeological characteristics. According to the uncertainty analysis of the predicted groundwater level after 10 years, the average amplitude of the water level variation is ±5 m, and the average upper limit of the 95% confidence level is 0.146 m. This provides a scientific basis for decision makers. |
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| Keywords: | TPROGS hydrogeological parameter stochastic nonlinear programming uncertainty |
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