The objective of this study is to develop an advanced approach to variogram modelling by integrating genetic algorithms(GA)with machine learning-based linear regression,aiming to improve the accuracy and efficiency of...The objective of this study is to develop an advanced approach to variogram modelling by integrating genetic algorithms(GA)with machine learning-based linear regression,aiming to improve the accuracy and efficiency of geostatistical analysis,particularly in mineral exploration.The study combines GA and machine learning to optimise variogram parameters,including range,sill,and nugget,by minimising the root mean square error(RMSE)and maximising the coefficient of determination(R^(2)).The experimental variograms were computed and modelled using theoretical models,followed by optimisation via evolutionary algorithms.The method was applied to gravity data from the Ngoura-Batouri-Kette mining district in Eastern Cameroon,covering 141 data points.Sequential Gaussian Simulations(SGS)were employed for predictive mapping to validate simulated results against true values.Key findings show variograms with ranges between 24.71 km and 49.77 km,opti-mised RMSE and R^(2) values of 11.21 mGal^(2) and 0.969,respectively,after 42 generations of GA optimisation.Predictive mapping using SGS demonstrated that simulated values closely matched true values,with the simu-lated mean at 21.75 mGal compared to the true mean of 25.16 mGal,and variances of 465.70 mGal^(2) and 555.28 mGal^(2),respectively.The results confirmed spatial variability and anisotropies in the N170-N210 directions,consistent with prior studies.This work presents a novel integration of GA and machine learning for variogram modelling,offering an automated,efficient approach to parameter estimation.The methodology significantly enhances predictive geostatistical models,contributing to the advancement of mineral exploration and improving the precision and speed of decision-making in the petroleum and mining industries.展开更多
Variogram plays a crucial role in remote sensing application and geostatistics.It is very important to estimate variogram reliably from sufficient data.In this study,the analysis of variograms computed on various samp...Variogram plays a crucial role in remote sensing application and geostatistics.It is very important to estimate variogram reliably from sufficient data.In this study,the analysis of variograms computed on various sample sizes of remotely sensed data was conducted.A 100×100-pixel subset was chosen randomly from an aerial multispectral image which contains three wavebands,Green,Red and near-infrared(NIR).Green,Red,NIR and Normalized Difference Vegetation Index(NDVI)datasets were imported into R software for spatial analysis.Variograms of these four full image datasets and sub-samples with simple random sampling method were investigated.In this case,half size of the subset image data was enough to reliably estimate the variograms for NIR and Red wavebands.To map the variation on NDVI within the weed field,ground sampling interval should be smaller than 12 m.The information will be particularly important for Kriging and also give a good guide of field sampling on the weed field in the future study.展开更多
Stochastic seismic inversion is the combination of geostatistics and seismic inversion technology which integrates information from seismic records, well logs, and geostatistics into a posterior probability density fu...Stochastic seismic inversion is the combination of geostatistics and seismic inversion technology which integrates information from seismic records, well logs, and geostatistics into a posterior probability density function （PDF） of subsurface models. The Markov chain Monte Carlo （MCMC） method is used to sample the posterior PDF and the subsurface model characteristics can be inferred by analyzing a set of the posterior PDF samples. In this paper, we first introduce the stochastic seismic inversion theory, discuss and analyze the four key parameters： seismic data signal-to-noise ratio （S/N）, variogram, the posterior PDF sample number, and well density, and propose the optimum selection of these parameters. The analysis results show that seismic data S/N adjusts the compromise between the influence of the seismic data and geostatistics on the inversion results, the variogram controls the smoothness of the inversion results, the posterior PDF sample number determines the reliability of the statistical characteristics derived from the samples, and well density influences the inversion uncertainty. Finally, the comparison between the stochastic seismic inversion and the deterministic model based seismic inversion indicates that the stochastic seismic inversion can provide more reliable information of the subsurface character.展开更多
Microbial indices and their spatial patterns are strongly affected by environmental factors. Spatial variability of soil properties is one of the most important causes of variability in soil microbial indices. This re...Microbial indices and their spatial patterns are strongly affected by environmental factors. Spatial variability of soil properties is one of the most important causes of variability in soil microbial indices. This research was conducted in the Caspian forest to assess spatial variabilities and frequency distributions of microbial properties.Ninety soil samples were taken using a grid sampling design 40 9 40 m. Microbial indices, organic carbon,nitrogen and pH were determined. Soil variable distributions showed that microbial indices had abnormal distributions. Logarithmic transformation produced normal distribution. Spatial continuity using geostatistical(variogram) was studied and maps obtained by point kriging.The variograms revealed the presence of spatial autocorrelation. The results indicate that spatial dependence of soil microbial indices was affected by non-intrinsic factors and forest management procedures. The maps show that soil microbial indices and soil properties have spatial variability. The spatial pattern of microbial indices was correlated to organic carbon and nitrogen.展开更多
Geostatistics provides a coherent framework for spatial prediction and uncertainty assessment, whereby spatial dependence, as quantified by variograms, is utilized for best linear unbiased estimation of a regionalized...Geostatistics provides a coherent framework for spatial prediction and uncertainty assessment, whereby spatial dependence, as quantified by variograms, is utilized for best linear unbiased estimation of a regionalized variable at unsampied locations. Geostatistics for prediction of continuous regionalized variables is reviewed, with key methods underlying the derivation of major variants of uni-vafiate Kriging described in an easy-to-follow manner. This paper will contribute to demysti- fication and, hence, popularization of geostatistics in geoinformatics communities.展开更多
