共查询到20条相似文献，搜索用时 31 毫秒
1.
This paper addresses the problem of quantifying the joint uncertainty in the grades of elements of interest (iron, silica, manganese, phosphorus and alumina), loss on ignition, granulometry and rock types in an iron ore deposit. Sampling information is available from a set of exploration drill holes. The methodology considers the construction of multiple rock type outcomes by plurigaussian simulation, then outcomes of the quantitative variables (grades, loss on ignition and granulometry) are constructed by multigaussian joint simulation, accounting for geological domains specific to each quantitative variable as well as for a stoichiometric closure formula linking these variables. The outcomes are validated by checking the reproduction of the data distributions and of the data values at the drill hole locations, and their ability to measure the uncertainty at unsampled locations is assessed by leave-one-out cross validation.Both the plurigaussian and multigaussian models offer much flexibility to the practitioner to face up to the complexity of the variables being modeled, in particular: (1) the contact relationships between rock types, (2) the geological controls exerted by the rock types over the quantitative variables, and (3) the cross-correlations and stoichiometric closure linking the quantitative variables. In addition to this flexibility, the use of efficient simulation algorithms turns out to be essential for a successful application, due to the high number of variables, data and locations targeted for simulation. 相似文献
2.
Ali Akbar Daya 《Journal of the Geological Society of India》2014,83(5):567-576
Most significant iron ore deposits in Iran are located in Central Iran Zone. These deposits belong to the Bafq mining district. The Bafq mining district is located in the Early Cambrian Kashmar-Kerman volcanic arc of Central Iran. Linear estimation of regionalized variables (for example by inverse distance weighting or ordinary Kriging) results in relatively high estimation variances, i.e. the estimates have very low precision. Assessment of project economics (or other critical decision making) based on linear estimation is therefore risky. Non-linear estimation methods like disjunctive kriging perform better and the lower estimation variance allows less risky economic decision-making. Another advantage of disjunctive kriging is that it allows estimation of functions of the primary variable, which here is the grade (Fe %) of the ore. In particular it permits estimation of indicator functions defined using thresholds on the primary variable. This paper is devoted to application of disjunctive kriging method in Choghart North Anomaly iron ore deposit in Central Iran, Yazd province, Iran. In this study, the Fe concentration of Choghart North Anomaly iron ore deposit was modelled and estimated. The exploration data consists of borehole samples measuring the Fe concentration. A Gaussian isofactorial model is fitted to these data and disjunctive kriging was used to estimate the regionalized variable (Fe %) at unsampled locations and to assess the probabilities that the actual concentrations exceed a threshold value at a given location. Consequently a three dimensional model of probability of exceeding a threshold value and the estimated value are provided by disjunctive kriging to divide the ore into an economic and uneconomic part on the basis of estimation of indicator functions using thresholds grades defined on point support. The tools and concepts are complemented by a set of computer programs that are applied to the case study. The study showed that disjunctive kriging can be applied successfully for modeling the grade of an ore deposit. Results showed that the correlation between the estimated value and real value at locations close to each other is 81.9%. 相似文献
3.
Xavier Emery 《Mathematical Geology》2006,38(7):801-819
Multigaussian kriging aims at estimating the local distributions of regionalized variables and functions of these variables
(transfer or recovery functions) at unsampled locations. In this paper, we focus on the evaluation of the recoverable reserves
in an ore deposit accounting for a change of support and information effect caused by ore/waste misclassifications. Two approaches
are proposed: the multigaussian model with Monte Carlo integration and the discrete Gaussian model. The latter is simpler
to use but requires stronger hypotheses than the former. In each model, ordinary multigaussian kriging gives unbiased estimates
of the recoverable reserves that do not utilize the mean value of the normal score data.
