共查询到20条相似文献，搜索用时 31 毫秒
1.
Soil pH plays an important role in biogeochemical processes in soils. The spatial distribution of soil pH provides basic and useful information relevant to soil management and agricultural production. To obtain an accurate distribution map of soil pH on the Loess Plateau of China, 382 sampling sites were investigated throughout the region and four interpolation methods, i.e., inverse distance weighting (IDW), splines, ordinary kriging, and cokriging, were applied to produce a continuous soil pH surface. In the study region, soil pH values ranged from 6.06 to 10.76, with a mean of 8.49 and a median of 8.48. Land use type had a significant effect (p < 0.01) on soil pH; grassland soils had higher pHs than cropland and forestland soils. From a regional perspective, soil pH showed weak variation and strong spatial dependence, indicated by the low values of the coefficient of variation (0.05) and the nugget-to-sill ratios (<0.25). Indices of cross-validation, i.e., average error, mean absolute error, root mean square error, and model efficiency coefficient were used to compare the performance of the four different interpolation methods. Kriging methods interpolated more accurately than IDW and splines. Cokriging performed better than ordinary kriging and the accuracy was improved using soil organic carbon as an auxiliary variable. Regional distribution maps of soil pH were produced. The southeastern part of the region had relatively low soil pH values, probably due to higher precipitation, leaching, and higher soil organic matter contents. Areas of high soil pH were located in the north of the central part of the region, possibly associated with the salinization of sandy soils under inappropriate irrigation practices in an arid climate. Map accuracy could be further improved using new methods and incorporating other auxiliary variables, such as precipitation, elevation, terrain attributes, and vegetation types. 相似文献
2.
Evaluation of Interpolation Accuracy of Neural Kriging with Application to Temperature-Distribution Analysis 总被引：8，自引：0，他引：8
An interpolation method based on a multilayer neural network (MNN), has been examined and tested for the data of irregular sample locations. The main advantage of MNN is in that it can deal with geoscience data with nonlinear behavior and extract characteristics from complex and noisy images. The training of MNN is used to modify connection weights between nodes located in different layers by a simulated annealing algorithm (one of the optimization algorithms of the network). In this process, three types of errors are considered: differences in values, semivariograms, and gradients between sample data and outputs from the trained network. The training is continued until the summation of these errors converges to an acceptably small value. Because the MNN trained by this learning criterion can estimate a value at an arbitrary location, this method is a form of kriging and termed Neural Kriging (NK). In order to evaluate the effectiveness of NK, a problem on restoration ability of a defined reference surface from randomly chosen discrete data was prepared. Two types of surfaces, whose semivariograms are expressed by isotropic spherical and geometric anisotropic gaussian models, were examined in this problem. Though the interpolation accuracy depended on the arrangement pattern of the sample locations for the same number of data, the interpolation errors of NK were shown to be smaller than both those of ordinary MNN and ordinal kriging. NK can also produce a contour map in consideration of gradient constraints. Furthermore, NK was applied to distribution analysis of subsurface temperatures using geothermal investigation loggings of the Hohi area in southwest Japan. In spite of the restricted quantity of sample data, the interpolation results revealed high temperature zones and convection patterns of hydrothermal fluids. NK is regarded as an interpolation method with high accuracy that can be used for regionalized variables with any structure of spatial correlation. 相似文献
3.
Jiachun Shi Haizhen Wang Jianming Xu Jianjun Wu Xingmei Liu Haiping Zhu Chunlan Yu 《Environmental Geology》2007,52(1):1-10
Six hundred and sixty-five soil samples were taken from Changxing County in Zhejiang Province, China, to characterize the
spatial variability of Hg Cd, Pb, Cu, As and Cr. The geostatistics and geographic information system (GIS) techniques were
applied, and the ordinary kriging and lognormal kriging were used to map the spatial patterns of the six heavy metals. Hg,
Pb, Cu and As were fitted to the spherical model with a range of 85.75, 82.32, 86.10, and 23.17 km, respectively. Cr was fitted
to the exponential model with a range of 6.27 km, and Cd was fitted to the linear model with a range of 37.66 km. Both Pb
and Cu had strong spatial dependence due to the effects of natural factors including parent material, topography and soil
type. Hg, Cd, Cr and As had, however, moderate spatial dependence, indicating an involvement of human factors. Meanwhile,
based on the comparison between the original data and the guide values of the six metals, the disjunctive kriging technique
was used to quantify their pollution risks. The results showed that only Cd and Hg exhibited pollution risks in the study
area. The pollution source evaluated was closely corresponded with the real discharge of industrial production and the application
of organomercury pesticides. The results of this study provide insight into risk assessment of environmental pollution and
decision making for agricultural production and industrial adjustment of building materials. 相似文献
4.