This paper presents a three-dimensional geological reservoir model created using stochastic simulation. The oil field presented is an East African oil field formed by a structural trap. Data analysis and transformatio...This paper presents a three-dimensional geological reservoir model created using stochastic simulation. The oil field presented is an East African oil field formed by a structural trap. Data analysis and transformations were conducted on the properties before simulation. The variogram was used to measure the spatial correlation of cell-based facies modeling, and porosity and permeability modeling. Two main lithologies were modelled using sequential indicator simulation, sand and shale. Sand had a percentage of 26.8% and shale of 73.2%. There was a clear property distribution trend of sand and shale from the southwest to the northeastern part of a reservoir. The distribution trend of the facies resembled the proposed depositional model of the reservoir. Simulations show that average porosity and permeability of the reservoir are about 20% and 1004 mD, respectively. Average water saturation was 64%. STOIIP volume of 689.42 MMbbls was calculated. The results of simulation showed that the south eastern part of the reservoir holds higher volumes of oil. In conclusion, the model gave a better geological understanding of the geology of the area and can be used for decision making about the future development of the reservoir, prediction performance and uncertainty analysis.展开更多
Airborne particulates play a central role in both the earth’s radiation balance and as a trigger for a wide range of health impacts. Air quality monitors are placed in networks across many cities glob-ally. Typically...Airborne particulates play a central role in both the earth’s radiation balance and as a trigger for a wide range of health impacts. Air quality monitors are placed in networks across many cities glob-ally. Typically these provide at best a few recording locations per city. However, large spatial var-iability occurs on the neighborhood scale. This study sets out to comprehensively characterize a full size distribution from 0.25 - 32 μm of airborne particulates on a fine spatial scale (meters). The data are gathered on a near daily basis over the month of May, 2014 in a 100 km2 area encompassing parts of Richardson, and Garland, TX. Wind direction was determined to be the dominant factor in classifying the data. The highest mean PM2.5 concentration was 14.1 ± 5.7 μg·m-3 corresponding to periods when the wind was out of the south. The lowest PM2.5 concentrations were observed after several consecutive days of rainfall. The rainfall was found to not only “cleanse” the air, leaving a mean PM2.5 concentration as low as 3.0 ± 0.5 μg·m-3, but also leave the region with a more uniform PM2.5 concentration. Variograms were used to determine an appropriate spatial scale for future sensor placement to provide measurements on a neighborhood scale and found that the spatial scales varied, depending on the synoptic weather pattern, from 0.8 km to 5.2 km, with a typical length scale of 1.6 km.展开更多
The association of organic carbon with secondary particles （aggregates） results in its storage and retention in soil. A study was carried out at a catchment covering about 92 km2 to predict spatial variability of so...The association of organic carbon with secondary particles （aggregates） results in its storage and retention in soil. A study was carried out at a catchment covering about 92 km2 to predict spatial variability of soil water-stable aggregates （WSA）, mean weight diameter （MWD） of aggregates and organic carbon （OC） content in macro.- （〉 2 mm）, meso- （1-2 mm）, and micro-aggregate （〈 1 mm） fractions, using geostatistical methods. One hundred and eleven soil samples were eSlleeted at the 0 10cm depth and fractionated into macro-, meso-, and mlcro-aggregates by wet sieving. The OC content was determined for each fraction. A greater percentage of water-stable aggregates was found for micro-aggregates, followed by meso-aggregates. Aggregate OC content was greatest in meso-aggregates （9 g kg-1）, followed by micro-aggregates （7 g kg-1）, while the least OC content was found in macro-aggregates （3 g kg-1）. Although a significant effect （P = 0.000） of aggregate size on aggregate OC content was found, however, our findings did not support the model of aggregate hierarchy. Land use had a significant effect （P = 0.073） on aggregate OC content. The coefficients of variation （CVs） for OC contents associated with each aggregate fraction indicated macro-aggregates as the most variable （CV = 71%）. Among the aggregate fractions, the micro-aggregate fraction had a lower CV value of 27%. macro-aggregates to 84% for micro-aggregates. Geostatistical analysis differences in their spatial patterns in both magnitude and space at variance for most aggregate-associated properties was lower than 45%. The mean content of WSA ranged from 15% for showed that the measured soil variables exhibited each aggregate size fraction. The relative nugget The range value for the variogram of water-stable aggregates was almost similar （about 3 km） for the three studied aggregate size classes. The range value for the variogram of aggregate-associated OC contents ranged from about 3 km for macro-aggregates to about 6.5 km for meso-aggregates. Kriged maps of predicted WSA, OC and MWD for the three studied aggregate size fractions showed clear spatial patterns. However, a close spatial similarity （co-regionalization） was observed between WSA and MWD.展开更多
Due to the lack of regulation and environmental education and awareness, Sepahanshahr located in vicinity of Isfahan City, central Iran, is now a rapid growing residential area suffering from the considerable conseque...Due to the lack of regulation and environmental education and awareness, Sepahanshahr located in vicinity of Isfahan City, central Iran, is now a rapid growing residential area suffering from the considerable consequences of poorly regulated mining activities operating in its vicinity. A survey was carried out on soil Pb, Zn and Cd concentrations around Sepahanshahr Town to explore the spatial structure of Pb, Zn and Cd distribution and to map their concentrations using geostatistical techniques. 100 near-surface soil samples were collected and analyzed for Pb, Zn and Cd and some related soil physical and chemical variables such as pH, organic matter content, electrical conductivity, and clay, silt and sand contents. The variography results showed a strong spatial dependency in heavy metals concentration due to the dilution effects of natural factors including atmospheric dispersion and precipitation. The almost same range values calculated for both In-transformed Pb and sand data suggested presence of spatial co-regionalization. However, In-transformed Zn data showed a shorter spatial dependency among the three tested heavy metals. Kriged maps of all three heavy metals showed a strong gradient of contamination around the three mining sites activating in the area. The results of this study provide insight into identification of the extent and spatial variability of Pb, Zn and Cd pollution in the mining sites and surrounding urban areas.展开更多
