Geophysical measurement for estimation of groundwater hydraulic properties.
Ontology highlight
ABSTRACT:
SUBMITTER: Yasir SF
PROVIDER: S-EPMC6222259 | biostudies-literature | 2018 Dec
REPOSITORIES: biostudies-literature
Publications
Geophysical measurement for estimation of groundwater hydraulic properties.
Yasir Safaa F SF Jani Janmaizatulriah J Mukri Mazidah M
Data in brief 20181025
In the study, a relationship was establishment between electrical resistivity by using electrical resistivity imaging (ERI) technique with hydraulic conductivity. By using Schlumberger array configuration, 2D electrical resistivity image was produced by using ABEM SAS 4000 with eighty-one (81) electrodes (Loke, 2004) [1]. By using regression equation, hydraulic conductivity was calculated from electrical resistivity and this result was compared with the hydraulic conductivity obtained from pumpi ...[more]
Similar Datasets
Project description:Hydraulic conductivity is a major parameter affecting the output accuracy of groundwater flow and transport models. The most commonly used semi-empirical formula for estimating conductivity is Kozeny-Carman equation. However, this method alone does not work well with heterogeneous strata. Two important parameters, grain size and porosity, often show spatial variations at different scales. This study proposes a method for estimating conductivity distributions by combining a stochastic hydrofacies model with geophysical methods. The Markov chain model with transition probability matrix was adopted to re-construct structures of hydrofacies for deriving spatial deposit information. The geophysical and hydro-chemical data were used to estimate the porosity distribution through the Archie's law. Results show that the stochastic simulated hydrofacies model reflects the sedimentary features with an average model accuracy of 78% in comparison with borehole log data in the Chaobai alluvial fan. The estimated conductivity is reasonable and of the same order of magnitude of the outcomes of the pumping tests. The conductivity distribution is consistent with the sedimentary distributions. This study provides more reliable spatial distributions of the hydraulic parameters for further numerical modeling.
| S-EPMC4772070 | biostudies-other
Project description:Hydraulic fracturing operations are generating considerable discussion about their potential to contaminate aquifers tapped by domestic groundwater wells. Groundwater wells located closer to hydraulically fractured wells are more likely to be exposed to contaminants derived from on-site spills and well-bore failures, should they occur. Nevertheless, the proximity of hydraulic fracturing operations to domestic groundwater wells is unknown. Here, we analyze the distance between domestic groundwater wells (public and self-supply) constructed between 2000 and 2014 and hydraulically fractured wells stimulated in 2014 in 14 states. We show that 37% of all recorded hydraulically fractured wells stimulated during 2014 exist within 2 km of at least one recently constructed (2000-2014) domestic groundwater well. Furthermore, we identify 11 counties where most ([Formula: see text]50%) recorded domestic groundwater wells exist within 2 km of one or more hydraulically fractured wells stimulated during 2014. Our findings suggest that understanding how frequently hydraulic fracturing operations impact groundwater quality is of widespread importance to drinking water safety in many areas where hydraulic fracturing is common. We also identify 236 counties where most recorded domestic groundwater wells exist within 2 km of one or more recorded oil and gas wells producing during 2014. Our analysis identifies hotspots where both conventional and unconventional oil and gas wells frequently exist near recorded domestic groundwater wells that may be targeted for further water-quality monitoring.
| S-EPMC5740626 | biostudies-literature
Project description:The chemical and physical properties of the interiors of terrestrial planets are largely determined during their formation and differentiation. Modeling a planet's formation provides important insights into the properties of its core and mantle, and conversely, knowledge of those properties may constrain formational narratives. Here, we present a multi-stage model of Martian core formation in which we calculate core-mantle equilibration using parameterizations from high pressure-temperature metal-silicate partitioning experiments. We account for changing core-mantle boundary (CMB) conditions, composition-dependent partitioning, and partial equilibration of metal and silicate, and we evolve oxygen fugacity (fO2) self-consistently. The model successfully reproduces published meteorite-based estimates of most elemental abundances in the bulk silicate Mars, which can be used to estimate core formation conditions and core composition. This composition implies that the primordial material that formed Mars was significantly more oxidized (0.9-1.4 log units below the iron-wüstite buffer) than that of the Earth, and that core-mantle equilibration in Mars occurred at 42-60% of the evolving CMB pressure. On average, at least 84% of accreted metal and at least 40% of the mantle were equilibrated in each impact, a significantly higher degree of metal equilibration than previously reported for the Earth. In agreement with previous studies, the modeled Martian core is rich in sulfur (18-19 wt%), with less than one weight percent O and negligible Si. We have used these core and mantle compositions to produce physical models of the present-day Martian interior and evaluate the sensitivity of core radius to crustal thickness, mantle temperature, core composition, core temperature, and density of the core alloy. Trade-offs in how these properties affect observable physical parameters like planetary mass, radius, moment of inertia, and tidal Love number k2 define a range of likely core radii: 1620-1870 km. Seismic velocity profiles for several combinations of model parameters have been used to predict seismic body-wave travel times and planetary normal mode frequencies. These results may be compared to forthcoming Martian seismic data to further constrain core formation conditions and geophysical properties.