The concepts are illustrated through a case study on a copper deposit which shows that local estimates of the metal content
based on ordinary multigaussian kriging are close to the optimal conditional expectation when the data are abundant and are
not dominated by the global mean when the data are scarce. The two proposed approaches (Monte Carlo integration and discrete
Gaussian model) lead to similar results when compared to two other geostatistical methods: service variables and ordinary
indicator kriging, which show strong deviations from conditional expectation. 相似文献
4.
Assessing spatial uncertainty in mapping soil erodibility factor using geostatistical stochastic simulation 总被引：1，自引：0，他引：1
Soil erosion is one of most widespread process of degradation. The erodibility of a soil is a measure of its susceptibility
to erosion and depends on many soil properties. Soil erodibility factor varies greatly over space and is commonly estimated
using the revised universal soil loss equation. Neglecting information about estimation uncertainty may lead to improper decision-making.
One geostatistical approach to spatial analysis is sequential Gaussian simulation, which draws alternative, equally probable,
joint realizations of a regionalised variable. Differences between the realizations provide a measure of spatial uncertainty
and allow us to carry out an error analysis. The objective of this paper was to assess the model output error of soil erodibility
resulting from the uncertainties in the input attributes (texture and organic matter). The study area covers about 30 km2 (Calabria, southern Italy). Topsoil samples were collected at 175 locations within the study area in 2006 and the main chemical
and physical soil properties were determined. As soil textural size fractions are compositional data, the additive-logratio
(alr) transformation was used to remove the non-negativity and constant-sum constraints on compositional variables. A Monte
Carlo analysis was performed, which consisted of drawing a large number (500) of identically distributed input attributes
from the multivariable joint probability distribution function. We incorporated spatial cross-correlation information through
joint sequential Gaussian simulation, because model inputs were spatially correlated. The erodibility model was then estimated
for each set of the 500 joint realisations of the input variables and the ensemble of the model outputs was used to infer
the erodibility probability distribution function. This approach has also allowed for delineating the areas characterised
by greater uncertainty and then to suggest efficient supplementary sampling strategies for further improving the precision
of K value predictions. 相似文献
5.
采用频率分析法计算入库设计洪水时,需要通过相关分析将坝址洪水系列插补得到对应的入库洪水系列。常用的线性回归法假设两者满足线性关系且入库洪水系列服从正态分布,可能与实际情况并不相符。引入Copula函数构建坝址洪水与入库洪水的联合概率分布和条件概率分布,计算给定坝址洪水时入库洪水的条件最可能值和置信区间,提出了一种基于Copula函数的入库洪水插补新方法。三峡水库的应用实例表明:线性回归法得到的入库洪水值在坝址洪水量级较大时明显偏小,甚至稀遇洪水时不在90%置信区间内。所提方法能较好地反映坝址洪水与入库洪水的内在关系,不仅可以计算入库洪水的各种点估计值,而且能够定量评价估计的不确定性。 相似文献
6.
Pinnaduwa H. S. W. Kulatilake Jinyong Park Pirahas Balasingam Sean A. Mckenna 《Geotechnical and Geological Engineering》2007,25(1):79-102
A 1 km square regular grid system created on the Universal Transverse Mercator zone 54 projected coordinate system is used
to work with volcanism related data for Sengan region. The following geologic variables were determined as the most important
for identifying volcanism: geothermal gradient, groundwater temperature, heat discharge, groundwater pH value, presence of
volcanic rocks and presence of hydrothermal alteration. Data available for each of these important geologic variables were
used to perform directional variogram modeling and kriging to estimate geologic variable vectors at each of the 23949 centers
of the chosen 1 km cell grid system. Cluster analysis was performed on the 23949 complete variable vectors to classify each
center of 1 km cell into one of five different statistically homogeneous groups with respect to potential volcanism spanning
from lowest possible volcanism to highest possible volcanism with increasing group number. A discriminant analysis incorporating
Bayes’ theorem was performed to construct maps showing the probability of group membership for each of the volcanism groups.