Inferring the experimental variogram used in geostatistics commonly relies on the method-of-moments approach.Ideally,the available data-set used for calculating the experimental variogram should be drawn from a regula...Inferring the experimental variogram used in geostatistics commonly relies on the method-of-moments approach.Ideally,the available data-set used for calculating the experimental variogram should be drawn from a regular pattern.However,in practice the available data-set is typically sampled over a sparse pattern at irregularly spaced locations.Hence,some binning of the variogram cloud is required to obtain fair estimates of the experimental variogram.Grouping of the variogram data pairs as a result of conventional binning depends on parameters such as the main anisotropic directions and a regular definition of the lag vectors.These parameters are not based on the configuration of the variogram data pairs in the variogram cloud but on a segment of it that is arbitrarily predefined.Therefore,the conventional experimental variogram estimation approach is biased because of the strict configuration of the bins over the variogram cloud.In this paper,a new method of estimating experimental variograms is proposed.Lag vectors and their tolerances are decided in the proposed method from information in the variogram cloud：they are not influenced by any predefined directions.The proposed methodology is a well-founded,practicable and easy-to-automate approach for experimental variogram calculation using an irregularly sampled data-set.Comparison of results from the new method to those from the traditional approach is very encouraging.展开更多
Species richness and abundance are two important species diversity variables that have attracted particular attention because of their significance in determining present and future species composition conditions. Thi...Species richness and abundance are two important species diversity variables that have attracted particular attention because of their significance in determining present and future species composition conditions. This paper aims to explain the qualitative and quantitative relationships between species diversity pattern and grain size （i.e. size of the sampling unit）, and species diversity pattern and sampling area, and to analyze species diversity variability on active sand dunes in the Horqin Sandy Land, northeastern Inner Mongolia, China. A 50 mx50 m sampling plot was selected on the windward slope, where the dominant species was annual herb Agriophyllum squarrosum. Species composition and abundance at five grain sizes were recorded, and the species-area curves were produced for thirteen grain sizes. The range of values for species abundance tended to increase with in- creasing grain size in the study area, whereas, generally, species richness did not follow this rule because of poor species richness on the windward slope of active sand dunes. However, the homogeneity of species richness in- creased significantly. With the increase in sampling area, species abundance increased linearly, but richness in- creased logarithmically. Furthermore, variograms showed that species diversity on the windward slope of active sand dunes was weakly anisotropic and the distribution pattern was random, according to the Moran Coefficient. The results also showed that species richness was low, with a random distribution pattern. This conflicts with the results of previous studies that showed spatial aggregation in lower richness in a sampling area within a community and inferred that the physical processes play a more important role in species diversity than distribution pattern on active sand dunes. Further research into different diversity patterns and mechanisms between active sand dunes and interdune lowlands should be conducted to better understand biodiversity conservation in sand dune fields.展开更多
A structural analysis of K of an aquifer system in the study area is presented, and the main direction and degree of the variability of K are found by using the unstationary regionalized variable theory of geostatisti...A structural analysis of K of an aquifer system in the study area is presented, and the main direction and degree of the variability of K are found by using the unstationary regionalized variable theory of geostatistics. Optimal estimation of K has been made by universal kriging method (U K M ). Both spatial variability distribution map and division map of K are given.展开更多
Void ratio measures compactness of ground soil in geotechnical engineering. When samples are collected in certain area for mapping void ratios, other relevant types of properties such as water content may be also anal...Void ratio measures compactness of ground soil in geotechnical engineering. When samples are collected in certain area for mapping void ratios, other relevant types of properties such as water content may be also analyzed. To map the spatial distribution of void ratio in the area based on these types of point, observation data interpolation is often needed. Owing to the variance of sampling density along the horizontal and vertical directions, special consideration is required to handle anisotropy of estimator. 3D property modeling aims at predicting the overall distribution of property values from limited samples, and geostatistical method can be employed naturally here because they help to minimize the mean square error of estimation. To construct 3D property model of void ratio, cokriging was used considering its mutual correlation with water content, which is another important soil parameter. Moreover, K-D tree was adopted to organize the samples to accelerate neighbor query in 3D space during the above modeling process. At last, spatial configuration of void ratio distribution in an engineering body was modeled through 3D visualization, which provides important information for civil engineering purpose.展开更多
Sampling plays an important role in acquiring precise soil information required in modern agricultural production worldwide, which determines both the cost and quality of final soil mapping products. For sampling desi...Sampling plays an important role in acquiring precise soil information required in modern agricultural production worldwide, which determines both the cost and quality of final soil mapping products. For sampling design, it has been proposed possibile to transfer the relationships between kriging variance and sampling grid spacing from an area with existing information to other areas with similar soil-forming environments. However, this approach is challenged in practice because of two problems: i) different population vaxiograms among similar areas and ii) sampling errors in estimated variograms. This study evaluated the effects of these two problems on the transferability of the relationships between kriging variance and sampling grid spacing, by using spatial data simulated with three variograms and soil samples collected from four grasslands in Ireland with similar soil-forming environments. Results showed that the variograms suggested by different samples collected with the same grid spacing in the same or similar areas were different, leading to a range of mean kriging variance (MKV) for each grid spacing. With increasing grid spacing, the variation of MKV for a specific grid spacing increased and deviated more from the MKV generated using the population variograms. As a result, the spatial transferability of the relationships between kriging variance and grid spacing for sampling design was limited.展开更多
