Using Stochastic Inverse Modeling Method to Obtain Probabilistic Capture Zones of a Spring in a Complex Fracture Aquifer
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摘要: 出于精度考量，研究场地尺度水文地质特征时，采用随机模拟技术建立多个满足场地有限地质信息的情景模型，可以较为有效地表征含（隔）水层结构，并描述目标泉点的捕集区域.但通常受条件限制，场地中的实际钻孔数量可能难以满足随机建模的数据密度要求.基于地面地质分析和一定量的实际钻孔资料，同时借助详实的瞬变电磁物探数据，在物探测点处构建虚拟钻孔，进而建立若干关于地层结构的随机模型（情景模型）；采用PEST参数自动识别程序筛选符合水位观测值的情景模型，并复核这些模型的地层结构，以保证情景模型的合理性.基于74个钻孔数据点（包含虚拟钻孔）的转移概率马尔科夫链（T-PORGS）共生成503个情景模型，以场地范围内9个地下水位观测点的数据为基准，通过PEST最终筛选出67个可以描述场地水文地质特征的模型，最后由筛选出的模型统计得到目标泉点的概率捕集区域.该建模流程可以在钻孔数据缺乏时，完成场地尺度的随机建模，并获得有效的场地水文地质信息.Abstract: Generating a series of stochastic models (realizations) by applying stochastic inverse modeling method is sometimes an efficient way to improve hydrogeological cognition accuracy of a site, such as obtaining a more clarity aquifer structure or a probabilistic capture zone of a spring. However, the borehole data size often cannot meet the requirements of stochastic modeling in a general project. Considering geological analysis result and borehole data, it may be a rational and effective method to translate geophysical prospecting (TEM) points into virtual boreholes to solve the data shortage problem. Using the PEST program, stochastic models established through practical boreholes and virtual boreholes can be screened with groundwater level data as the reference. The stratigraphic structure of the filtered models is then checked artificially to guarantee model geological rationality. In this paper, a total of 503 realizations are generated by using a transition probability Markov chain (T-PORGS) based on 74 data points (including virtual boreholes). With data from 9 groundwater observation points within the site as a benchmark, 67 models that effectively describe the hydrogeological characteristics of the site are selected through PEST. Finally, the probabilistic capture zones of the target spring in a fracture aquifer are calculated from these selected models. This modeling process enables stochastic modeling at a site scale even in the absence of sufficient borehole data, providing valuable hydrogeological information for the site.
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Key words:
- transition probability Markov chain /
- T-PROGS /
- PEST /
- fracture aquifer /
- probabilistic capture zone /
- hydrogeology
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图 2 场地概况、实物工作量与地质构造
Fig. 2. Conceptual representation of the modeling area, TEM lines & scanning points, boreholes, and geological structural outlines
图 3 建模区简化的岩性剖面
钻孔岩性层标号与简述：ZKl：a.混凝土、碎石土；b.强风化砂质板岩与板岩互层；c.弱-中风化砂岩、砂质板岩；d.砂质板岩与板岩互层. ZK2：a.第四系土层；b.中-强风化板岩；c.砂岩；d.砂岩与板岩互层. ZK3：a.第四系土层；c.中-强风化砂岩、板岩；d.强风化板岩；e.砂质板岩，局部夹砂岩、石英砂岩.ZK4：a.第四系土层；d.强风化板岩；e.砂岩夹中风化板岩. ZK5：a.第四系土层；b.强-全风化板岩，局部有砂质板岩夹层；c.砂岩、石英砂岩局部夹砂质板岩；d.弱-中风化板岩；e.砂岩、石英砂岩局部夹砂质板岩；f.炭质页岩. ZK6：a.混凝土、碎石土；d.强风化板岩；e.弱-中风化石英砂岩、砂岩、板岩、砂质板岩；f.炭质页岩. ZK9：a.第四系土层；d.强风化板岩；e.板岩、砂质板岩，底部含石英砂岩；f.炭质页岩
Fig. 3. Brief lithologic profiles in the modeling area
图 4 建模区沿岩层走向的简化岩性剖面
Fig. 4. A brief lithologic profile of strike direction in the modeling area
图 5 全67个模型不同岩性单元的渗透系数及模型的未加权残差平方和
Fig. 5. Hydraulic conductivity of lithologic units (materials) in all 67 realizations and the unweighted residual sum of squares of 67 realizations
图 6 水位拟合效果较好的5个情景模型
Fig. 6. The five realizations with better head calibration results
图 7 水位拟合程度较好的4个情景模型的南北向剖面
Fig. 7. North-south profiles of the four well fitted realizations
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