This study compares kriging and maximum entropy estimators for spatial estimation and monitoring network design. For second-order
stationary random fields (a subset of Gaussian fields) the estimators and their associated interpolation error variances are
identical. Simple lognormal kriging differs from the lognormal maximum entropy estimator, however, in both mathematical formulation
and estimation error variances. Two numerical examples are described that compare the two estimators. Simple lognormal kriging
yields systematically higher estimates and smoother interpolation surfaces compared to those produced by the lognormal maximum
entropy estimator. The second empirical comparison applies kriging and entropy-based models to the problem of optimizing groundwater
monitoring network design, using six alternative objective functions. The maximum entropy-based sampling design approach is
shown to be the more computationally efficient of the two. 相似文献
5.
Abdullah Arik 《Geotechnical and Geological Engineering》1990,8(4):305-318
Summary Reliable ore reserve estimates for deposits with highly skewed grade distributions are difficult tasks to perform. Although some recent geostatistical techniques are available to handle problems with these estimations, ordinary kriging or conventional interpolation methods are still widely used to estimate the ore reserves for such deposits. The estimation results can be very sensitive to the search parameters used during the interpolation of grades with these methods.This paper compares the ore reserve estimates from ordinary kriging using several cases in which certain search parameters are varied. The comparisons are extended to different mineralizations to show the changing effects of these parameters. 相似文献
6.
Chris Roth 《Mathematical Geology》1998,30(8):999-1009
Lognormal kriging was developed early in geostatistics to take account of the often seen skewed distribution of the experimental mining data. Intuitively, taking the distribution of the data into account should lead to a better local estimate than that which would have been obtained when it is ignored. In practice however, the results obtained are sometimes disappointing. This paper tries to explain why this is so from the behavior of the lognormal kriging estimator. The estimator is shown to respect certain unbiasedness properties when considering the whole working field using the regression curve and its confidence interval for both simple or ordinary kriging. When examined locally, however, the estimator presents a behavior that is neither expected nor intuitive. These results lead to the question: is the theoretically correct lognormal kriging estimator suited to the practical problem of local estimation? 相似文献
7.
Jorge Kazuo Yamamoto 《Mathematical Geology》2000,32(4):489-509
This paper presents an interpolation variance as an alternative to the measure of the reliability of ordinary kriging estimates. Contrary to the traditional kriging variance, the interpolation variance is data-values dependent, variogram dependent, and a measure of local accuracy. Natural phenomena are not homogeneous; therefore, local variability as expressed through data values must be recognized for a correct assessment of uncertainty. The interpolation variance is simply the weighted average of the squared differences between data values and the retained estimate. Ordinary kriging or simple kriging variances are the expected values of interpolation variances; therefore, these traditional homoscedastic estimation variances cannot properly measure local data dispersion. More precisely, the interpolation variance is an estimate of the local conditional variance, when the ordinary kriging weights are interpreted as conditional probabilities associated to the n neighboring data. This interpretation is valid if, and only if, all ordinary kriging weights are positive or constrained to be such. Extensive tests illustrate that the interpolation variance is a useful alternative to the traditional kriging variance. 相似文献
8.
On unbiased backtransform of lognormal kriging estimates 总被引：4，自引：0，他引：4
Jorge Kazuo Yamamoto 《Computational Geosciences》2007,11(3):219-234
Lognormal kriging is an estimation technique that was devised for handling highly skewed data distributions. This technique
takes advantage of a logarithmic transformation that reduces the data variance. However, backtransformed lognormal kriging
estimates are biased because the nonbias term is totally dependent on a semivariogram model. This paper proposes a new approach
for backtransforming lognormal kriging estimates that not only presents none of the problems reported in the literature but
also reproduces the sample histogram and, consequently, the sample mean. 相似文献
9.
