共查询到20条相似文献，搜索用时 31 毫秒
1.
This paper proposes an interpolation method based on a modified Kohonen artificial neural network, and is used to interpolate
marine gravity data on a regular grid. This method combines accuracy comparable to that of kriging with a much shorter computing
time than kriging. It is particularly efficient when both the size of the grid and the quantity of available data are large.
Under some hypotheses similar to those of kriging with a trend, the unbiasedness and optimality of the method can be demonstrated.
Comparison with kriging with a trend using marine gravity data shows similar results. Although neural interpolation is slightly
less efficient, it is more robust outside of the marine data area. 相似文献
2.
Interpolating geo-data with curvilinear structures using geostatistics is often disappointing. Channels, for example, become
disconnected sets of lakes when interpolated from point data. In order to improve the interpolation of geological structures
(e.g., curvilinear structures), we present a new form of kriging, local anisotropy kriging (LAK). Local anisotropy kriging
combines a gradient algorithm from image analysis with kriging in an iterative way. After an initial standard kriging interpolation,
the gradient algorithm determines the local anisotropy for each cell in the grid using a search area around the cell. Subsequently,
kriging is carried out with the spatially varying anisotropy. The anisotropy calculation and subsequent kriging steps will
then succeed until the result is satisfactory in the way of reproducing the curvilinear structures. Depending on the size
of the search area more or less detail in the geological structures can be reproduced with LAK. Using test examples we show
that LAK interpolates data with curvilinear structures more realistically than standard kriging. In a real world case, using
bathymetric data of the Oosterschelde estuary, LAK also proves to be quantitatively superior to standard kriging. Absolute
interpolation errors are decreased by 23%. Local anisotropy kriging only uses information from point data, which makes the
method very objective, it only presents “what the data can tell.” 相似文献
3.
A Stanley Raj Y SRINIVAS D Hudson Oliver D Muthuraj 《Journal of Earth System Science》2014,123(2):395-411
The non-linear apparent resistivity problem in the subsurface study of the earth takes into account the model parameters in terms of resistivity and thickness of individual subsurface layers using the trained synthetic data by means of Artificial Neural Networks (ANN). Here we used a single layer feed-forward neural network with fast back propagation learning algorithm. So on proper training of back propagation networks it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data with reference to the synthetic data trained in the appropriate network. During training, the weights and biases of the network are iteratively adjusted to make network performance function level more efficient. On adequate training, errors are minimized and the best result is obtained using the artificial neural networks. The network is trained with more number of VES data and this trained network is demonstrated by the field data. The accuracy of inversion depends upon the number of data trained. In this novel and specially designed algorithm, the interpretation of the vertical electrical sounding has been done successfully with the more accurate layer model. 相似文献
4.
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
5.
An artificial neural network (ANN) toolbox is created within GIS software for spatial interpolation, which will help GIS users to train and test ANNs, perform spatial analysis, and display results as a single process. The performance is compared to that of the open source Fast Artificial Neural Network library and conventional interpolation methods by creating digital elevation models (DEMs) given that nearly exact solutions exist. Simulation results show that the advanced backpropagations such as iRprop speed up the learning, while they can get stuck in a local minimum depending on initial weight sets. Besides, the division of input–output examples into training and test data affects the accuracy, particularly when the distribution of the examples is skewed and peaked, and the number of data is small. ANNs, however, show the similar performance to inversed distance weighted or kriging and outperform polynomial interpolations as a global interpolation method in high-dimensional data. In addition, the neural network residual kriging (NNRK) model, which combines the ANN toolbox and kriging within GIS software, is performed. The NNRK outperforms conventional methods and well captures global trends and local variations. A key outcome of this work is that the ANN toolbox created within the de facto standard GIS software is applicable to various spatial analysis including hazard risk assessment over a large area, in particular when there are multiple potential causes, the relationship between risk factors and hazard events is not clear, and the number of available data is small given its performance for DEM generation. 相似文献
6.