Knowledge of spatio-spectral heterogeneity within multisensor remote sensing images across visible,near-infrared and short wave infrared spectra is important.Till now,little comparative research on spatio-spectral het...Knowledge of spatio-spectral heterogeneity within multisensor remote sensing images across visible,near-infrared and short wave infrared spectra is important.Till now,little comparative research on spatio-spectral heterogeneity has been conducted on real multisensor images,especially on both multispectral and hyperspectral airborne images.In this study,four airborne images,Airborne Thematic Mapper,Compact Airborne Spectrographic Imager,Specim AISA Eagle and AISI Hawk hyperspectral airborne images of woodland and heath landscapes at Harwood,UK,were applied to quantify and evaluate the differences in spatial heterogeneity through semivariogram modelling.Results revealed that spatial heterogeneity of multisensor airborne images has a close relationship with spatial and spectral resolution and wavelength.Within the visible,near-infrared spectra and short wave infrared spectra,greater spatial heterogeneity is generally observed from the relatively longer wavelength in short wave infrared spectra.There are dramatic changes across the red and red edge spectra,and the peak value is generally examined in the red middle or red edge wavelength across the visible and near-infrared spectra for vegetation or non-vegetation landscape respectively.In all,for real multisensor airborne images,the change in spatial heterogeneity with spatial resolution will accord with the change of support theory depending on whether dramatic change exists across the corresponding wavelength.Besides,if with close spatial resolution,the spatial heterogeneity of multispectral images might be far from the overall integration of these bands from the hyperspectral images involved.A comparative assessment of spatio-spectral heterogeneity using real hyperspectral and multispectral airborne images provides practical guidance for designing the placement and width of a spectral band for different applications and also makes a contribution to the understanding of how to reconcile spatial patterns generated by multisensors.展开更多
In various environmental studies, geoscience variables not only have the characteristics of time and space, but also are influenced by other variables. Multivariate spatiotemporal variables can improve the accuracy of...In various environmental studies, geoscience variables not only have the characteristics of time and space, but also are influenced by other variables. Multivariate spatiotemporal variables can improve the accuracy of spatiotemporal estimation. Taking the monthly mean ground observation data of the period 1960–2013 precipitation in the Xinjiang Uygur Autonomous Region, China, the spatiotemporal distribution from January to December in 2013 was respectively estimated by space-time Kriging and space-time CoKriging. Modeling spatiotemporal direct variograms and a cross variogram was a key step in space-time CoKriging. Taking the monthly mean air relative humidity of the same site at the same time as the covariates, the spatiotemporal direct variograms and the spatiotemporal cross variogram of the monthly mean precipitation for the period 1960–2013 were modeled. The experimental results show that the space-time CoKriging reduces the mean square error by 31.46% compared with the space-time ordinary Kriging. The correlation coefficient between the estimated values and the observed values of the space-time CoKriging is 5.07% higher than the one of the space-time ordinary Kriging. Therefore, a space-time CoKriging interpolation with air humidity as a covariate improves the interpolation accuracy.展开更多
The morphological characteristics of small-scale bedforms were measured by means of an acoustic profiling sonar on the Dafeng tidal flat, Jiangsu, in 2009, and in the Jiulong Estuary, Xiamen, in 2010, respectively. T...The morphological characteristics of small-scale bedforms were measured by means of an acoustic profiling sonar on the Dafeng tidal flat, Jiangsu, in 2009, and in the Jiulong Estuary, Xiamen, in 2010, respectively. The ＂multi-threshold value＂ method was utilized to reveal the morphological undulations along which bedforms were present. Analyses of the datasets obtained show that： （1） sand ripples can have irregular shapes, and （2） changes in bedform morphology are small within a single tidal cycle but may be significant over several tidal cycles. Fractal and variogram analyses of the seabed roughness revealed the existence of a significant relationship between current speed and the fractal dimension of the seabed roughness. As current speed increases, seabed roughness increases with a trend of smaller-scale bottom structures being replaced by larger-scale structures. Furthermore, the surface of the larger-scale bottom structures can either become smooth due to the absence of smaller-scale features or become rougher due to the presence of superimposed smaller-scale structures.展开更多
In this study, the petrophysical parameters such as density, sonic, neutron, and porosity were investigated and presented in the 3D models. The 3D models were built using geostatistical method that is used to estimate...In this study, the petrophysical parameters such as density, sonic, neutron, and porosity were investigated and presented in the 3D models. The 3D models were built using geostatistical method that is used to estimate studied parameters in the entire reservoir. For this purpose, the variogram of each parameter was determined to specify spatial correlation of data. Resulted variograms were non-monotonic. That shows anisotropy of structure. The lithology and porosity parameters are the main causes of this anisotropy. The 3D models also show that petrophysical data has higher variation in north part of reservoir than south part. In addition to, the west limb of reservoir shows higher porosity than east limb. The variation of sonic and neutron data are similar whereas the density data has opposed variation.展开更多