Goovaerts P 《Mathematical Geosciences》2010,42(5):535-554
A common issue in spatial interpolation is the combination of data measured over different spatial supports. For example,
information available for mapping disease risk typically includes point data (e.g. patients’ and controls’ residence) and
aggregated data (e.g. socio-demographic and economic attributes recorded at the census track level). Similarly, soil measurements
at discrete locations in the field are often supplemented with choropleth maps (e.g. soil or geological maps) that model the
spatial distribution of soil attributes as the juxtaposition of polygons (areas) with constant values. This paper presents
a general formulation of kriging that allows the combination of both point and areal data through the use of area-to-area,
area-to-point, and point-to-point covariances in the kriging system. The procedure is illustrated using two data sets: (1)
geological map and heavy metal concentrations recorded in the topsoil of the Swiss Jura, and (2) incidence rates of late-stage
breast cancer diagnosis per census tract and location of patient residences for three counties in Michigan. In the second
case, the kriging system includes an error variance term derived according to the binomial distribution to account for varying
degree of reliability of incidence rates depending on the total number of cases recorded in those tracts. Except under the
binomial kriging framework, area-and-point (AAP) kriging ensures the coherence of the prediction so that the average of interpolated
values within each mapping unit is equal to the original areal datum. The relationships between binomial kriging, Poisson
kriging, and indicator kriging are discussed under different scenarios for the population size and spatial support. Sensitivity
analysis demonstrates the smaller smoothing and greater prediction accuracy of the new procedure over ordinary and traditional
residual kriging based on the assumption that the local mean is constant within each mapping unit. 相似文献
10.
Evaluation of spatial interpolation methods for groundwater level in an arid inland oasis,northwest China 总被引：3，自引：1，他引：2
Liqiang Yao Zailin Huo Shaoyuan Feng Xiaomin Mao Shaozhong Kang Jin Chen Jijun Xu Tammo S. Steenhuis 《Environmental Earth Sciences》2014,71(4):1911-1924
The Wuwei oasis, situated in the upper reaches of the Shiyang River basin in the arid inland of northwest China, is intensively cultivated using both groundwater and irrigation water originating from the Qilian Mountains. Groundwater levels are declining due to overuse of irrigation water. To estimate the decline over the entire Wuwei oasis, eight different interpolation methods were used for interpolating groundwater levels over 3 years, i.e. starting in 1983, followed by 1988 and ending with 1992. Cross-validation and orthogonal-validation were applied to evaluate the accuracy of the different methods. Root mean squared error and the correlation coefficient (R 2) were calculated for each of the interpolation methods and years. Three kriging methods (simply, ordinary, and universal) gave the best fit. Modified ordinary kriging was found better than simple and universal kriging methods with a smaller number of points having large differences (>50 m) between estimated and predicted values. Based on the groundwater surfaces determined by the ordinary kriging as modified by Yamamoto, the groundwater decline was found from 1983 to 1992 to be a modest 2.1 m in average. 相似文献
11.
Spatial variability of soil organic matter and nutrients in paddy fields at various scales in southeast China 总被引：2，自引：0，他引：2
Xingmei Liu Keli Zhao Jianming Xu Minghua Zhang Bing Si Fan Wang 《Environmental Geology》2008,53(5):1139-1147
The present study examines the spatial dependency of soil organic matter and nutrients in paddy fields at three different
scales using geostatistics and geographic information system techniques (GIS). The spatial variability of soil organic matter
(SOM), total nitrogen (TN) and available phosphorus (AP) has been characterized using a total of 460, 131 and 64 samples that
were, respectively, collected from the Hangzhou–Jiaxing–Huzhou (HJH) Plain (10 km), Pinghu county (1,000 m) and a test plot
area (100 m) within the Pinghu county, Zhejiang province of the southeast China. Semivariograms showed that the SOM and TN
had moderate spatial dependency on the large scale of HJH plain and moderate scale of Pinghu county with long spatial correlation
distances. At the moderate scale of Pinghu county and the small scale of a test plot area, the AP data did not show any spatial
correlation, but had moderate spatial dependency in HJH plain. Spherical and exponential variogram models were best fitted
to all these soil properties. Maps of SOM and TN were generated through interpolation of measured values by ordinary kriging,
and AP by lognormal kriging. This study suggests that precision management of SOM and TN is feasible at all scales, and precision
management of AP is feasible at large scales. 相似文献
12.