以浅剖数据为源数据,钻孔实测数据为验证数据,利用普通克里金法对海底地层厚度进行空间插值得到地层分布特征,采用3种半变异函数模型和不同取样间距对某井场3组地层厚度进行普通克里金插值并验证其插值效果。结果表明:普通克里金是一种有效的海底地层厚度预测方法;结构分析最佳的模型不一定是误差最小的模型,应对不同模型下的插值结果进行综合分析来选择最合适的模型,并提出球状模型在该井场厚度估计中最优,高斯模型次之;对于球状模型,增大取样间距对地层厚度变化剧烈的地层回归效果影响较小,对地层厚度变化不大的地层回归效果影响较大;同时,SE预测值变化率分析表明对于地层厚度变化剧烈的地层,减小取样间距可以大幅度地减少插值误差,而对于地层厚度变化不大的地层,减小取样间距对插值精度提高的意义不大。 相似文献
7.
矿物识别在许多研究领域都有着重要作用,基于深度学习技术的智能矿物识别为这些领域带来了新的发展方向,不仅能有效节省人工成本,还能减小识别错误。针对石英、角闪石、黑云母、石榴石和橄榄石共5种矿物进行实验,提出了一种准确高效的智能矿物识别方法。实验采用图像分析常用的卷积神经网络建立模型,设计出一套基于残差神经网络的矿物识别方法。本实验独立采集了5种矿物的偏光显微图像数据集,用于模型的训练、验证和测试,并通过合理的数据增强策略来扩充训练数据集。在卷积神经网络的结构设计上,选取了ResNet-18作为框架,最终于模型测试中取得89%的准确率,成功训练出一个较为精准的矿物识别模型,实现了基于深度学习的智能矿物识别方法。 相似文献
8.
Chunfa Wu Jiaping Wu Yongming Luo Haibo Zhang Ying Teng Stephen D. DeGloria 《Environmental Earth Sciences》2011,63(5):1093-1103
It was not unusual in soil and environmental studies that the distribution of data is severely skewed with several high peak
values, which causes the difficulty for Kriging with data transformation to make a satisfied prediction. This paper tested
an approach that integrates kriging and triangular irregular network interpolation to make predictions. A data set consisting
of total Copper (Cu) concentrations of 147 soil samples, with a skewness of 4.64 and several high peak values, from a copper
smelting contaminated site in Zhejiang Province, China. The original data were partitioned into two parts. One represented
the holistic spatial variability, followed by lognormal distribution, and then was interpolated by lognormal ordinary kriging.
The other assumed to show the local variability of the area that near to high peak values, and triangular irregular network
interpolation was applied. These two predictions were integrated into one map. This map was assessed by comparing with rank-order
ordinary kriging and normal score ordinary kriging using another data set consisting of 54 soil samples of Cu in the same
region. According to the mean error and root mean square error, the approach integrating lognormal ordinary kriging and triangular
irregular network interpolation could make improved predictions over rank-order ordinary kriging and normal score ordinary
kriging for the severely skewed data with several high peak values. 相似文献
9.
Correcting the Smoothing Effect of Ordinary Kriging Estimates 总被引：2，自引：0，他引：2
Jorge Kazuo Yamamoto 《Mathematical Geology》2005,37(1):69-94
The smoothing effect of ordinary kriging is a well-known dangerous effect associated with this estimation technique. Consequently kriging estimates do not reproduce both histogram and semivariogram model of sample data. A four-step procedure for correcting the smoothing effect of ordinary kriging estimates is shown to be efficient for the reproduction of histogram and semivariogram without loss of local accuracy. Furthermore, this procedure provides a unique map sharing both local and global accuracies. Ordinary kriging with a proper correction for smoothing effect can be revitalized as a reliable estimation method that allows a better use of the available information. 相似文献
10.
Inversion of residual gravity anomalies using neural network 总被引：1，自引：1，他引：0
Mansour A. Al-Garni 《Arabian Journal of Geosciences》2013,6(5):1509-1516
A new approach is presented in order to interpret residual gravity anomalies from simple geometrically shaped bodies such as horizontal cylinder, vertical cylinder, and sphere. This approach is mainly based on using modular neural network (MNN) inversion for estimating the shape factor, the depth, and the amplitude coefficient. The sigmoid function has been used as an activation function in the MNN inversion. The new approach has been tested first on synthetic data from different models using only one well-trained network. The results of this approach show that the parameter values estimated by the modular inversion are almost identical to the true parameters. Furthermore, the noise analysis has been examined where the results of the inversion produce satisfactory results up to 10% of white Gaussian noise. The reliability of this approach is demonstrated through two published real gravity field anomalies taken over a chromite deposit in Camaguey province, Cuba and over sulfide ore body, Nornada, Quebec, Canada. A comparable and acceptable agreement is obtained between the results derived by the MNN inversion method and those deduced by other interpretation methods. Furthermore, the depth obtained by the proposed technique is found to be very close to that obtained by drilling information. 相似文献
11.
