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1.
This paper is concerned with the problem of predicting the surface elevation of the Braden breccia pipe at the El Teniente mine in Chile. This mine is one of the world’s largest and most complex porphyry-copper ore systems. As the pipe surface constitutes the limit of the deposit and the mining operation, predicting it accurately is important. The problem is tackled by applying a geostatistical approach based on closed-form non-stationary covariance functions with locally varying anisotropy. This approach relies on the mild assumption of local stationarity and involves a kernel-based experimental local variogram a weighted local least-squares method for the inference of local covariance parameters and a kernel smoothing technique for knitting the local covariance parameters together for kriging purpose. According to the results, this non-stationary geostatistical method outperforms the traditional stationary geostatistical method in terms of prediction and prediction uncertainty accuracies. 相似文献
2.
地统计方法学研究进展 总被引：15，自引：2，他引：13
地统计方法学已经成为空间预测与不确定性分析的关键性工具。本研究从文献计量学和方法学演变过程两个角度开展了1967~2005年的地统计方法学研究综述研究。首先从宏观角度分析其发展趋势、应用情况与模式,然后总结其演化规律、适宜性和选择原则。研究表明:地统计方法学的演化规律表现为稳态向非稳态演变、单变量向多变量(含二次信息)演变、参数与非参数方法相互补充、线性向非线性方法演变和空间静态向时空动态演变;此外,未来研究发展方向集中在半变异函数估计新方法、不确定性地统计学、时空地统计学与多点地统计学、机理模型与地统计学耦合研究和基于地统计学模拟的不确定性决策等。 相似文献
3.
不同时间尺度、季节的气温数据表现出不同的空间平稳特征。为探讨分析空间平稳性对气温插值的影响规律,采用趋势线法对气温数据进行空间平稳性探索,并对比分析不同空间平稳性条件下,普通线性回归、普通克里格、回归克里格的气温插值精度及插值结果的空间分布特点。结果显示：冬季日均、月均气温与年均气温呈现空间非平稳,插值精度随时间序列的增长而提高,随着气温数据逐渐趋于稳定,精度提高的幅度逐渐下降;夏季日均、月均气温呈现空间平稳,随时间序列的增长,插值精度的提高并不显著;夏季日均气温各插值方法的插值精度普遍高于冬季日均气温。与普通克里格相比,回归克里格能有效提高空间非平稳数据的插值精度。时间序列的增长削弱了不同插值算法之间的插值精度差异和插值结果空间分布差异。 相似文献
4.
Mineral resource evaluation requires defining grade domains of an ore deposit. Common practice in mineral resource estimation
consists of partitioning the ore body into several grade domains before the geostatistical modeling and estimation at unsampled
locations. Many ore deposits are made up of different mineralogical ensembles such as oxide and sulfide zone: being able to
model the spatial layout of the different grades is vital to good mine planning and management. This study addresses the application
of the plurigaussian simulation to Sivas (Turkey) gold deposits for constructing grade domain models that reproduce the contacts
between different grade domains in accordance with geologist’s interpretation. The method is based on the relationship between
indicator variables from grade distributions on the Gaussian random functions chosen to represent them. Geological knowledge
is incorporated into the model by the definition of the indicator variables, their truncation strategy, and the grade domain
proportions. The advantages of the plurigaussian simulation are exhibited through the case study. The results indicated that
the processes are seen to respect reproducing complex geometrical grades of an ore deposit by means of simulating several
grade domains with different spatial structure and taking into account their global proportions. The proposed proportion model
proves as simple to use in resource estimation, to account for spatial variations of the grade characteristics and their distribution
across the studied area, and for the uncertainty in the grade domain proportions. The simulated models can also be incorporated
into mine planning and scheduling. 相似文献
5.
