Characterizing Fracture Spatial Patterns by Using Semivariograms

Semivariogram is applied to fracture data obtained from detailed scanlinesurveys of nine field sites in western New York, USA in order to investigate the spatial patterns ofnatural fractures. The length of the scanline is up to 36 m. How both fracture spacing and fracturelength vary with distance is determined through semivariogram calculations. In this study, theauthors developed a FORTRAN program to resample the fracture data from the scanline survey. Bycalculating experimental semivariogram, the authors found five different types of spatial patternsthat can be described by linear, spherical, reversed spherical, polynomial I (for aO) models, of which the last three arc newly proposed in this study. Thewell-structured semivariograms of fracture spacing and length indicate that both the location of thefractures and the length distribution within their structure domains are not random. The results ofthis study also suggest that semivariograms can provide useful information in terms of spatialcorrelation distance for fracture location and fracture length. These semivariograms can also beutilized to design more efficient sampling schemes for further surveys. as well as to define thelimits of highly probable extrapolation of a structure domain.

Strategic Planning for Equitable RWIS Implementation:A Comprehensive Study Incorporating a Multi-variable Semivariogram Model

This paper extends the previously developed method of optimizing Road Weather Information Systems(RWIS)station placement by unveiling a sophisticated multi-variable semivariogram model that concurrently considers multiple vital road weather variables.Previous research primarily centered on single-variable analysis focusing on road surface temperature(RST).The study bridges this oversight by introducing a framework that integrates multiple critical weather variables into the RWIS location allocation framework.This novel approach ensures balanced and equitable RWIS distribution across zones and aligns the network with areas both prone to traffic accidents and areas of high uncertainty.To demonstrate the effectiveness of this refinement,the authors applied the framework to Maine’s existing RWIS network,conducted a gap analysis through varying planning scenarios and generated optimal solutions using a heuristic optimization algorithm.The analysis identified areas that would benefit most from additional RWIS stations and guided optimal resource utilization across different road types and priority locations.A sensitivity analysis was also performed to evaluate the effect of different weightings for weather and traffic factors on the selection of optimal locations.The location solutions generated have been adopted by MaineDOT for future implementations,attesting to the model’s practicality and signifying an important advancement for more effective management of road weather conditions.

The application of geostatistics in grain size trend analysis： A case study of eastern Beibu Gulf

There are 71 surface sediment samples collected from the eastern Beibu Gulf. The moment parameters （i.e. mean size, sorting and skewness） were obtained after applying grain size analysis. The geostatistical analysis was then applied to study the spatial autocorrelation for these parameters; while range, a parameter in the semivariogram that meters the scale of spatial autocorrelation, was estimated. The results indicated that the range for sorting coefficient was physically meaningful. The trend vectors calculated from grain size trend analysis model were consistent with the annual ocean circulation patterns and sediment transport rates according to previous studies. Therefore the range derived from the semivariogram of mean size can be used as the characteristic distance in the grain size trend analysis, which may remove the bias caused by the traditional way of basing on experiences or testing methods to get the characteristic distance. Hence the results from geostatistical analysis can also offer useful information for the determination of sediment sampling density in the future field work.

Spatial heterogeneity of soil nutrients in old growth forests of Korean pine

In this paper, we used geostatistics studied the spatial heterogeneity of total nitrogen and phosphorus on the top soil (0–10 cm) in old growth forests of Korean pine. There was a high degree of spatial heterogeneity of both nutrients which were dependent scales. The isotropic spatial dependent scale were 6.19 m (N%) and 11.10 m (P%). Both nutrients have anisotropic structures at sampled area. Spatial heterogeneity of autocorrelated was over 80%, and spatial autocorrelation was important in nutrient variations in space. This caused spatial patterns of total nitrogen and phosphorus in forest top soil.