The clastic sedimentary realm comprises a number of genetically distinct depositional systems, which are dominated by distinct depositional processes. A variogram and a Levy-stable probability distribution-based geost...The clastic sedimentary realm comprises a number of genetically distinct depositional systems, which are dominated by distinct depositional processes. A variogram and a Levy-stable probability distribution-based geostatistical method have been applied to analyze petrophysical properties from well logs and cores from a variety of depositional environments in sedimentary basins from Australia to quantify the heterogeneity and upscaling range of different depositional systems. Two reservoir sequences with contrasting sedimentary facies, depositional processes and a diagenetic history are investigated for their petrographic, petrophysical and log characters and their scaling behaviour. The microscopic derived petrophysical parameters, including visual porosity, grain size, sorting and amount of matrix, core plug measured porosity and permeability and log-derived V-shale, porosity and permeability, have been found to be well correlated (|R|=0.72 to 0.91) across all the scales for the reservoir sequence deposited under a single predominant depositional process and a gradational change of the energy regime (Bilyara-1). In contrast, for the reservoir sequence (East Swan-2), which was deposited in heterogeneous processes and underwent diagenetic alteration, the cross-correlation of the petrophysical properties derived from the three different scales is extremely poor (|R|=0.01-0.54). Log-derived porosity and permeability for a thinly bedded reservoir sequence with an individual bed thinner than one metre can therefore be affected by the intrinsic averaging effects of the logging tools.展开更多
文摘The objective of this study is to develop an advanced approach to variogram modelling by integrating genetic algorithms(GA)with machine learning-based linear regression,aiming to improve the accuracy and efficiency of geostatistical analysis,particularly in mineral exploration.The study combines GA and machine learning to optimise variogram parameters,including range,sill,and nugget,by minimising the root mean square error(RMSE)and maximising the coefficient of determination(R^(2)).The experimental variograms were computed and modelled using theoretical models,followed by optimisation via evolutionary algorithms.The method was applied to gravity data from the Ngoura-Batouri-Kette mining district in Eastern Cameroon,covering 141 data points.Sequential Gaussian Simulations(SGS)were employed for predictive mapping to validate simulated results against true values.Key findings show variograms with ranges between 24.71 km and 49.77 km,opti-mised RMSE and R^(2) values of 11.21 mGal^(2) and 0.969,respectively,after 42 generations of GA optimisation.Predictive mapping using SGS demonstrated that simulated values closely matched true values,with the simu-lated mean at 21.75 mGal compared to the true mean of 25.16 mGal,and variances of 465.70 mGal^(2) and 555.28 mGal^(2),respectively.The results confirmed spatial variability and anisotropies in the N170-N210 directions,consistent with prior studies.This work presents a novel integration of GA and machine learning for variogram modelling,offering an automated,efficient approach to parameter estimation.The methodology significantly enhances predictive geostatistical models,contributing to the advancement of mineral exploration and improving the precision and speed of decision-making in the petroleum and mining industries.
文摘Variogram plays a crucial role in remote sensing application and geostatistics.It is very important to estimate variogram reliably from sufficient data.In this study,the analysis of variograms computed on various sample sizes of remotely sensed data was conducted.A 100×100-pixel subset was chosen randomly from an aerial multispectral image which contains three wavebands,Green,Red and near-infrared(NIR).Green,Red,NIR and Normalized Difference Vegetation Index(NDVI)datasets were imported into R software for spatial analysis.Variograms of these four full image datasets and sub-samples with simple random sampling method were investigated.In this case,half size of the subset image data was enough to reliably estimate the variograms for NIR and Red wavebands.To map the variation on NDVI within the weed field,ground sampling interval should be smaller than 12 m.The information will be particularly important for Kriging and also give a good guide of field sampling on the weed field in the future study.
基金supported by the National Major Science and Technology Project of China on Development of Big Oil-Gas Fields and Coalbed Methane (No. 2008ZX05010-002)
文摘Stochastic seismic inversion is the combination of geostatistics and seismic inversion technology which integrates information from seismic records, well logs, and geostatistics into a posterior probability density function （PDF） of subsurface models. The Markov chain Monte Carlo （MCMC） method is used to sample the posterior PDF and the subsurface model characteristics can be inferred by analyzing a set of the posterior PDF samples. In this paper, we first introduce the stochastic seismic inversion theory, discuss and analyze the four key parameters： seismic data signal-to-noise ratio （S/N）, variogram, the posterior PDF sample number, and well density, and propose the optimum selection of these parameters. The analysis results show that seismic data S/N adjusts the compromise between the influence of the seismic data and geostatistics on the inversion results, the variogram controls the smoothness of the inversion results, the posterior PDF sample number determines the reliability of the statistical characteristics derived from the samples, and well density influences the inversion uncertainty. Finally, the comparison between the stochastic seismic inversion and the deterministic model based seismic inversion indicates that the stochastic seismic inversion can provide more reliable information of the subsurface character.
基金the Iran National Science Foundation (INFS) which provided financial support through various steps of our research
文摘Microbial indices and their spatial patterns are strongly affected by environmental factors. Spatial variability of soil properties is one of the most important causes of variability in soil microbial indices. This research was conducted in the Caspian forest to assess spatial variabilities and frequency distributions of microbial properties.Ninety soil samples were taken using a grid sampling design 40 9 40 m. Microbial indices, organic carbon,nitrogen and pH were determined. Soil variable distributions showed that microbial indices had abnormal distributions. Logarithmic transformation produced normal distribution. Spatial continuity using geostatistical(variogram) was studied and maps obtained by point kriging.The variograms revealed the presence of spatial autocorrelation. The results indicate that spatial dependence of soil microbial indices was affected by non-intrinsic factors and forest management procedures. The maps show that soil microbial indices and soil properties have spatial variability. The spatial pattern of microbial indices was correlated to organic carbon and nitrogen.
基金the National 973 Program of China (No. 2007CB714402-5).
文摘Geostatistics provides a coherent framework for spatial prediction and uncertainty assessment, whereby spatial dependence, as quantified by variograms, is utilized for best linear unbiased estimation of a regionalized variable at unsampied locations. Geostatistics for prediction of continuous regionalized variables is reviewed, with key methods underlying the derivation of major variants of uni-vafiate Kriging described in an easy-to-follow manner. This paper will contribute to demysti- fication and, hence, popularization of geostatistics in geoinformatics communities.
