Optimized Groundwater Numerical Simulation Model with Trending Parameter Field

doi:10.19657/j.geoscience.1000-8527.2022.008

Abstract

Abstract:

Hydrogeological parameter field is the key and challenging topic in groundwater numerical simulation. Generally, high-precision simulation results are derived from more reasonable parameter field. In this study, a trending stochastic parameter field construction method was proposed with stochastic and spatial distribution simulation technology. We took the hydraulic conductivity field as an example, and used the MCMC sampling method and sample feature analysis to extract data structure first. After that, the stochastic parameter field was reshaped with the trend features. For the case study, we compared the traditional parameter field, which filled blocks with the regional mean value. The trending parameter field has significantly improved the model accuracy with the mean error reduced by about 2 times. Practically, the mean simulation error comes down from 2.76 to 0.64 m in the coarse-grained area. In contrast, there is little effect in the low permeability area. To conclude, our method can provide reference for optimizing groundwater numerical simulation, improve the model fitting accuracy, and better describe the groundwater flow system.

Key words: hydraulic conductivity, groundwater numerical simulation, model optimization, stochastic method, trend

CLC Number:

P641.2

NAN Tian, CAO Wengeng, WANG Zhuoran, ZHANG Juanjuan, ZHANG Dong. Optimized Groundwater Numerical Simulation Model with Trending Parameter Field[J]. Geoscience, 2022, 36(02): 591-601.

Figures/Tables 14

Fig.1 Technical route of the trending stochastic parameter field method

Fig.2 Hydraulic conductivity distribution of the numerical example

Fig.3 Distribution of wells

Fig.4 Flow field of the numerical case(unit: m)

Table 1 Hydraulic conductivity field of numerical example

岩性	最小值/ (m/d)	最大值/ (m/d)	平均值/ (m/d)	标准差/ (m/d)
R1	34	46	40.5	2
R2	14	26	20.5	2
R3	24	36	30.5	2

Table 2 Mean and standard deviation of the simulation hydraulic conductivity field

采样方法	R1			R2			R3
采样方法	$μ$ / (m/d)	$σ$ / (m/d)		$μ$ / (m/d)	$σ$ / (m/d)		$μ$ / (m/d)	$σ$ / (m/d)
MCMC	40.27	2.38		20.25	2.54		30.24	2.40

Table 2 Mean and standard deviation of the simulation hydraulic conductivity field

采样方法	R1			R2			R3
采样方法	$μ$ / (m/d)	$σ$ / (m/d)		$μ$ / (m/d)	$σ$ / (m/d)		$μ$ / (m/d)	$σ$ / (m/d)
MCMC	40.27	2.38		20.25	2.54		30.24	2.40

Fig.5 Distribution of the fitting error

Fig.6 Location and administrative regions of the study area

Fig.7 Profile of Ⅰ-Ⅰ' in the study area (profile’s location denoted in Fig.6)

Fig.8 Hydraulic conductivity distribution of shallow aquifer in the study area

Fig.9 Trending hydraulic conductivity field of Quaternary shallow aquifer in the study area

Table 3 Sampling values of hydraulic conductivity in different zones

分区	均值/ (m/d)	标准差/ (m/d)	最大值/ (m/d)	最小值/ (m/d)
1	10	1	14	6
1	10	2	18	2
2	50	1	55	46
2	50	10	90	15

Table 4 Two-dimensional linear equation parameters of hydraulic conductivity

分区	潜水含水层渗透系数/(m/d)	a/(m/d)	b/d^-1
1	10	10	1/24
2	50	50	1/22

Fig.10 Fitting curves for mornitoring wells (a) DJ38-1B;(b) DJ35-2B

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