Spatial variation and prediction of forest biomass in a heterogeneous landscape

Large areas assessments of forest bioinass distribution are a challenge in heterogeneous landscapes, where variations in tree growth and species composition occur over short distances. In this study, we use statistical and geospatial modeling on densely sample.d forest biomass data to analyze the relative importance of ecological and physiographic variables as determinants of spatial variation of forest biomass in the environmentally heterogeneous region of the Big Sur, California. We estimated biomass in 280 forest Plots （one plot per 2.85 km2） and meas- ured an array of ecological （vegetation community type, distance to edge, amount of surrounding non-forest vegetation, soil properties, fire history） and physiographic drivers （elevation, potential soil moisture and solar radiation, proximity to the coast） of tree growth at each plot location. Our geostatistical analyses revealed that biomass distribution is spatially structured and autocorrelated up to 3.1 kin. Regression tree （RT） models showed that both physiographic and ecological factors influenced bio- mass distribution. Across randomly selected sample densities （sample size 112 to 280）, ecological effects of vegetation community type and distance to forest edge, and physiographic effects of elevation, potential soil moisture and solar radiation were the most consistent predictors of biomass. Topographic moisture index and potential solar radiation had apositive effect on biomass, indicating the importance of topographically- mediated energy and moisture on plant growth and biomass accumula- tion. RT model explained 35% of the variation in biomass and spatially autocorrelated variation were retained in regession residuals. Regression kriging model, developed from RT combined with kriging of regression residuals, was used to map biomass across the Big Sur. This study dem- onstrates how statistical and geospatial modeling can be used to dis- criminate the relative importance of physiographic and ecologic effects on forest biomass and develop spatial models to predict and map biomass distribution across a heterogeneous landscape.

Quantification of Spatial Heterogeneity in Old GrowthForests of Korean Pine

Spatial heterogeneity is a very important issue in studying functions and processes of ecological systems at various scales. Semivariogram analysis is an effective technique to summarize spatial data,and quantification of spatial het-erogeneity. In this paper, we propose some prmciples to use semivariograms to characterize and compare spatial heterogene ity of ecological systems and use an example of old growth forests of Korean pine to demonstrate these prmciples and to dis-cuss its charactcristics of spatial heterogeneity.

Simulation of Landscape Pattern of Old Growth Forests of Korean Pine By Block Kriging

The study area was located in Liangshui Natural Reserve. Xaoxing'an Mountains. Northeastern China. Korean pine forests are the typical forest ecosystems and landscapcs in this region. It is a high degrees of spatial and temporal hetcrogeneity at different scales, which effected on landscape pattern and processes. In this paper we used the data of 144 plots and semivariogram to analyze spatial heterogeneity of old growth forests of Korean pine in landscape level. Model for forest variations by isotropic semivariogram is linear with sill. The spatial heterogeneity is dependent on scales and dircctions in Korean pine forests. Patterns of forest types in space were resulted from complex interactions between physical and biological forces. We uscd 20 metres for interpolation interval to cstimate the values of unsampled area. Comparing the results with field data, block kriging and mapping are an cffective techniques to simulate landscape pattern.

Optimization of Groundwater Level Monitoring Network Using GIS-based Geostatistical Method and Multi-parameter Analysis:A Case Study in Wainganga Sub-basin,India

Groundwater is one of the most important resources, its monitoring and optimized management has now become the priority to satisfy the demand of rapidly increasing population. In many developing countries, optimized groundwater level monitoring networks are rarely designed to build up a strong groundwater level data base, and to reduce operation time and cost. The paper presents application of geostatistical method to optimize existing network of observation wells for 18 sub-watersheds within the Wainganga Sub-basin located in the central part of India. The average groundwater level fluctuation(GWLF) from 37 observation wells is compared with parameters like lineament density, recharge, density of irrigation wells, land use and hydrogeology(LiRDLH) of Wainganga Sub-basin and analyzed stochastically in Geographic Information System(GIS) environment using simple, ordinary, disjunctive and universal kriging methods. Semivariogram analyses have been performed separately for all kriging methods to fit the best theoretical model with experimental model. Results from gaussian, spherical, exponential and circular theoretical models were compared with those of experimental models obtained from the groundwater level data. Spatial analyses conclude that the exponential semivariogram model obtained from ordinary kriging gives the best fit model. Study demonstrates that ordinary kriging gives the optimal solution and additional number of observation wells can be added utilizing the error variance for optimal design of groundwater level monitoring networks. This study describes the use of Geostatistics methods in GIS to predict the groundwater level and upgrade groundwater level monitoring networks from the randomly distributed observation wells considering multiple parameters such as GWLF and LiRDLH. The method proposed in the present study is observed to be an efficient method for selecting observation well locations in a complex geological set up. The study concludes that minimum 82 wells are required for proper monitoring of groundwater level in the study area.