文摘This paper presents a three-dimensional geological reservoir model created using stochastic simulation. The oil field presented is an East African oil field formed by a structural trap. Data analysis and transformations were conducted on the properties before simulation. The variogram was used to measure the spatial correlation of cell-based facies modeling, and porosity and permeability modeling. Two main lithologies were modelled using sequential indicator simulation, sand and shale. Sand had a percentage of 26.8% and shale of 73.2%. There was a clear property distribution trend of sand and shale from the southwest to the northeastern part of a reservoir. The distribution trend of the facies resembled the proposed depositional model of the reservoir. Simulations show that average porosity and permeability of the reservoir are about 20% and 1004 mD, respectively. Average water saturation was 64%. STOIIP volume of 689.42 MMbbls was calculated. The results of simulation showed that the south eastern part of the reservoir holds higher volumes of oil. In conclusion, the model gave a better geological understanding of the geology of the area and can be used for decision making about the future development of the reservoir, prediction performance and uncertainty analysis.
文摘Airborne particulates play a central role in both the earth’s radiation balance and as a trigger for a wide range of health impacts. Air quality monitors are placed in networks across many cities glob-ally. Typically these provide at best a few recording locations per city. However, large spatial var-iability occurs on the neighborhood scale. This study sets out to comprehensively characterize a full size distribution from 0.25 - 32 μm of airborne particulates on a fine spatial scale (meters). The data are gathered on a near daily basis over the month of May, 2014 in a 100 km2 area encompassing parts of Richardson, and Garland, TX. Wind direction was determined to be the dominant factor in classifying the data. The highest mean PM2.5 concentration was 14.1 ± 5.7 μg·m-3 corresponding to periods when the wind was out of the south. The lowest PM2.5 concentrations were observed after several consecutive days of rainfall. The rainfall was found to not only “cleanse” the air, leaving a mean PM2.5 concentration as low as 3.0 ± 0.5 μg·m-3, but also leave the region with a more uniform PM2.5 concentration. Variograms were used to determine an appropriate spatial scale for future sensor placement to provide measurements on a neighborhood scale and found that the spatial scales varied, depending on the synoptic weather pattern, from 0.8 km to 5.2 km, with a typical length scale of 1.6 km.
基金Supported by Shahrekord University, Shahrekord, Iran
文摘The association of organic carbon with secondary particles （aggregates） results in its storage and retention in soil. A study was carried out at a catchment covering about 92 km2 to predict spatial variability of soil water-stable aggregates （WSA）, mean weight diameter （MWD） of aggregates and organic carbon （OC） content in macro.- （〉 2 mm）, meso- （1-2 mm）, and micro-aggregate （〈 1 mm） fractions, using geostatistical methods. One hundred and eleven soil samples were eSlleeted at the 0 10cm depth and fractionated into macro-, meso-, and mlcro-aggregates by wet sieving. The OC content was determined for each fraction. A greater percentage of water-stable aggregates was found for micro-aggregates, followed by meso-aggregates. Aggregate OC content was greatest in meso-aggregates （9 g kg-1）, followed by micro-aggregates （7 g kg-1）, while the least OC content was found in macro-aggregates （3 g kg-1）. Although a significant effect （P = 0.000） of aggregate size on aggregate OC content was found, however, our findings did not support the model of aggregate hierarchy. Land use had a significant effect （P = 0.073） on aggregate OC content. The coefficients of variation （CVs） for OC contents associated with each aggregate fraction indicated macro-aggregates as the most variable （CV = 71%）. Among the aggregate fractions, the micro-aggregate fraction had a lower CV value of 27%. macro-aggregates to 84% for micro-aggregates. Geostatistical analysis differences in their spatial patterns in both magnitude and space at variance for most aggregate-associated properties was lower than 45%. The mean content of WSA ranged from 15% for showed that the measured soil variables exhibited each aggregate size fraction. The relative nugget The range value for the variogram of water-stable aggregates was almost similar （about 3 km） for the three studied aggregate size classes. The range value for the variogram of aggregate-associated OC contents ranged from about 3 km for macro-aggregates to about 6.5 km for meso-aggregates. Kriged maps of predicted WSA, OC and MWD for the three studied aggregate size fractions showed clear spatial patterns. However, a close spatial similarity （co-regionalization） was observed between WSA and MWD.
文摘Due to the lack of regulation and environmental education and awareness, Sepahanshahr located in vicinity of Isfahan City, central Iran, is now a rapid growing residential area suffering from the considerable consequences of poorly regulated mining activities operating in its vicinity. A survey was carried out on soil Pb, Zn and Cd concentrations around Sepahanshahr Town to explore the spatial structure of Pb, Zn and Cd distribution and to map their concentrations using geostatistical techniques. 100 near-surface soil samples were collected and analyzed for Pb, Zn and Cd and some related soil physical and chemical variables such as pH, organic matter content, electrical conductivity, and clay, silt and sand contents. The variography results showed a strong spatial dependency in heavy metals concentration due to the dilution effects of natural factors including atmospheric dispersion and precipitation. The almost same range values calculated for both In-transformed Pb and sand data suggested presence of spatial co-regionalization. However, In-transformed Zn data showed a shorter spatial dependency among the three tested heavy metals. Kriged maps of all three heavy metals showed a strong gradient of contamination around the three mining sites activating in the area. The results of this study provide insight into identification of the extent and spatial variability of Pb, Zn and Cd pollution in the mining sites and surrounding urban areas.