The said maps showed good comparisons with the recorded locations of volcanism within the Sengan region. No volcanic data
were found to exist in the group 1 region. The high probability areas within group 1 have the chance of being the no volcanism
region. Entropy of classification is calculated to assess the uncertainty of the allocation process of each 1 km cell center
location based on the calculated probabilities. The recorded volcanism data are also plotted on the entropy map to examine
the uncertainty level of the estimations at the locations where volcanism exists. The volcanic data cell locations that are
in the high volcanism regions (groups 4 and 5) showed relatively low mapping estimation uncertainty. On the other hand, the
volcanic data cell locations that are in the low volcanism region (group 2) showed relatively high mapping estimation uncertainty.
The volcanic data cell locations that are in the medium volcanism region (group 3) showed relatively moderate mapping estimation
uncertainty. Areas of high uncertainty provide locations where additional site characterization resources can be spent most
effectively. The new data collected can be added to the existing database to perform future regionalized mapping and reduce
the uncertainty level of the existing estimations. 相似文献
7.
Spatial data analytics provides new opportunities for automated detection of anomalous data for data quality control and subsurface segmentation to reduce uncertainty in spatial models. Solely data-driven anomaly detection methods do not fully integrate spatial concepts such as spatial continuity and data sparsity. Also, data-driven anomaly detection methods are challenged in integrating critical geoscience and engineering expertise knowledge. The proposed spatial anomaly detection method is based on the semivariogram spatial continuity model derived from sparsely sampled well data and geological interpretations. The method calculates the lag joint cumulative probability for each matched pair of spatial data, given their lag vector and the semivariogram under the assumption of bivariate Gaussian distribution. For each combination of paired spatial data, the associated head and tail Gaussian standardized values of a pair of spatial data are mapped to the joint probability density function informed from the lag vector and semivariogram. The paired data are classified as anomalous if the associated head and tail Gaussian standardized values fall within a low probability zone. The anomaly decision threshold can be decided based on a loss function quantifying the cost of overestimation or underestimation. The proposed spatial correlation anomaly detection method is able to integrate domain expertise knowledge through trend and correlogram models with sparse spatial data to identify anomalous samples, region, segmentation boundaries, or facies transition zones. This is a useful automation tool for identifying samples in big spatial data on which to focus professional attention.
相似文献8.
Ore grade is the most important source of uncertainty in a mining operation which plays an important role to classify run-of-mine (ROM) material into ore and waste parcels. As a widely used method, kriging estimator is used to estimate the grade of ore blocks. In conventional mining practices, if the estimated grade of a parcel is above the cut-off grade, this parcel is classified as ore, otherwise, is labelled as a waste parcel. An alternative approach is to simultaneously consider the grade of parcels and the economic consequences of sending parcels to destinations by applying simulation-based methods. In this study, kriging and simulation-based methods including loss and profit functions are applied on a real-world case study to classify ore/waste material based on the initial exploration data. Then, the actual known data, collected from blast holes samples, are compared with the estimated results in order to validate the performance of the presented methods. Outcomes show that simulation-based methods can perform better and show more adjustability with real data. 相似文献
9.
考虑台风导致的高水位和海浪波高对风暴潮灾害的贡献,对1949年以来影响青岛地区的台风暴潮进行了抽样统计.基于二维的泊松冈贝尔逻辑分布模式,对海岸地区的致灾风暴潮进行了长期的随机分析.与传统的警戒水位法不同,新模式能够反映多种环境荷载的综合作用,推算了青岛地区的特大台风暴潮灾害的重现期.计算结果显示,二维复合分布模式适合于描述台风暴潮过程中极值水位与相应波高的联合概率,所得结论对青岛地区的防潮减灾规划和工程建设具有指导意义. 相似文献
10.