Quantitative assessment of the spatio-temporal correlations of seismic events induced by longwall coal mining

Rock failure process as a natural response to mining activities is associated with seismic events, which can pose a potential hazard to mine operators, equipment and infrastructures. Mining-induced seismicity has been found to be internally correlated in both time and space domains as a result of rock fracturing during progressive mining activities. Understanding the spatio-temporal(ST) correlation of mininginduced seismic events is an essential step to use seismic data for further analysis, such as rockburst prediction and caving assessment. However, there are no established methods to perform this critical task. Input parameters used for the prediction of seismic hazards, such as the time window of past data and effective prediction distance, are determined based on site-specific experience without statistical or physical reasons to support. Therefore, the accuracy of current seismic prediction methods is largely constrained, which can only be addressed by quantitively assessing the ST correlations of mininginduced seismicity. In this research, the ST correlation of seismic event energy collected from a study mine is quantitatively analysed using various statistical methods, including autocorrelation function(ACF), semivariogram and Moran’s I analysis. In addition, based on the integrated ST correlation assessment, seismic events are further classified into seven clusters, so as to assess the correlations within individual clusters. The correlation of seismic events is found to be quantitatively assessable, and their correlations may vary throughout the mineral extraction process.

A quantitative assessment on groundwater salinization in the Tarim River lower reaches,Northwest China

Based on monitored data from 840 samples, we assessed the spatial and temporal variability of groundwater salinization in the Tarim River lower reaches combining classical statistics and geostatistics. Results show that total dissolved solids （TDS） is significantly correlated with other related ions, such as Na＋, Mg2＋, Ca2-, C1- and K＋. TDS and underground water level have characteristics of spatial autocorrelation, both of which present the isotropic characteristic and con- form to the spherical model in each year from 2001-2009. TDS is basically greater than 1 g/L but less than 2 g/L in the Tarim River lower reaches, which indicates that salt stagnation pollution is more serious. The most serious salinization （3 g/L 〈 TDS _〈 35 g/L） contaminated area is mainly in the middle and lower part of the study area.

Simulation and Prediction on Space-Time Distribution of Water Quality in Dianchi Lake

It is very important in pollution treatment to clarify the space-time distribution of water quality in Dianchi Lake. Based on the sample data obtained from 10 observation stations every month from 2008 to 2009, it uses space-time semi-variogram and ordinary kriging method to simulate the space-time variance and distribution of water quality indictors (TN, TP, BOD, CODMn, DO, Chlorophyl-α, etc.). Because the space-time semivariogram also has a certain of ex-trapolation function. From the simulation results, the pollution is mainly concentrated at the North (Caohai) and the Southwest area (Haikou), and water pollution has a increase trend.

Application of Geostatistical Models for Estimating Spatial Variability of Rock Depth

Rock depth information of a site is a significant factor for geotechnical engineering and earthquake ground response analysis. In this paper, reduced level of rock at Bangalore is arrived from the 652 boreholes in the area covering 220 km2. Geostatistical modeling based on kriging (simple and ordinary) techniques has been applied for estimating reduced level of hard rock in Bangalore. The models are used to compute variance of estimated reduced level of the rock. A new type of cross-validation analysis proves the robustness of the developed models. The comparison between the simple and ordinary kriging model demonstrates that the ordinary kriging model is superior to simple kriging model in predicting reduced level of rock in the subsurface of Bangalore.

Spatial Distribution of Soil Organic Matter and Soil Organic Carbon Stocks in Semi-Arid Area of Northeastern Syria

Although soil organic matter (SOM) forms a small portion of the soil body. Nevertheless, it is the most important component of the soil ecosystem, as well as of the carbon global cycle. In the semi-arid environment, there has been little research on the spatial distribution of SOM and soil organic carbon (SOC) stock. In this study, stratified random samples of total 30 soils were collected from two different soil depth (topsoil, subsoil) of Al Balikh plain and used for mapping the spatial variability of SOC and to estimating the SOC stock. The result showed that the values were relatively homogenate, with the normal decreasing trend with increasing the depth. The standard deviation (Std. D) for both SOC and SOC stock indicates homogeneous and absence of outliers values, whereas the coefficient of variation (C.V) indicates non-dispersion and clustering of values around the average. SOC was 0.38%, 0.17% in topsoil and subsoil respectively;the corresponding averages of SOC stock were 1.23 kg·m-2? and 1.14 kg·m-2 respectively, these values reflecting typical characteristics of poor SOC semi-arid soil. The correlation between SOC and SOC stock was (R2 = 0.996, p 2 = 0.941, p < 0.001) for subsoil. The semivariograms were indicated that both SOC and SOC stock were best fitted to the exponential model. Nugget, range, and sill were equal to 0.002, 0.036, and 0.044, respectively for SOC in topsoil, and 0.014, 0.071, and 0.081, for SOC in the subsoil. For SOC stock, it was 0.0, 0.036, and 0.0508, respectively in topsoil. In the subsoil, the values were 0.1899, 0.086, and 4.159, respectively. SOC and SCO stock in both two layers are shown a strong spatial dependence, for which were 4.3, 17.2 for SOC in topsoil and subsoil respectively, and 0.0, 4.5 for SOC stock in topsoil and subsoil respectively, thus, which can be attributed to intrinsic factors.