Samanta Biswajit Bandopadhyay Sukumar Ganguli Rajive Dutta Sridhar 《Natural Resources Research》2004,13(3):189-200
In this paper, sparse data problem in neural network and geostatistical modeling for ore-grade estimation was addressed in the Nome offshore placer gold deposit. The problem of sparse data arises because of the random data division into training, validation, and test subsets during ore-grade modeling. In this regard, the possibility of generating statistically dissimilar data subsets by random data division was also explored through a simulation exercise. A combined approach of data segmentation and application of a Kohonen network then was used to solve the data division problem. Two neural networks and five kriging models were applied for grade modeling. The neural network was trained using an early stopping method. Performance evaluation of the models was carried out on the test data set. The study results indicated that all the models that were investigated in this study performed almost equally. It was also revealed that by using the secondary variable watertable depth the neural network and the kriging models slightly improved their prediction precision. Further, the overall R
2 of the models was poor as a result of high nugget (noisy) component in ore-grade variation. 相似文献
6.
M. A. OLIVER R. WEBSTER 《International journal of geographical information science》2013,27(3):313-332
Geographical information systems could be improved by adding procedures for geostatistical spatial analysis to existing facilities. Most traditional methods of interpolation are based on mathematical as distinct from stochastic models of spatial variation. Spatially distributed data behave more like random variables, however, and regionalized variable theory provides a set of stochastic methods for analysing them. Kriging is the method of interpolation deriving from regionalized variable theory. It depends on expressing spatial variation of the property in terms of the variogram, and it minimizes the prediction errors which are themselves estimated. We describe the procedures and the way we link them using standard operating systems. We illustrate them using examples from case studies, one involving the mapping and control of soil salinity in the Jordan Valley of Israel, the other in semi-arid Botswana where the herbaceous cover was estimated and mapped from aerial photographic survey. 相似文献
7.
Additional Samples: Where They Should Be Located 总被引：2，自引：0，他引：2
Information for mine planning requires to be close spaced, if compared to the grid used for exploration and resource assessment. The additional samples collected during quasimining usually are located in the same pattern of the original diamond drillholes net but closer spaced. This procedure is not the best in mathematical sense for selecting a location. The impact of an additional information to reduce the uncertainty about the parameter been modeled is not the same everywhere within the deposit. Some locations are more sensitive in reducing the local and global uncertainty than others. This study introduces a methodology to select additional sample locations based on stochastic simulation. The procedure takes into account data variability and their spatial location. Multiple equally probable models representing a geological attribute are generated via geostatistical simulation. These models share basically the same histogram and the same variogram obtained from the original data set. At each block belonging to the model a value is obtained from the n simulations and their combination allows one to access local variability. Variability is measured using an uncertainty index proposed. This index was used to map zones of high variability. A value extracted from a given simulation is added to the original data set from a zone identified as erratic in the previous maps. The process of adding samples and simulation is repeated and the benefit of the additional sample is evaluated. The benefit in terms of uncertainty reduction is measure locally and globally. The procedure showed to be robust and theoretically sound, mapping zones where the additional information is most beneficial. A case study in a coal mine using coal seam thickness illustrates the method. 相似文献
8.
Oktay Erten Mehmet Siddik Kizil Erkan Topal Lachlan McAndrew 《Natural Resources Research》2013,22(3):207-227
Tropical laterite-type bauxite deposits often pose a unique challenge for resource modelling and mine planning due to the extreme lateral variability at the base of the bauxite ore unit within the regolith profile. An economically viable drilling grid is often rather sparse for traditional prediction techniques to precisely account for the lateral variability in the lower contact of a bauxite ore unit. However, ground-penetrating radar (GPR) offers an inexpensive and rapid method for delineating laterite profiles by acquiring fine-scale data from the ground. These numerous data (secondary variable) can be merged with sparsely spaced borehole data (primary variable) through various statistical and geostatistical techniques, provided that there is a linear relation between the primary and secondary variables. Four prediction techniques, including standard linear regression, simple kriging with varying local means, co-located cokriging and kriging with an external drift, were used in this study to incorporate exhaustive GPR data in predictive estimation the base of a bauxite ore unit within a lateritic bauxite deposit in Australia. Cross-validation was used to assess the performance of each technique. The most robust estimates are produced using ordinary co-located cokriging in accordance with the cross-validation analysis. Comparison of the estimates against the actual mine floor indicates that the inclusion of ancillary GPR data substantially improves the quality of the estimates representing the bauxite base surface. 相似文献
9.