文摘Inferring the experimental variogram used in geostatistics commonly relies on the method-of-moments approach.Ideally,the available data-set used for calculating the experimental variogram should be drawn from a regular pattern.However,in practice the available data-set is typically sampled over a sparse pattern at irregularly spaced locations.Hence,some binning of the variogram cloud is required to obtain fair estimates of the experimental variogram.Grouping of the variogram data pairs as a result of conventional binning depends on parameters such as the main anisotropic directions and a regular definition of the lag vectors.These parameters are not based on the configuration of the variogram data pairs in the variogram cloud but on a segment of it that is arbitrarily predefined.Therefore,the conventional experimental variogram estimation approach is biased because of the strict configuration of the bins over the variogram cloud.In this paper,a new method of estimating experimental variograms is proposed.Lag vectors and their tolerances are decided in the proposed method from information in the variogram cloud：they are not influenced by any predefined directions.The proposed methodology is a well-founded,practicable and easy-to-automate approach for experimental variogram calculation using an irregularly sampled data-set.Comparison of results from the new method to those from the traditional approach is very encouraging.
基金funded by the National Natural Science Foundation of China (41071187)the State Forestry Administration Industry Special Project (201004023)
文摘Species richness and abundance are two important species diversity variables that have attracted particular attention because of their significance in determining present and future species composition conditions. This paper aims to explain the qualitative and quantitative relationships between species diversity pattern and grain size （i.e. size of the sampling unit）, and species diversity pattern and sampling area, and to analyze species diversity variability on active sand dunes in the Horqin Sandy Land, northeastern Inner Mongolia, China. A 50 mx50 m sampling plot was selected on the windward slope, where the dominant species was annual herb Agriophyllum squarrosum. Species composition and abundance at five grain sizes were recorded, and the species-area curves were produced for thirteen grain sizes. The range of values for species abundance tended to increase with in- creasing grain size in the study area, whereas, generally, species richness did not follow this rule because of poor species richness on the windward slope of active sand dunes. However, the homogeneity of species richness in- creased significantly. With the increase in sampling area, species abundance increased linearly, but richness in- creased logarithmically. Furthermore, variograms showed that species diversity on the windward slope of active sand dunes was weakly anisotropic and the distribution pattern was random, according to the Moran Coefficient. The results also showed that species richness was low, with a random distribution pattern. This conflicts with the results of previous studies that showed spatial aggregation in lower richness in a sampling area within a community and inferred that the physical processes play a more important role in species diversity than distribution pattern on active sand dunes. Further research into different diversity patterns and mechanisms between active sand dunes and interdune lowlands should be conducted to better understand biodiversity conservation in sand dune fields.
文摘A structural analysis of K of an aquifer system in the study area is presented, and the main direction and degree of the variability of K are found by using the unstationary regionalized variable theory of geostatistics. Optimal estimation of K has been made by universal kriging method (U K M ). Both spatial variability distribution map and division map of K are given.
基金supported by Beijing Multi-parameters 3D Geological Survey Program (No. 200313000045)
文摘Void ratio measures compactness of ground soil in geotechnical engineering. When samples are collected in certain area for mapping void ratios, other relevant types of properties such as water content may be also analyzed. To map the spatial distribution of void ratio in the area based on these types of point, observation data interpolation is often needed. Owing to the variance of sampling density along the horizontal and vertical directions, special consideration is required to handle anisotropy of estimator. 3D property modeling aims at predicting the overall distribution of property values from limited samples, and geostatistical method can be employed naturally here because they help to minimize the mean square error of estimation. To construct 3D property model of void ratio, cokriging was used considering its mutual correlation with water content, which is another important soil parameter. Moreover, K-D tree was adopted to organize the samples to accelerate neighbor query in 3D space during the above modeling process. At last, spatial configuration of void ratio distribution in an engineering body was modeled through 3D visualization, which provides important information for civil engineering purpose.
基金?nancially supported by the National Natural Science Foundation of China (Nos. 41541006 and 41771246)co-funded by Enterprise Ireland and the European Regional Development Fund (ERDF) under the National Strategic Reference Framework (NSRF) 2007–2013
文摘Sampling plays an important role in acquiring precise soil information required in modern agricultural production worldwide, which determines both the cost and quality of final soil mapping products. For sampling design, it has been proposed possibile to transfer the relationships between kriging variance and sampling grid spacing from an area with existing information to other areas with similar soil-forming environments. However, this approach is challenged in practice because of two problems: i) different population vaxiograms among similar areas and ii) sampling errors in estimated variograms. This study evaluated the effects of these two problems on the transferability of the relationships between kriging variance and sampling grid spacing, by using spatial data simulated with three variograms and soil samples collected from four grasslands in Ireland with similar soil-forming environments. Results showed that the variograms suggested by different samples collected with the same grid spacing in the same or similar areas were different, leading to a range of mean kriging variance (MKV) for each grid spacing. With increasing grid spacing, the variation of MKV for a specific grid spacing increased and deviated more from the MKV generated using the population variograms. As a result, the spatial transferability of the relationships between kriging variance and grid spacing for sampling design was limited.
基金This research is funded by Chinese National Natural Science Foundation(Grant No.41071267)Scientific Research Foundation for Returned Scholars,Ministry of Education of China([2012]940)+1 种基金the Science&technology department of Fujian province of China(Grant Nos.2012I0005,2012J01167)The authors would like to thank the Natural Environment Research Council of UK for the provision of the airborne remote sensing data.Part of the work for this study was carried out while Qiu Bingwen was a Visiting Scholar at the Department of Geography,University of Cambridge,England.The authors would like to acknowledge the advice of Robert Haining during her visit and to thank Ben Taylor and Gabriel Amable who kindly offered help in processing these datasets.