以多重分形理论为基础,对中天山乌拉斯台地区铜多金属元素的岩屑测量数据,采用C-A法获得铜多金属的异常下限值,将其作为阈值进行指示克里格插值,绘制研究区的铜多金属地球化学异常图。研究显示,基于该方法获得的Cu矿化异常高值区主要集中在华力西早期第三侵入次的石英闪长岩和花岗闪长岩岩体中,受北西向和次级北东向断裂构造控制明显,该异常区可以作为寻找热液型铜多金属矿产的重要远景区。该方法对于地球化学数据空间变异性强烈的地区,较之普通克里格插值法具有更好的地球化学异常识别能力和高值信息重建能力,所得结果的最高累计频率值范围与已知矿化点的空间位置吻合度更高,在地球化学异常信息提取工作中具有推广意义。 相似文献
12.
Application of universal kriging for estimation of earthquake ground motion: Statistical significance of results 总被引：1，自引：0，他引：1
Universal kriging is compared with ordinary kriging for estimation of earthquake ground motion. Ordinary kriging is based on a stationary random function model; universal kriging is based on a nonstationary random function model representing first-order drift. Accuracy of universal kriging is compared with that for ordinary kriging; cross-validation is used as the basis for comparison. Hypothesis testing on these results shows that accuracy obtained using universal kriging is not significantly different from accuracy obtained using ordinary kriging. Tests based on normal distribution assumptions are applied to errors measured in the cross-validation procedure;t andF tests reveal no evidence to suggest universal and ordinary kriging are different for estimation of earthquake ground motion. Nonparametric hypothesis tests applied to these errors and jackknife statistics yield the same conclusion: universal and ordinary kriging are not significantly different for this application as determined by a cross-validation procedure. These results are based on application to four independent data sets (four different seismic events). 相似文献
13.
A number of criteria based on kriging variance calculations may be used for infill sampling design in geologic site characterization. Searching for the best new sample locations from a set of candidate locations can result in excessive computation time if these criteria and the naive rekriging are used. The relative updated kriging estimate and variance for universal kriging estimation are demonstrated as a simple kriging estimate and variance, respectively. The updated kriging variance is demonstrated as the multiplication of two kriging variances. Using these updated kriging variance equations can increase the computational speed for selecting the best new sample locations. The application results for oil rock thickness in an oilfield indicate that minimizing the average relative updated kriging variance is a useful alternative to the other criteria based on kriging variance in optimal infill sampling design for geologic site characterization. 相似文献
14.
Acoustic and light detection and ranging are the recent methods used in hydrographical surveying. Depth values depending on X and Y horizontal coordinates are measured in both methods. While processing the hydrographical data, all data with different density are interpolated and modeled for determining the seafloor model using different interpolation techniques. In this study, effects of different surface modeling methods are investigated. Data obtained from single-beam echo sounder (SBES) are modeled using inverse distance, kriging, local polynomial, minimum curvature, moving average, nearest-neighbor, and Delaunay interpolation methods. Interpolation results are compared with the multibeam echo sounder data which were collected on the same area for determining the accuracy of modeling methods. Depending on the maximum total vertical uncertainty values in hydrographic survey standards, the best results were determined by using the kriging method. The Delaunay, minimum curvature, and inverse distance methods can be used for modeling the SBES data in shallow waters. 相似文献
15.
We evaluate the performance and statistical accuracy of the fast Fourier transform method for unconditional and conditional simulation. The method is applied under difficult but realistic circumstances of a large field (1001 by 1001 points) with abundant conditioning criteria and a band limited, anisotropic, fractal-based statistical characterization (the von Kármán model). The simple Fourier unconditional simulation is conducted by Fourier transform of the amplitude spectrum model, sampled on a discrete grid, multiplied by a random phase spectrum. Although computationally efficient, this method failed to adequately match the intended statistical model at small scales because of sinc-function convolution. Attempts to alleviate this problem through the covariance method (computing the amplitude spectrum by taking the square root of the discrete Fourier transform of the covariance function) created artifacts and spurious high wavenumber content. A modified Fourier method, consisting of pre-aliasing the wavenumber spectrum, satisfactorily remedies sinc smoothing. Conditional simulations using Fourier-based methods require several processing stages, including a smooth interpolation of the differential between conditioning data and an unconditional simulation. Although kriging is the ideal method for this step, it can take prohibitively long where the number of conditions is large. Here we develop a fast, approximate kriging methodology, consisting of coarse kriging followed by faster methods of interpolation. Though less accurate than full kriging, this fast kriging does not produce visually evident artifacts or adversely affect the a posteriori statistics of the Fourier conditional simulation. 相似文献
16.