Renato Macciotta C. Derek Martin Norbert R. Morgenstern David M. Cruden 《Landslides》2016,13(1):115-127
Railway alignments through the Canadian Cordillera are constantly exposed to slope instabilities. Proactive mitigation strategies have been in place for a few decades now, and instability record keeping has been recognized as an important aspect of them. Such a proactive strategy has enhanced the industry’s capacity to manage slope risks, and some sections have been recognized as critical due to the frequency of instabilities. At these locations, quantification of the risks becomes necessary. Risk analysis requires knowledge of some variables for which statistical data are scarce or not available, and elicitation of subjective probabilities is needed. A limitation of such approaches lies in the uncertainty associated to those elicited probabilities. In this paper, a quantitative risk analysis is presented for a section of railway across the Canadian Cordillera. The analysis focused on the risk to life of the freight train crews working along this section. Upper and lower bounds were elicited to cope with the uncertainties associated with this approach. A Monte Carlo simulation technique was then applied to obtain the probability distribution of the estimated risks. The risk probability distribution suggests that the risk to life of the crews is below previously published evaluation criteria and within acceptable levels. The risk assessment approach proposed focuses on providing a measure of the uncertainty associated with the estimated risk and is capable of handling distributions that cover more than two orders of magnitude. 相似文献
11.
The assessment of the risks associated with contamination by elevated levels of pollutants is a major issue in most parts
of the world. The risk arises from the presence of a pollutant and from the uncertainty associated with estimating its concentration,
extent and trajectory. The uncertainty in the assessment comes from the difficulty of measuring the pollutant concentration
values accurately at any given location and the impossibility of measuring it at all locations within a study zone. Estimations
tend to give smoothed versions of reality, with the smoothing effect being inversely proportional to the amount of data. If
risk is a measure of the probability of pollutant concentrations exceeding specified thresholds, then the variability is the
key feature in risk assessment and risk analysis. For this reason, geostatistical simulations provide an appropriate way of
quantifying risk by simulating possible “realities” and determining how many of these realities exceed the contamination thresholds,
and, finally, provides a means of visualizing risk and the geological causes of risk. This study concerns multivariate simulations
of organic and inorganic pollutants measured in terrain samples to assess the uncertainty for the risk analysis of a contaminated
site, an industrial site in northern Italy that has to be remediated. The main geostatistical tools are used to model the
local uncertainty of pollutant concentrations, which prevail at any unsampled site, in particular by means of stochastic simulation.
These models of uncertainty have been used in the decision-making processes to identify the areas targeted for remediation. 相似文献
12.
边坡稳定性一直是边坡安全的重点研究对象,针对边坡评价中常见的不确定性因素,可靠度分析是值得利用的方法。为评价某节理发育的岩质岸坡稳定性,通过有限元计算软件,结合现场勘探测绘数据,建立以边坡节理强度参数c、φ为输入变量,安全系数为输出变量的点估计（PEM）计算概率模型,计算结果表明:节理发育对该边坡变形具有明显控制作用;边坡整体可靠性较好,破坏概率极低。最后,通过蒙托卡罗法对可靠度结果进行验证,结果表明两种方法的计算结果不存在显著性差异。研究结果表明节理对岩质边坡稳定具有良好的敏感性,基于节理不确定性的点估计法分析边坡可靠度是一种有效的方法。 相似文献
13.
水文变量多特征属性的频率分析,以及各种水文事件的遭遇及联合概率分布问题需要采用多变量概率分布模型解决。总结了当前应用最广泛的几种两变量概率分布模型,对各种模型的适用性和局限性做了详细分析,并介绍了一种新的两变量概率模型——Copula函数。现有模型大都基于变量之间的线性相关关系而建立,对于非线性、非对称的随机变量难以很好地描述;大部分模型假定各变量服从相同的边际分布或对变量间的相关性有严格的限定,从而限制了其应用。Copula函数所构造的两变量概率分布模型克服了现有模型的不足,它具有任意的边际分布,可以描述变量间非线性、非对称的相关关系。作为一种用于构造灵活的多变量联合分布的工具,Copula函数在水科学领域具有广阔的应用前景。 相似文献
14.