气象站点观测降水难以精确反映降水时空分布与变化,而雷达降水存在复杂地形区域精度不高等问题。为了最大限度发挥两者的优势,文章以广东省北部山区为研究区域,选择2018-08-26—30一次暴雨过程为研究对象,结合地形、与海岸线距离、植被指数、经纬度等地表辅助参量,分析地面站点降水与地表辅助参量、雷达降水的相关关系,利用XGBoost算法与克里金插值方法,构建地面-雷达日降水数据融合模型,得到了空间分辨率为1 km的日降水融合数据集。此外,采用多元线性回归（LM）与克里金插值方法,实现了地面-雷达日降水数据的融合,并利用地面降水数据分别对XGBoost与LM日降水融合性能进行精度验证。结果表明：1）地面降水与雷达降水存在显著的正相关,地面降水与地表辅助参量之间的相关性随时间变化;2）XGBoost预测精度整体上高于LM预测结果;经模型残差校正后,XGBoost融合模型的精度整体上优于LM融合模型,这是因为XGBoost方法在捕捉地面降水与地表辅助参量、雷达降水之间关系性能上优于LM方法。 相似文献
10.
Spatial uncertainty analysis is a complex and difficult task for orebody estimation in the mining industry. Conventional models (kriging and its variants) with variogram-based statistics fail to capture the spatial complexity of an orebody. Due to this, the grade and tonnage are incorrectly estimated resulting in inaccurate mine plans, which lead to costly financial decision. Multiple-point geostatistical simulation model can overcome the limitations of the conventional two-point spatial models. In this study, a multiple-point geostatistical method, namely SNESIM, was applied to generate multiple equiprobable orebody models for a copper deposit in Africa, and it helped to analyze the uncertainty of ore tonnage of the deposit. The grade uncertainty was evaluated by sequential Gaussian simulation within each equiprobable orebody models. The results were validated by reproducing the marginal distribution and two- and three-point statistics. The results show that deviations of volume of the simulated orebody models vary from ? 3 to 5% compared to the training image. The grade simulation results demonstrated that the average grades from the different simulation are varied from 3.77 to 4.92% and average grade 4.33%. The results also show that the volume and grade uncertainty model overestimates the orebody volume as compared to the conventional orebody. This study demonstrates that incorporating grade and volume uncertainty leads to significant changes in resource estimates. 相似文献
11.
Paul Harris Chris Brunsdon Martin Charlton 《International journal of geographical information science》2013,27(3):511-541
In this study, we demonstrate a novel use of comaps to explore spatially the performance, specification and parameterisation of a non-stationary geostatistical predictor. The comap allows the spatial investigation of the relationship between two geographically referenced variables via conditional distributions. Rather than investigating bivariate relationships in the study data, we use comaps to investigate bivariate relationships in the key outputs of a spatial predictor. In particular, we calibrate moving window kriging (MWK) models, where a local variogram is found at every target location. This predictor has often proved worthy for processes that are heterogeneous, and most standard (global variogram) kriging algorithms can be adapted in this manner. We show that the use of comaps enables a better understanding of our chosen MWK models, which in turn allows a more informed choice when selecting one MWK specification over another. As case studies, we apply four variants of MWK to two heterogeneous example data sets: (i) freshwater acidification critical load data for Great Britain and (ii) London house price data. As both of these data sets are strewn with local anomalies, three of our chosen models are robust (and novel) extensions of MWK, where at least one of which is shown to perform better than a non-robust counterpart. 相似文献
12.
高精度的中长期径流预报信息是水资源规划管理与水利工程经济运行的重要基础支撑。论文在组合预报与误差修正2类径流预报后处理方法串联应用的技术框架下,考虑径流的高度非平稳与非线性等特征,提出了基于时变权重组合和贝叶斯修正的中长期径流预报方法。应用该方法开展了云南龙江水库年、月入库径流预报的实例研究,结果表明时变权重组合平衡了已建立的随机森林与支持向量机模型在建模期与检验期预报性能的差异,经贝叶斯修正后的预报精度接近或优于两阶段各自的最优单一模型。根据年径流预报结果判断水文年型的正确率达到77.2%,月预报径流的确定性系数超过0.90。因此,该方法在提升中长期径流预报精度方面具有积极效果。 相似文献
13.