| S-EPMC7344332 | biostudies-literature
Project description:To provide theoretical support for the protection of centralized drinking groundwater sources in karst areas, it is necessary to accurately identify the development of karst conduits and analyze the differences in hydrogeochemical characteristics of different karst systems. This provides a scientific basis for the accurate designation of risk zones that may cause drinking groundwater pollution. In this study, a geophysical survey, hydrogeological chemical process analysis and optimized fuzzy cluster analysis were used to gradually improve the understanding of karst water systems. AMT and HDR methods were used to calibrate the resistivity around the water-filling karst conduits, which ranged from 39 to 100 ?•m. A total of seven karst systems were identified, including four karst systems in the north of the study area, one karst system in the west and two karst systems in the south. Analysis of the hydrochemical data showed that HCO3-Ca and HCO3-Mg-Ca types accounted for 90% of all samples. The ?D and ?18O values of their main conduits were -51.70? to -38.30? and -7.99? to -5.96?, respectively. The optimized fuzzy clustering analysis method based on the weight of variables assigned by AHP more accurately verified karst water systems. Based on these findings, the drinking groundwater source risk zone was designated with an area of 33.90 km2, accounting for 34.5% of the study area. This study effectively improved the rationality and accuracy of the designation of drinking groundwater source risk zones in karst areas, and provided a scientific basis for the identification of karst water systems and decision-making of drinking groundwater source protection in karst areas.
| S-EPMC7277465 | biostudies-literature
Project description:Starting with a log-linear relationship between groundwater discharge per unit drainage area (Q/A b), hydraulic turnover time (t) and aquifer mobile storage (z), this study builds a groundwater security method at catchment scale. The method embeds previously published approaches to calculate Q/A b, t and z, and relies solely on stream flow discharges and watershed areas. The ability to build a method on a couple of variables is remarkable. The method recasts the calculated variables as aquifer security indicators (S Q, S t and S z), relating S Q with yield capacity, S t with self-depuration capacity and S z with resilience. Groundwater security is the weighted product of S Q, S t and S z. The method is validated with stream flow discharges and drainage areas concerning 294 hydrometric stations and their watersheds, located in continental Portugal. The results revealed a majority of moderately to highly secure watersheds, especially as regards S t (> 62%), while 7-10% were classified as very highly secured in general (S Q-S t-S z). The least secured basins are located in the more arid regions of continental Portugal (Northeast and south regions), as expected. The method can be easily transposed to any other region worldwide, with the necessary adaptions to regional climate, geological and topographic settings. • Compile stream flow discharge data and organize them as natural logarithms and logarithmic variations as function of time, to estimate Q, t and z; • Recast the Q, t and z values as S Q, S t and S z ratings, respectively, using the appropriate reclassification scales, and estimate watershed security levels, namely average security or customized (weighted) securities that highlight the contributions of Q/A b (watershed yield), t (aquifer's self-depuration capacity) or z (aquifer's resilience); • Use the results to draw illustrative diagrams and spatial distribution maps.
