Determination of the hydraulic conductivity components using a three-dimensional fracture network model in volcanic rock

Park, B.Y; Kim, K.S; Kwon, Saeha; Kim, C; Bae, Dae-Seok; Hartley, Lee; Lee, H.K

doi:10.1016/s0013-7952(02)00037-6

Cited by 33 publications

(8 citation statements)

References 1 publication

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“…The principal eigenvalue and corresponding eigenvector are the principal value and direction of the hydraulic conductivity tensor, respectively. The geometrical mean is closer to the actual value (Park et al, 2002), the ratio of the geometrical mean hydraulic conductivity obtain by site hydraulic test to the geometrical mean of the three principal values is used to modify the hydraulic conductivity tensor. The corresponding formula is given as follows (Tian and Wang, 1989;Li et al, 2008):…”

Section: Calculation Of Representative Elementary Volume (Rev) and Hymentioning

confidence: 99%

The anisotropic seepage analysis of water-sealed underground oil storage caverns

Chong

Deng

et al. 2013

Tunnelling and Underground Space Technology

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Section: Calculation Of Representative Elementary Volume (Rev) and Hymentioning

confidence: 99%

The anisotropic seepage analysis of water-sealed underground oil storage caverns

Chong

Deng

et al. 2013

Tunnelling and Underground Space Technology

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“…Aquifers at specific demand locations are being drained, and this state may lead to deprivation of this indispensable resource unless allayed [2]. The hydraulic conductivity and permeability of the rocks depend on the rocks' porosity [3]. Heavy metals are among the most significant pollutants of groundwater sources [4,5].…”

Section: Introductionmentioning

confidence: 99%

Geospatial Assessment of Groundwater Quality with the Distinctive Portrayal of Heavy Metals in the United Arab Emirates

Salem

Nazzal

Howari

et al. 2022

Water

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Groundwater is a valuable resource, and its quality is critical to human survival. Optimal farming and urbanization degraded groundwater reserves. This research investigates and reports the spatial variability of selected heavy metals developed in the Liwa area of the United Arab Emirates. Forty water samples were collected from existing wells and analyzed for different elements. Principal components analysis was applied to a subgroup of the data set in terms of their usefulness for determining the variability of groundwater quality variables. Geographic information systems were used to produce contour maps to analyze the distribution of heavy metals. Ordinary kriging was used with Circular, Spherical, Tetraspherical, Pentaspherical-Bessel, K-Bessel, Hole effect, and Stable models for better representation. The water quality index was constructed using heavy metal concentrations and other variables. This yielded a value of 900 beyond the limit stated by WHO and US EPA. Nugget analysis showed that Cd (0), K (7.38%), and SO4 (1.81%) variables exhibited strong spatial dependence. Al (27%), Ba (40.87%), Cr (63%), Cu (34%), EC (27%), HCO3 (56%), NO3(36%), Pb (64%), and TDS (53%) represented moderate spatial dependence. As (76%), Mn (79%), Ni (100%), pH (100%), Temp (93%), and Zn (100%) exhibited weak spatial dependence.

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“…In a rock mass, fluid flow is often concentrated or localized in certain flow paths, which heavily influence hydraulic behavior (Park et al, 2002;Wang and Kulatilake, 2008). Identifying these flow paths is therefore essential.…”

Section: Introductionmentioning

confidence: 99%

Multidimensional scaling and inverse distance weighting transform for image processing of hydrogeological structure in rock mass

Mito¹,

Ismail²,

Yamamoto³

2011

Journal of Hydrology

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A new imaging method based on the multidimensional scaling (MDS) and inverse distance weighting (IDW) transform is proposed in this study. This method aims to identify, characterize and process an image of the preferential flow path in a rock mass, which strongly governs the hydraulic behavior of this rock mass. This methodology uses pair-wise hydraulic diffusivity data from cross-hole hydraulic testing as the input data. The input data are then processed by MDS and IDW to generate a spatial distribution map of the hydraulic properties, which can be used to 2 infer the preferential flow path in the rock mass. The reliability of this novel method was validated through numerical experiments using several continuum models with different hydrogeological structures, and the applicability of the developed method to the actual field was verified through in-situ experiments.

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Determination of the hydraulic conductivity components using a three-dimensional fracture network model in volcanic rock

Cited by 33 publications

References 1 publication

The anisotropic seepage analysis of water-sealed underground oil storage caverns

The anisotropic seepage analysis of water-sealed underground oil storage caverns

Geospatial Assessment of Groundwater Quality with the Distinctive Portrayal of Heavy Metals in the United Arab Emirates

Multidimensional scaling and inverse distance weighting transform for image processing of hydrogeological structure in rock mass

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