文摘Knowledge of spatio-spectral heterogeneity within multisensor remote sensing images across visible,near-infrared and short wave infrared spectra is important.Till now,little comparative research on spatio-spectral heterogeneity has been conducted on real multisensor images,especially on both multispectral and hyperspectral airborne images.In this study,four airborne images,Airborne Thematic Mapper,Compact Airborne Spectrographic Imager,Specim AISA Eagle and AISI Hawk hyperspectral airborne images of woodland and heath landscapes at Harwood,UK,were applied to quantify and evaluate the differences in spatial heterogeneity through semivariogram modelling.Results revealed that spatial heterogeneity of multisensor airborne images has a close relationship with spatial and spectral resolution and wavelength.Within the visible,near-infrared spectra and short wave infrared spectra,greater spatial heterogeneity is generally observed from the relatively longer wavelength in short wave infrared spectra.There are dramatic changes across the red and red edge spectra,and the peak value is generally examined in the red middle or red edge wavelength across the visible and near-infrared spectra for vegetation or non-vegetation landscape respectively.In all,for real multisensor airborne images,the change in spatial heterogeneity with spatial resolution will accord with the change of support theory depending on whether dramatic change exists across the corresponding wavelength.Besides,if with close spatial resolution,the spatial heterogeneity of multispectral images might be far from the overall integration of these bands from the hyperspectral images involved.A comparative assessment of spatio-spectral heterogeneity using real hyperspectral and multispectral airborne images provides practical guidance for designing the placement and width of a spectral band for different applications and also makes a contribution to the understanding of how to reconcile spatial patterns generated by multisensors.
基金Project(17D02)supported by the Open Fund of State Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,ChinaProject supported by the State Key Laboratory of Satellite Navigation System and Equipment Technology,China
文摘In various environmental studies, geoscience variables not only have the characteristics of time and space, but also are influenced by other variables. Multivariate spatiotemporal variables can improve the accuracy of spatiotemporal estimation. Taking the monthly mean ground observation data of the period 1960–2013 precipitation in the Xinjiang Uygur Autonomous Region, China, the spatiotemporal distribution from January to December in 2013 was respectively estimated by space-time Kriging and space-time CoKriging. Modeling spatiotemporal direct variograms and a cross variogram was a key step in space-time CoKriging. Taking the monthly mean air relative humidity of the same site at the same time as the covariates, the spatiotemporal direct variograms and the spatiotemporal cross variogram of the monthly mean precipitation for the period 1960–2013 were modeled. The experimental results show that the space-time CoKriging reduces the mean square error by 31.46% compared with the space-time ordinary Kriging. The correlation coefficient between the estimated values and the observed values of the space-time CoKriging is 5.07% higher than the one of the space-time ordinary Kriging. Therefore, a space-time CoKriging interpolation with air humidity as a covariate improves the interpolation accuracy.
基金supported by the National Natural Science Foundation of China (Grant Nos. 40876043,40976051 andJ1103408)Public Science and Technology Research Funds Projects of Ocean (Grant No. 201105001-2)the Priority Academic Program Development of Jiangsu Higher Education Institutions fund
文摘The morphological characteristics of small-scale bedforms were measured by means of an acoustic profiling sonar on the Dafeng tidal flat, Jiangsu, in 2009, and in the Jiulong Estuary, Xiamen, in 2010, respectively. The ＂multi-threshold value＂ method was utilized to reveal the morphological undulations along which bedforms were present. Analyses of the datasets obtained show that： （1） sand ripples can have irregular shapes, and （2） changes in bedform morphology are small within a single tidal cycle but may be significant over several tidal cycles. Fractal and variogram analyses of the seabed roughness revealed the existence of a significant relationship between current speed and the fractal dimension of the seabed roughness. As current speed increases, seabed roughness increases with a trend of smaller-scale bottom structures being replaced by larger-scale structures. Furthermore, the surface of the larger-scale bottom structures can either become smooth due to the absence of smaller-scale features or become rougher due to the presence of superimposed smaller-scale structures.
文摘In this study, the petrophysical parameters such as density, sonic, neutron, and porosity were investigated and presented in the 3D models. The 3D models were built using geostatistical method that is used to estimate studied parameters in the entire reservoir. For this purpose, the variogram of each parameter was determined to specify spatial correlation of data. Resulted variograms were non-monotonic. That shows anisotropy of structure. The lithology and porosity parameters are the main causes of this anisotropy. The 3D models also show that petrophysical data has higher variation in north part of reservoir than south part. In addition to, the west limb of reservoir shows higher porosity than east limb. The variation of sonic and neutron data are similar whereas the density data has opposed variation.
基金with the financial support of the key laboratory of petroleum accumulation mechanism of the Education Minstry University of Petroleum (Beijing)China
文摘The clastic sedimentary realm comprises a number of genetically distinct depositional systems, which are dominated by distinct depositional processes. A variogram and a Levy-stable probability distribution-based geostatistical method have been applied to analyze petrophysical properties from well logs and cores from a variety of depositional environments in sedimentary basins from Australia to quantify the heterogeneity and upscaling range of different depositional systems. Two reservoir sequences with contrasting sedimentary facies, depositional processes and a diagenetic history are investigated for their petrographic, petrophysical and log characters and their scaling behaviour. The microscopic derived petrophysical parameters, including visual porosity, grain size, sorting and amount of matrix, core plug measured porosity and permeability and log-derived V-shale, porosity and permeability, have been found to be well correlated (|R|=0.72 to 0.91) across all the scales for the reservoir sequence deposited under a single predominant depositional process and a gradational change of the energy regime (Bilyara-1). In contrast, for the reservoir sequence (East Swan-2), which was deposited in heterogeneous processes and underwent diagenetic alteration, the cross-correlation of the petrophysical properties derived from the three different scales is extremely poor (|R|=0.01-0.54). Log-derived porosity and permeability for a thinly bedded reservoir sequence with an individual bed thinner than one metre can therefore be affected by the intrinsic averaging effects of the logging tools.