| S-EPMC9253716 | biostudies-literature
Project description:Concern persists over the potential for unconventional oil and gas development to contaminate groundwater with methane and other chemicals. These concerns motivated our 2-year prospective study of groundwater quality within the Marcellus Shale. We installed eight multilevel monitoring wells within bedrock aquifers of a 25-km2 area targeted for shale gas development (SGD). Twenty-four isolated intervals within these wells were sampled monthly over 2 years and groundwater pressures were recorded before, during, and after seven shale gas wells were drilled, hydraulically fractured, and placed into production. Perturbations in groundwater pressures were detected at hilltop monitoring wells during drilling of nearby gas wells and during a gas well casing breach. In both instances, pressure changes were ephemeral (<24 hours) and no lasting impact on groundwater quality was observed. Overall, methane concentrations ([CH4]) ranged from detection limit to 70 mg/L, increased with aquifer depth, and, at several sites, exhibited considerable temporal variability. Methane concentrations in valley monitoring wells located above gas well laterals increased in conjunction with SGD, but CH4 isotopic composition and hydrocarbon composition (CH4/C2H6) are inconsistent with Marcellus origins for this gas. Further, salinity increased concurrently with [CH4], which rules out contamination by gas phase migration of fugitive methane from structurally compromised gas wells. Collectively, our observations suggest that SGD was an unlikely source of methane in our valley wells, and that naturally occurring methane in valley settings, where regional flow systems interact with local flow systems, is more variable in concentration and composition both temporally and spatially than previously understood.
| S-EPMC6142237 | biostudies-literature
Project description:This paper is motivated by a wide range of background correction problems in gene array data analysis, where the raw gene expression intensities are measured with error. Estimating a conditional density function from the contaminated expression data is a key aspect of statistical inference and visualization in these studies. We propose re-weighted deconvolution kernel methods to estimate the conditional density function in an additive error model, when the error distribution is known as well as when it is unknown. Theoretical properties of the proposed estimators are investigated with respect to the mean absolute error from a "double asymptotic" view. Practical rules are developed for the selection of smoothing-parameters. Simulated examples and an application to an Illumina bead microarray study are presented to illustrate the viability of the methods.
| S-EPMC4183069 | biostudies-literature
Project description:The xylem of Metasequoia glyptostroboides Hu et Cheng is characterized by very low density (average specific gravity = 0.27) and tracheids with relatively large dimensions (length and diameter). The microfibril angle in the S2 layer of tracheid walls is large, even in outer rings, suggesting a cambial response to compressive rather than tensile stresses. In some cases, this compressive stress is converted to irreversible strain (plastic deformation), as evidenced by cell wall corrugations. The heartwood is moderately decay resistant, helping to prevent Brazier buckling. These xylem properties are referenced to the measured bending properties of modulus of rupture and modulus of elasticity, and compared with other low-to-moderate density conifers. The design strategy for Metasequoia is to produce a mechanically weak but hydraulically efficient xylem that permits rapid height growth and crown development to capture and dominate a wet site environment. The adaptability of these features to a high-latitude Eocene palaeoenvironment is discussed.
| S-EPMC4243641 | biostudies-literature
Project description:Geographic information systems (GIS) based techniques are cost-effective and efficient methods used by state agencies and epidemiology researchers for estimating concentration and exposure. However, budget limitations have made statewide assessments of contamination difficult, especially in groundwater media. Many studies have implemented address geocoding, land use regression, and geostatistics independently, but this is the first to examine the benefits of integrating these GIS techniques to address the need of statewide exposure assessments. A novel framework for concentration exposure is introduced that integrates address geocoding, land use regression (LUR), below detect data modeling, and Bayesian Maximum Entropy (BME). A LUR model was developed for tetrachloroethylene that accounts for point sources and flow direction. We then integrate the LUR model into the BME method as a mean trend while also modeling below detects data as a truncated Gaussian probability distribution function. We increase available PCE data 4.7 times from previously available databases through multistage geocoding. The LUR model shows significant influence of dry cleaners at short ranges. The integration of the LUR model as mean trend in BME results in a 7.5% decrease in cross validation mean square error compared to BME with a constant mean trend.
| S-EPMC3494280 | biostudies-literature
Project description:Model-based clustering with finite mixture models has become a widely used clustering method. One of the recent implementations is MCLUST. When objects to be clustered are summary statistics, such as regression coefficient estimates, they are naturally associated with estimation errors, whose covariance matrices can often be calculated exactly or approximated using asymptotic theory. This article proposes an extension to Gaussian finite mixture modeling-called MCLUST-ME-that properly accounts for the estimation errors. More specifically, we assume that the distribution of each observation consists of an underlying true component distribution and an independent measurement error distribution. Under this assumption, each unique value of estimation error covariance corresponds to its own classification boundary, which consequently results in a different grouping from MCLUST. Through simulation and application to an RNA-Seq data set, we discovered that under certain circumstances, explicitly, modeling estimation errors, improves clustering performance or provides new insights into the data, compared with when errors are simply ignored, whereas the degree of improvement depends on factors such as the distribution of error covariance matrices.
| S-EPMC7074130 | biostudies-literature