Spatial Variability of Selected Soil Attributes under Agricultural Land Use System in a Mountainous Watershed, Ethiopia

In the Ethiopian Highlands, research projects were often measuring soil attributes of spatially structured point data but soil variability at a watershed scale is not clearly defined. This study was conducted to assess the correlation among selected soil attributes and to illustrate the spatial pattern and dependence of neighboring observations. The 53.7 km2?study watershed was divided into a 500 m by 500 m square grid using arcgis and at the center of each grid soil samples from 0 to 25 cm depth were collected within 184 locations. The descriptive statistics revealed available phosphorous (AP) had the largest coefficient of variation (CV = 104) while pH was the least variable. There was a positive link between elevation and SOC whereas bulk density (ρd) and pH indicated an inverse relationship with elevation and SOC. The value for nugget/sill of ρd, pH and elevation are less than 0.25, and depicts that it has strong spatial autocorrelation. The value for nugget/?sill of SOC, and TN found between 0.25 and 0.75, and indicate that they have moderate spatial?autocorrelation. With regard to AP, the value for nugget/sill is more than 0.75, which displays a weak?spatial autocorrelation. Semivariograms of ρd, pH and elevation were best fitted to Gaussian model whereas SOC, TN and AP were best fitted to exponential function. Generally, the study verified that soil measurements taken at the given scale through regular sampling interval were adequate to capture the spatial dependence of numerous initial soil assessments in the study watershed.

Spatial Intra-Annual Variability of Precipitation Based on Geostatistics.A Case Study for the Paraiba Do Sul Basin,Southeastern Brazil

The emphasis in this research is to evaluate the spatial distribution of the precipitation using a geostatistics approach. Seasonal time scales records considering DJF, MAM, JJA e SON periods performed the analysis. Procedures to evaluate the variogram selection and to produce kriging maps were performed in a GIS environment . The results showed that kriging method was very suitable to detect both large changes in the whole area as those local small and subtle changes. Kriging demonstrated be a powerful statistical interpolation method that might be very useful in regions with great complexity in climatology and geomorphology.

Geostatistics as a Methodology for Studying the Spatiotemporal Dynamics of Ramularia areola in Cotton Crops

Geostatistics as a methodology for studying the spatiotemporal dynamics of Ramularia areola in cotton crops. Geostatistics is a tool that has been used to study plant pathology, by modeling the spatiotemporal pattern of diseases, generating hypotheses about their epidemiological aspects in order to use tactics and strategies of rational control. The objective of this study was to use geostatistics to study the spatiotemporal dynamics of Ramularia areola in cotton crops. The experiment was conducted at the experimental area of Mato Grosso State University-Tangará da Serra campus, and arranged in a 2 × 3 factorial design, with randomized blocks, with two spaicngs (0.45 and 0.90 cm) and three conditions of soil coverage (no cover, P. glaucum and C. spectabilis). Geostatistical analysis of data was performed using data from temporal and spatial progress of R. areola, obtained through assessments of the incidence and severity of the disease in plants, and spatial dependence, and analyzed using semivariogram fittings. Through the isotropic exponential semivariogram model, it was possible to check the distribution pattern and spatial dependence of Ramularia leaf spot. Spatial dependence was observed for the disease—moderate to strong for most data evaluated. The pathogen spread from the primary source of inoculum, from the center portion towards the edges, forming foci originating from a source of secondary inoculum.