陕西省铜川矿区居民对环境问题的感知 总被引：1，自引：0，他引：1
陕西省铜川矿区王石凹矿的资源丰富,但环境问题严重。通过实地调查和490份有效问卷的统计,利用秩和检验等方法,探讨了对居民环境问题感知的影响因素。主要结论是:①矿区居民对居住环境普遍不满意,他们认为最严重的环境问题是水污染。产生环境问题的最主要原因是采煤。②在居民属性与环境问题感知方面发现:性别对大气污染的感知有明显的影响。年龄和居住时间对塌陷和地裂缝的感知有显著影响。居民居住地点对矿区环境问题的感知均具有显著性差异。 相似文献
14.
A Magnetotelluric (MT) sounding was carried out at a site in south-east Queensland, in the Clarence-Moreton Basin. The synoptic recordings were taken over a period of four months at sampling frequencies from 500 Hz to 5 × 10-5 Hz. The resulting data was analysed by the stationary cross-frequency and the Cone kernel time-frequency distribution (TFD) methods of MT analysis. The results were compared as apparent resistivities on a daily basis for frequencies above 1 Hz, as well as over all the available data. The TFD MT apparent-resistivity results were more stable and less noisy on an daily basis than the cross-frequency results. Similarly the TFD analysis gave less noisy results than the cross-frequency analysis when all available data was processed. Application of these new non-stationary analysis techniques to MT processing should decrease the bias error problem of the MT methods and so increase reliability and repeatability of MT soundings. 相似文献
15.
Martin Ryan Machuca-Mory David Leuangthong Oy Boisvert Jeff B. 《Natural Resources Research》2019,28(2):291-307
Incorporating locally varying anisotropy (LVA) in geostatistical modeling improves estimates for structurally complex domains where a single set of anisotropic parameters modeled globally do not account for all geological features. In this work, the properties of two LVA-geostatistical modeling frameworks are explored through application to a complexly folded gold deposit in Ghana. The inference of necessary parameters is a significant requirement of geostatistical modeling with LVA; this work focuses on the case where LVA orientations, derived from expert geological interpretation, are used to improve the grade estimates. The different methodologies for inferring the required parameters in this context are explored. The results of considering different estimation frameworks and alternate methods of parameterization are evaluated with a cross-validation study, as well as visual inspection of grade continuity along select cross sections. Results show that stationary methodologies are outperformed by all LVA techniques, even when the LVA framework has minimal guidance on parameterization. Findings also show that additional improvements are gained by considering parameter inference where the LVA orientations and point data are used to infer the local range of anisotropy. Considering LVA for geostatistical modeling of the deposit considered in this work results in better reproduction of curvilinear geological features.
相似文献16.
An important aim of modern geostatistical modeling is to quantify uncertainty in geological systems. Geostatistical modeling
requires many input parameters. The input univariate distribution or histogram is perhaps the most important. A new method
for assessing uncertainty in the histogram, particularly uncertainty in the mean, is presented. This method, referred to as
the conditional finite-domain (CFD) approach, accounts for the size of the domain and the local conditioning data. It is a
stochastic approach based on a multivariate Gaussian distribution. The CFD approach is shown to be convergent, design independent,
and parameterization invariant. The performance of the CFD approach is illustrated in a case study focusing on the impact
of the number of data and the range of correlation on the limiting uncertainty in the parameters. The spatial bootstrap method
and CFD approach are compared. As the number of data increases, uncertainty in the sample mean decreases in both the spatial
bootstrap and the CFD. Contrary to spatial bootstrap, uncertainty in the sample mean in the CFD approach decreases as the
range of correlation increases. This is a direct result of the conditioning data being more correlated to unsampled locations
in the finite domain. The sensitivity of the limiting uncertainty relative to the variogram and the variable limits are also
discussed. 相似文献
17.