In consideration of large uncertainties in severe convective weather forecast, ensemble forecasting is a dynamic method developed to quantitatively estimate forecast uncertainty. Based on ensemble output, joint probability is a post-processing method to delineate key areas where weather event may actually occur by taking account of the uncertainty of several important physical parameters. An investigation of the environments of little rainfall convection and strong rainfall convection from April to September (warm season) during 2009-2015 was presented using daily disastrous weather data, precipitation data of 80 stations in Anhui province and NCEP Final Analysis (FNL) data. Through ingredients-based forecasting methodology and statistical analysis,four convective parameters characterizing two types of convection were obtained, respectively, which were used to establish joint probability forecasting together with their corresponding thresholds. Using the ECMWF ensemble forecast and observations from April to September during 2016-2017, systematic verification mainly based on ROC and case study of different weather processes were conducted. The results demonstrate that joint probability method is capable of discriminating little rainfall convection and non-convection with comparable performance for different lead times, which is more favorable to identifying the occurrence of strong rainfall convection. The joint probability of little rainfall convection is a good indication for the occurrence of regional or local convection, but may produce some false alarms. The joint probability of strong rainfall convection is good at indicating regional concentrated short-term heavy precipitation as well as local heavy rainfall. There are also individual missing reports in this method, and in practice, 10% can be roughly used as joint probability threshold to achieve relative high TS score. Overall, ensemble-based joint probability method can provide practical short-term probabilistic guidance for severe convective weather. 相似文献
15.
Sequential Gaussian Simulation(SGSIM)as a stochastic method has been developed to avoid the smoothing effect produced in deterministic methods by generating various stochastic realizations.One of the main issues of this technique is,however,an intensive computation related to the inverse operation in solving the Kriging system,which significantly limits its application when several realizations need to be produced for uncertainty quantification.In this paper,a physics-informed machine learning(PIML)model is proposed to improve the computational efficiency of the SGSIM.To this end,only a small amount of data produced by SGSIM are used as the training dataset based on which the model can discover the spatial correlations between available data and unsampled points.To achieve this,the governing equations of the SGSIM algorithm are incorporated into our proposed network.The quality of realizations produced by the PIML model is compared for both 2D and 3D cases,visually and quantitatively.Furthermore,computational performance is evaluated on different grid sizes.Our results demonstrate that the proposed PIML model can reduce the computational time of SGSIM by several orders of magnitude while similar results can be produced in a matter of seconds. 相似文献
16.
Rock Mass Characterization and Rock Property Variability Considerations for Tunnel and Cavern Design 总被引：5，自引：3，他引：2
M. Cai 《Rock Mechanics and Rock Engineering》2011,44(4):379-399
17.
Joint geostatistical simulation techniques are used to quantify uncertainty for spatially correlated attributes, including mineral deposits, petroleum reservoirs, hydrogeological horizons, environmental contaminants. Existing joint simulation methods consider only second-order spatial statistics and Gaussian processes. Motivated by the presence of relatively large datasets for multiple correlated variables that typically are available from mineral deposits and the effects of complex spatial connectivity between grades on the subsequent use of simulated realizations, this paper presents a new approach for the joint high-order simulation of spatially correlated random fields. First, a vector random function is orthogonalized with a new decorrelation algorithm into independent factors using the so-termed diagonal domination condition of high-order cumulants. Each of the factors is then simulated independently using a high-order univariate simulation method on the basis of high-order spatial cumulants and Legendre polynomials. Finally, attributes of interest are reconstructed through the back-transformation of the simulated factors. In contrast to state-of-the-art methods, the decorrelation step of the proposed approach not only considers the covariance matrix, but also high-order statistics to obtain independent non-Gaussian factors. The intricacies of the application of the proposed method are shown with a dataset from a multi-element iron ore deposit. The application shows the reproduction of high-order spatial statistics of available data by the jointly simulated attributes. 相似文献
18.