A parametric study of robustness of kriging variance as a function of range and relative nugget effect for a spherical semivariogram 总被引：1，自引：0，他引：1
P. I. Brooker 《Mathematical Geology》1986,18(5):477-488
In geostatistics, an estimation of blocks of a deposit is reported along with the variance of error made in their estimation. This calculation is based on the model chosen for the semivariogram of the deposit so that mistakes in its estimation can manifest themselves in the perception of accuracy with which blocks are known. Changes in kriging variance resulting from various amounts of error in modeling the relative nugget effect and range of the semivariogram are investigated for an extensive set of spherical semivariograms. 相似文献
17.
Arnon Karnieli Dr. 《GeoJournal》1990,22(4):391-398
The classical methods for interpolating and spatial averaging of precipitation fields fail to quantify the accuracy of the estimate. On the other hand, kriging is an interpolation method for predicting values of regionalized variables at points (punctual kriging) or average values over an area (block kriging).This paper demonstrates the use of the kriging method for mapping and evaluating precipitation data for the State of Arizona. Using 158 rain gauge stations with 30 years or more of record, the precipitation over the state has been modeled as a realization of a two dimensional random field taking into consideration the spatial variability conditions.Three data sets have been used: (1) the mean annual precipitation over the state; (2) the mean summer rainy season; and (3) the mean winter rainy season. Validation of the empirical semi-variogram for a constant drift case indicated that the exponential model was appropriate for all the data sets. In addition to a global kriging analysis, the data have been examined under an anisotropic assumption which reflects the topographic structure of the state. 相似文献
18.
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
19.
Sample schemes used in geostatistical surveys must be suitable for both variogram estimation and kriging. Previously schemes
have been optimized for one of these steps in isolation. Ordinary kriging generally requires the sampling locations to be
evenly dispersed over the region. Variogram estimation requires a more irregular pattern of sampling locations since comparisons
must be made between measurements separated by all lags up to and beyond the range of spatial correlation. Previous studies
have not considered how to combine these optimized schemes into a single survey and how to decide what proportion of sampling
effort should be devoted to variogram estimation and what proportion devoted to kriging
An expression for the total error in a geostatistical survey accounting for uncertainty due to both ordinary kriging and variogram
uncertainty is derived. In the same manner as the kriging variance, this expression is a function of the variogram but not
of the sampled response data. If a particular variogram is assumed the total error in a geostatistical survey may be estimated
prior to sampling. We can therefore design an optimal sample scheme for the combined processes of variogram estimation and
ordinary kriging by minimizing this expression. The minimization is achieved by spatial simulated annealing. The resulting
sample schemes ensure that the region is fairly evenly covered but include some close pairs to analyse the spatial correlation
over short distances. The form of these optimal sample schemes is sensitive to the assumed variogram. Therefore a Bayesian
approach is adopted where, rather than assuming a single variogram, we minimize the expected total error over a distribution
of plausible variograms. This is computationally expensive so a strategy is suggested to reduce the number of computations
required 相似文献
20.
An Experimental Comparison of Ordinary and Universal Kriging and Inverse Distance Weighting 总被引：17，自引：0，他引：17
A factorial, computational experiment was conducted to compare the spatial interpolation accuracy of ordinary and universal kriging and two types of inverse squared-distance weighting. The experiment considered, in addition to these four interpolation methods, the effects of four data and sampling characteristics: surface type, sampling pattern, noise level, and strength of small-scale spatial correlation. Interpolation accuracy was measured by the natural logarithm of the mean squared interpolation error. Main effects of all five factors, all two-factor interactions, and several three-factor interactions were highly statistically significant. Among numerous findings, the most striking was that the two kriging methods were substantially superior to the inverse distance weighting methods over all levels of surface type, sampling pattern, noise, and correlation. 相似文献