Talebi Hassan Mueller Ute Tolosana-Delgado Raimon Grunsky Eric C. McKinley Jennifer M. Caritat Patrice de 《Natural Resources Research》2019,28(3):869-891
Prediction of true classes of surficial and deep earth materials using multivariate spatial data is a common challenge for geoscience modelers. Most geological processes leave a footprint that can be explored by geochemical data analysis. These footprints are normally complex statistical and spatial patterns buried deep in the high-dimensional compositional space. This paper proposes a spatial predictive model for classification of surficial and deep earth materials derived from the geochemical composition of surface regolith. The model is based on a combination of geostatistical simulation and machine learning approaches. A random forest predictive model is trained, and features are ranked based on their contribution to the predictive model. To generate potential and uncertainty maps, compositional data are simulated at unsampled locations via a chain of transformations (isometric log-ratio transformation followed by the flow anamorphosis) and geostatistical simulation. The simulated results are subsequently back-transformed to the original compositional space. The trained predictive model is used to estimate the probability of classes for simulated compositions. The proposed approach is illustrated through two case studies. In the first case study, the major crustal blocks of the Australian continent are predicted from the surface regolith geochemistry of the National Geochemical Survey of Australia project. The aim of the second case study is to discover the superficial deposits (peat) from the regional-scale soil geochemical data of the Tellus Project. The accuracy of the results in these two case studies confirms the usefulness of the proposed method for geological class prediction and geological process discovery.
相似文献18.
同位模式表示不同类型的实体在空间邻域内共同频繁出现的规律,是城市实体空间关联的主要表达形式,但不能挖掘出指定实体的空间关联,需要寻找新的计算方法。在城市计算的视角下,通过引入粗糙集研究城市空间关联问题发现：1）该方法能把复杂的地理空间关联问题转换成信息决策问题,在信息决策表中计算城市实体之间的空间关联等拓扑关系,计算过程和结果可以挖掘城市行业之间的空间集聚和关联问题。2）通过属性约简得到属性核可以把高维空间数据降维,找到影响空间关联的重要因子。3）该方法拓宽了城市计算的理论方法体系和粗糙集方法的行业应用。最后,通过Python爬取南宁市城市服务业数据,进行方法的验证,计算结果与成熟的Apriori算法结果,以及南宁市服务业空间关联实际情况基本一致,证明了粗糙空间关联方法的可行性和正确性。 相似文献
19.
Geostatistics applies statistics to quantitatively describe geological sites and assess the uncertainty due to incomplete sampling. Strong assumptions are required regarding the location independence of statistical parameters to construct numerical models with geostatistical tools. Most geological data exhibit large-scale deterministic trends together with short-scale variations. Such location dependence violates the common geostatistical assumption of stationarity. The trend-like deterministic features should be modeled prior to conventional geostatistical prediction and accounted for in subsequent geostatistical calculations. The challenge of using a trend in geostatistical simulation algorithms for the continuous variable is the subject of this paper. A stepwise conditional transformation with a Gaussian mixture model is considered to provide a stable and artifact-free numerical model. The complex features of the regionalized variable in the presence of a trend are removed in the forward transformation and restored in the back transformation. The Gaussian mixture model provides a seamless bin-free approach to transformation. Data from a copper deposit were used as an example. These data show an apparent trend unsuitable for conventional geostatistical algorithms. The result shows that the proposed algorithm leads to improved geostatistical models. 相似文献
20.
Chengyang Qian Ruqiao Jiang Yi Long Qi Zhang Muxian Li 《International journal of geographical information science》2019,33(10):2011-2032
Vehicle trajectory modelling is an essential foundation for urban intelligent services. In this paper, a novel method, Distant Neighbouring Dependencies (DND) model, has been proposed to transform vehicle trajectories into fixed-length vectors which are then applied to predict the final destination. This paper defines the problem of neighbouring and distant dependencies for the first time, and then puts forward a way to learn and memorize these two kinds of dependencies. Next, a destination prediction model is given based on the DND model. Finally, the proposed method is tested on real taxi trajectory datasets. Results show that our method can capture neighbouring and distant dependencies, and achieves a mean error of 1.08 km, which outperforms other existing models in destination prediction significantly. 相似文献