合肥义城地区土壤重金属污染评价中典型插值方法的对比 总被引：5，自引：0，他引：5
空间插值对于土壤中重金属元素的空间分布及污染评价具有重要意义。对合肥义城地区土壤中的Cu、Pb、Zn、Cd、As、Hg等污染重金属元素,以常用且具有代表性的反距离加权法、径向基函数法、普通克里格法,进行了空间插值的对比验证分析和评价。通过对各种元素的空间插值各种误差进行综合比较的结果表明：Cu、Pb、As元素采用普通克里格法进行插值结果最优,而Zn元素采用反距离加权法最优,对于Cd、Hg元素则径向基函数插值法最优。 相似文献
13.
长期地下煤炭开采在地表产生了大面积的塌陷塘,并造成了不同程度的水域污染。为研究塌陷塘重金属的分布特征及成因,选择了8种对环境影响较大的重金属元素（Fe,Mn,Zn,Cu,Cr,Cd,Pb,Ni）为研究对象,以淮南潘集一矿塌陷塘为研究区域,利用ArcGIS地统计模块中的协同克里格算法,通过水体实测光谱反射率作为协变量来估算水体中的重金属含量空间分布特征。结果表明:水体实测光谱与重金属含量有较好的关系,以水体光谱为协变量的协同克里格插值与单变量的普通克里格插值相比,8种重金属元素的预测值与实际值之间的均方根误差明显减少,证明水体实测光谱适合作为协变量来估计水体重金属的空间分布情况。综合分析发现,水体中的Cd,Pb,Cu,Ni主要来自水域西北部的煤矸石堆山,且Cd,Cu,Pb含量均超过了当地的背景值,对环境影响较大;Cr主要来自农业肥料、成土母质和周边道路旁的煤泥灰厂及煤矸石堆;Zn的来源主要是煤矸石、上游生活污水、农业肥料、土壤母质,由于其含量较低,对水环境质量的影响不大。 相似文献
14.
Compositional data are very common in the earth sciences. Nevertheless, little attention has been paid to the spatial interpolation of these data sets. Most interpolators do not necessarily satisfy the constant sum and nonnegativity constraints of compositional data, nor take spatial structure into account. Therefore, compositional kriging is introduced as a straightforward extension of ordinary kriging that complies with these constraints. In two case studies, the performance of compositional kriging is compared with that of the additive logratio-transform. In the first case study, compositional kriging yielded significantly more accurate predictions than the additive logratio-transform, while in the second case study the performances were comparable. 相似文献
15.
16.
Spatial prediction is a problem common to many disciplines. A simple application is the mapping of an attribute recorded at a set of points. Frequently a nonlinear functional of the observed variable is of interest, and this calls for nonlinear approaches to prediction. Nonlinear kriging methods, developed in recent years, endeavour to do so and additionally provide estimates of the distribution of the target quantity conditional on the observations. There are few empirical studies that validate the various forms of nonlinear kriging. This study compares linear and nonlinear kriging methods with respect to precision and their success in modelling prediction uncertainty. The methods were applied to a data set giving measurements of the topsoil concentrations of cobalt and copper at more than 3000 locations in the Border Region of Scotland. The data stem from a survey undertaken to identify places where these trace elements are deficient for livestock. The comparison was carried out by dividing the data set into calibration and validation sets. No clear differences between the precision of ordinary, lognormal, disjunctive, indicator, and model-based kriging were found, neither for linear nor for nonlinear target quantities. Linear kriging, supplemented with the assumption of normally distributed prediction errors, failed to model the conditional distribution of the marginally skewed data, whereas the nonlinear methods modelled the conditional distributions almost equally well. In our study the plug-in methods did not fare any worse than model-based kriging, which takes parameter uncertainty into account. 相似文献
17.