Characterizing Spatial Variability of Soil Textural Fractions and Fractal Parameters Derived from Particle Size Distributions

Soil particle size distribution(PSD) is a fundamental physical property affecting other soil properties. Characterizing spatial variability of soil texture is very important in environmental research. The objectives of this work were: 1) to partition PSD of 75 soil samples, collected from a flat field in the University of Guilan, Iran, into two scaling domains using a piecewise fractal model to evaluate the relationships between fractal dimensions of scaling domains and soil clay, silt, and sand fractions and 2) to assess the potential of fractal parameters as an index used in a geostatistical approach reflecting the spatial variability of soil texture. Features of PSD of soil samples were studied using fractal geometry, and geostatistical techniques were used to characterize the spatial variability of fractal and soil textural parameters. There were two scaling domains for the PSD of soil samples. The fractal dimensions of these two scaling domains(D_1 and D_2) were then used to characterize different ranges of soil particle sizes and their relationships to the soil textural parameters. There was a positive correlation between D_1 and clay content(R^2= 0.924), a negative correlation between D_1 and silt content(R^2= 0.801), and a negative correlation between D_2 and sand content(R^2= 0.913). The geometric mean diameter of soil particles had a negative correlation with D_1(R^2= 0.569) and D_2(R^2= 0.682). Semivariograms of fractal dimensions and soil textural parameters were calculated and the maps of spatial variation of D_1 and D_2 and soil PSD parameters were provided using ordinary kriging. The results showed that there were also spatial correlations between D_1 and D_2 and particle size fractions.According to the semivariogram models and validation parameters, the fractal parameters had powerful spatial structure and could better describe the spatial variability of soil texture.

Predicting the spatial distribution of soil erodibility factor using USLE nomograph in an agricultural watershed,Ethiopia

Soil erosion in the northwestern Amhara region,Ethiopia has been a subject of anxiety,resulting in a major environmental threat to the sustainability and productive capacity of agricultural areas.This study tried to estimate soil erodibility factor(Kfactor)using Universal Soil Loss Equation(USLE)nomograph,and evaluate the spatial distribution of the predicted K-factor in a mountainous agricultural watershed.To investigate the K-factor,the 54 km2 study watershed was divided into a 500 m by 500 m square grid and approximately at the center of each grid,topsoil samples(roughly 10 to 20 cm depth)were collected over 234 locations.Sand,silt,clay and organic matter(OM)percentage were analyzed,while soil permeability and structure class codes were obtained using the United States Department of Agriculture(USDA)document.The resulting coefficient of variation(CV)of the estimated K-factor was 0.31,suggesting a moderate variability.Meanwhile,the value of nugget to sill ratio of K-factor was 0.32,which categorized as moderate spatial autocorrelation.Prediction accuracy and model fitting effect of the Gaussian semivariogram approach was best,suggesting that the Gaussian ordinary Kriging model was more appropriate for predicting Kfactor.The resulting value of the mean error(ME)was 0 and the mean squared deviation ratio(MSDR)was nearly 1,which indicates the Gaussian model was unbiased and reproduced the experimental variance sufficiently.The values of K-factor were smaller(0.0217 to 0.0188)in the northern part and gradually increased(0.0273 to 0.033 Mg h MJ^(-1)mm^(-1))towards the central and south of the study watershed.

Evaluation of semivariogram features for objectbased image classification

Inclusion of textures in image classification has been shown beneficial.This paper studies an efficient use of semivariogram features for object-based high-resolution image classification.First,an input image is divided into segments,for each of which a semivariogram is then calculated.Second,candidate features are extracted as a number of key locations of the semivariogram functions.Then we use an improved Relief algorithm and the principal component analysis to select independent and significant features.Then the selected prominent semivariogram features and the conventional spectral features are combined to constitute a feature vector for a support vector machine classifier.The effect of such selected semivariogram features is compared with those of the gray-level co-occurrence matrix(GLCM)features and window-based semivariogram texture features(STFs).Tests with aerial and satellite images show that such selected semivariogram features are of a more beneficial supplement to spectral features.The described method in this paper yields a higher classification accuracy than the combination of spectral and GLCM features or STFs.

Field investigation of intelligent compaction for hot mix asphalt resurfacing

Intelligent compaction （IC） is a relatively new technology for asphalt paving industry. The present study evaluated the effectiveness and potential issues of the IC technology for flexible pavement resurfacing construction using two field projects. In the first project, a geostatistical semivariogram model was established and the parameters derived from it were compared with univariate statistical parameters for the Compaction Meter Value （CMV） data. Further analyses illustrated the effect of temperature on the CMV value and compaction uniformity. In the second project, a multivariate analysis was performed between in situ tests and IC data. The possibility of combining various IC data to predict the asphalt layer density and improve the current quality control and assurance system was discussed.