分析洪峰、洪量和历时三变量联合分布与风险概率及其设计分位数,为水利工程规划设计和风险评估提供参考依据。以珠江流域西江高要站52年洪水数据为例,采用非对称阿基米德M6 Copula函数与Kendall分布函数计算三变量洪水联合分布的“或”重现期、“且”重现期和二次重现期及其最可能的设计分位数。结果表明:“或”重现期的风险率偏高,“且”重现期的风险率偏低,二次重现期更准确地反映了特定设计频率情况下三变量洪水要素遭遇的风险率;按三变量“或”重现期或三变量同频率设计值推算的洪水设计值偏高,以最大可能概率推算的三变量洪水要素的二次重现期设计值可为防洪工程安全与风险管理提供新的选择。 相似文献
19.
地球化学异常通常是直接按某个阈值将整个研究区划分为高异常区或低异常区,这可能导致一些重要异常信息的丢失或决策判断失误。从统计学角度分析,推断未采样点处可能取得结果的概率,或者刻画估计值大于或小于某一地球化学异常阈值的概率分布,更符合勘查地球化学找矿活动的实际需要。针对确定性地球化学场建模方法的局限性,本研究通过集成地统计随机模拟和局部奇异性理论实现地球化学异常识别及其不确定性度量。通过奇异性指数-分位数分析,刻画奇异性指数在频率域中的分布模式,实现地球化学异常阈值分割。采用局部不确定性和空间不确定性算法模拟地球化学异常不确定性传播过程,并以新疆西天山地区为研究区,开展铜异常识别及其不确定性评价应用研究。 相似文献
20.
There has recently been a rapid growth in the amount and quality of digital geological and geophysical data for the majority of the Australian continent. Coupled with an increase in computational power and the rising importance of computational methods, there are new possibilities for a large scale, low expenditure digital exploration of mineral deposits. Here we use a multivariate analysis of geophysical datasets to develop a methodology that utilises machine learning algorithms to build and train two-class classifiers for provincial-scale, greenfield mineral exploration. We use iron ore in Western Australia as a case study, and our selected classifier, a mixture of a Gaussian classifier with reject option, successfully identifies 88% of iron ore locations, and 92% of non-iron ore locations. Parameter optimisation allows the user to choose the suite of variables or parameters, such as classifier and degree of dimensionality reduction, that provide the best classification result. We use randomised hold-out to ensure the generalisation of our classifier, and test it against known ground-truth information to demonstrate its ability to detect iron ore and non-iron ore locations. Our classification strategy is based on the heterogeneous nature of the data, where a well-defined target “iron-ore” class is to be separated from a poorly defined non-target class. We apply a classifier with reject option to known data to create a discriminant function that best separates sampled data, while simultaneously “protecting” against new unseen data by “closing” the domain in feature space occupied by the target class. This shows a substantial 4% improvement in classification performance. Our predictive confidence maps successfully identify known areas of iron ore deposits through the Yilgarn Craton, an area that is not heavily sampled in training, as well as suggesting areas for further exploration throughout the Yilgarn Craton. These areas tend to be more concentrated in the north and west of the Yilgarn Craton, such as around the Twin Peaks mine (~ 27°S, 116°E) and a series of lineaments running east–west at ~ 25°S. Within the Pilbara Craton, potential areas for further expansion occur throughout the Marble Bar vicinity between the existing Spinifex Ridge and Abydos mines (21°S, 119–121°E), as well as small, isolated areas north of the Hamersley Group at ~ 21.5°S, ~ 118°E. We also test the usefulness of radiometric data for province-scale iron ore exploration, while our selected classifier makes no use of the radiometric data, we demonstrate that there is no performance penalty from including redundant data and features, suggesting that where possible all potentially pertinent data should be included within a data-driven analysis. This methodology lends itself to large scale, reconnaissance mineral explorations, and, through varying the datasets used and the commodity being targeted, predictive confidence maps for a wide range of minerals can be produced. 相似文献