Muhammad Imran Ather Ashraf Ateeq Ur Rehman 《Journal of the Geological Society of India》2016,88(6):711-717
Quality of soil data is vital to formulate agricultural policies at different scales. Current agricultural applications in Pakistan depend however, on average values of soil estimates over larger areas. In this work, model-based ordinary kriging (OK) and Bayesian kriging (BK) to interpolate soil data is used. The aim is to compare the two different methods for the accuracy of soil data prediction. For this soils were sampled for Electrical Conductivity (EC, dS m –1) at 759 different locations in the rural agricultural areas of Qasur Tehsil, Pakistan. Cross validation was used to compare the performance of OK and BK. Our results show strong skewness and spatial dependency of soil EC values in heterogeneous regions. Box-Cox transformation successfully reduced the level of skewness in the soil EC data (from 14.1 to 0.11). Contrary to OK, under-estimation of soil EC values was not evident in the BK interpolation. Mean square prediction error for BK (1.45) was significantly reduced as compared to that for OK (6.1). Considering these findings, BK is a better model to explain the sub-regional soil EC variability and estimating strategies for sustainable agricultural planning in Pakistan. 相似文献
18.
《Applied Geochemistry》1999,14(1):133-145
Three univariate geostatistical methods of estimation are applied to a geochemical data set. The studied methods are: ordinary kriging (cross-validation), factorial kriging, and indicator kriging. These techniques use the probabilistic and spatial behaviour of geochemical variables, giving a tool for identifying potential anomalous areas to locate mineralization. Ordinary kriging is easy to apply and to interpret the results. It has the advantage of using the same experimental grid points for its estimates, and no additional grid points are needed. Factorial kriging decomposes the raw variable into as many components as there are identified structures in the variogram. This, however, is a complex method and its application is more difficult than that of ordinary or indicator kriging. The main advantages of indicator kriging are that data are used by their rank order, being more robust about outlier values, and that the presentation of results is simple. Nevertheless, indicator kriging is incapable of separating anomalous values and the high values from the background, which have a behaviour different to the anomaly. In this work, the results of the application of these 3 kriging methods to a set of mineral exploration data obtained from a geochemical survey carried out in NW Spain are presented. This area is characterised by the presence of Au mineral occurrences. The kriging methods were applied to As, considered as a pathfinder of Au in this area. Numerical treatment of Au is not applicable, because it presents most values equal to the detection limit, and a series of extreme values. The results of the application of ordinary kriging, factorial kriging and indicator kriging to As make possible the location of a series of rich values, sited along a N–S shear zone, considered a structure related to the presence of Au. 相似文献
19.
When do we need a trend model in kriging? 总被引：1，自引：0，他引：1
Under usual estimation practice with local search windows for data and for interpolation situations, universal kriging and ordinary kriging yield the same estimates, using a data set with apparent trend, for both the unknown attribute and its trend component. Modeling the trend matters only in extrapolation situations. Because conditions of the case study presented arise most frequently in practice, the simpler ordinary kriging is the preferred option. 相似文献
20.
元素空间分布插值方法的对比研究:以铜陵地区土壤中的重金属元素为例 总被引：7，自引：0，他引：7
文中以铜陵地区As、Cd、Cu、Pb、Tl、Zn等6种土壤污染元素为例,选取常用且具有代表性的反距离加权法、径向基函数法、普通克里格法、多维分形法4种空间插值方法,进行土壤元素空间插值,并对其结果进行验证分析和评价。各方法均选取最优参数进行插值对比,土壤样本数共372个,其中337个用于插值计算,35个不参与插值计算而用于验证插值结果。对比研究显示,普通克里格法对刻画区域土壤元素的空间分布趋势效果最佳,但其半变异函数模型及参数的优选仍有待进一步研究;多维分形法对刻画土壤元素局部异常和污染效果最佳,但其对土壤元素分布普遍特征的反映仍需深入研究;反距离加权法和径向基函数法对土壤元素分布的空间插值精度一般,但其简单易用、插值最优参数易于选择。 相似文献