共查询到20条相似文献，搜索用时 31 毫秒
1.
de Souza Luis Eduardo Costa João Felipe C. L. Koppe Jair C. 《Natural Resources Research》2004,13(1):1-15
For many decades the mining industry regarded resources/reserves estimation and classification as a mere calculation requiring basic mathematical and geological knowledge. Most methods were based on geometrical procedures and spatial data distribution. Therefore, uncertainty associated with tonnages and grades either were ignored or mishandled, although various mining codes require a measure of confidence in the values reported. Traditional methods fail in reporting the level of confidence in the quantities and grades. Conversely, kriging is known to provide the best estimate and its associated variance. Among kriging methods, Ordinary Kriging (OK) probably is the most widely used one for mineral resource/reserve estimation, mainly because of its robustness and its facility in uncertainty assessment by using the kriging variance. It also is known that OK variance is unable to recognize local data variability, an important issue when heterogeneous mineral deposits with higher and poorer grade zones are being evaluated. Altenatively, stochastic simulation are used to build local or global uncertainty about a geological attribute respecting its statistical moments. This study investigates methods capable of incorporating uncertainty to the estimates of resources and reserves via OK and sequential gaussian and sequential indicator simulation The results showed that for the type of mineralization studied all methods classified the tonnages similarly. The methods are illustrated using an exploration drill hole data sets from a large Brazilian coal deposit. 相似文献
2.
In most modern coal mines, there are many coal quality parameters that are measured on samples taken from boreholes. These data are used to generate spatial models of the coal quality parameters, typically using inverse distance as an interpolation method. At the same time, downhole geophysical logging of numerous additional boreholes is used to measure various physical properties but no coal quality samples are taken. The work presented in this paper uses two of the most important coal quality variables—ash and volatile matter—and assesses the efficacy of using a number of geostatistical interpolation methods to improve the accuracy of the interpolated models, including the use of auxiliary variables from geophysical logs. A multivariate spatial statistical analysis of ash, volatile matter and several auxiliary variables is used to establish a co-regionalization model that relates all of the variables as manifestations of an underlying geological characteristic. A case study of a coal mine in Queensland, Australia, is used to compare the interpolation methods of inverse distance to ordinary kriging, universal kriging, co-kriging, regression kriging and kriging with an external drift. The relative merits of these six methods are compared using the mean error and the root mean square error as measures of bias and accuracy. The study demonstrates that there is significant opportunity to improve the estimations of coal quality when using kriging with an external drift. The results show that when using the depth of a sample as an external drift variable there is a significant improvement in the accuracy of estimation for volatile matter, and when using wireline density logs as the drift variable there is improvement in the estimation of the in situ ash. The economic benefit of these findings is that cheaper proxies for coal quality parameters can significantly increase data density and the quality of estimations.
相似文献3.
Mine planning is influenced by many sources of uncertainty. Significant sources of geological uncertainty in mine planning include uncertainty in layout of geological domains and uncertainty in metal grades. These two sources of uncertainty cannot be modeled separately because the distribution of the grade is controlled usually by geological domains. Two approaches exist for combining these two sources of uncertainty: the joint simulation approach and the cascade approach. In this paper, these two approaches were compared using a real case study. To this end, uncertainty in iron grade (quantitative variable) and ore zones (qualitative variable) was modeled using both approaches. There were some considerable differences in the results obtained by each approach, which confirm the importance of choosing the most appropriate approach with consideration of the dominate features of a deposit.
相似文献4.
Francky Fouedjio 《Natural Resources Research》2016,25(3):283-296
This article addresses the problem of the prediction of the breccia pipe elevation named Braden at the El Teniente mine in Chile. This mine is one of the world’s largest known porphyry-copper ore bodies. Knowing the exact location of the pipe surface is important, as it constitutes the internal limit of the deposit. The problem is tackled by applying a non-stationary geostatistical method based on space deformation, which involves transforming the study domain into a new domain where a standard stationary geostatistical approach is more appropriate. Data from the study domain is mapped into the deformed domain, and classical stationary geostatistical techniques for prediction can then be applied. The predicted results are then mapped back into the original domain. According to the results, this non-stationary geostatistical method outperforms the conventional stationary one in terms of prediction accuracy and conveys a more informative uncertainty model of the predictions. 相似文献
5.
From May 7 to August 13, 1985, soil moisture was measured at 12 study sites located along a 200-km east-west trending transect in west-central Oklahoma. Soil moisture was sampled at three depths at each site: 15 cm, 61 cm, and 91 cm. Study site location and the time (week) of data collection were analyzed through correlation and regression analysis in order to assess their impact on soil moisture variability measured at the three sampled depths. Along the transect for the study period, soil moisture increased with depth; soil moisture also increased with depth from west to east along the transect during the sample period. The correlation between the location of the sample site and soil moisture was weak at the 15 cm depth (0.48), but was stronger at greater depths (0.78 at 61 cm; 0.65 at 91 cm). The location of the study site along the transect explained 25% of the variation in soil moisture at a 15 cm depth; 62% at a 61 cm depth; and 51% at a 91 cm depth. The time (week variable) of data collection at each sample site was less useful in explaining the variability in soil moisture than site location. Time explains 15, 23, and 16% of the variability observed in soil moisture along the transect for the depths of 15, 61, and 91 cm, respectively. A combination of time and location variables, however, explained 46% of the variability in soil moisture for all three depths. The same time and location variables explained 55%, 76%, and 52% of the variability observed in soil moisture for the three individual depths: 15, 61, and 91 cm, respectively. Unusual precipitation events affected the transect throughout the study period and diminished the impact of location as a significant explanatory variable for describing variability in soil moisture. 相似文献
6.
7.
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
8.
There are multiple ways to characterize uncertainty in the assessment of coal resources, but not all of them are equally satisfactory. Increasingly, the tendency is toward borrowing from the statistical tools developed in the last 50 years for the quantitative assessment of other mineral commodities. Here, we briefly review the most recent of such methods and formulate a procedure for the systematic assessment of multi-seam coal deposits taking into account several geological factors, such as fluctuations in thickness, erosion, oxidation, and bed boundaries. A lignite deposit explored in three stages is used for validating models based on comparing a first set of drill holes against data from infill and development drilling. Results were fully consistent with reality, providing a variety of maps, histograms, and scatterplots characterizing the deposit and associated uncertainty in the assessments. The geostatistical approach was particularly informative in providing a probability distribution modeling deposit wide uncertainty about total resources and a cumulative distribution of coal tonnage as a function of local uncertainty. 相似文献
9.
Qunying Huang 《International journal of geographical information science》2017,31(3):523-541
Social media applications are widely deployed in mobile platforms equipped with built-in GPS tracking devices, and these devices have led to an unprecedented collection of geolocated data (geo-tags). Geo-tags, along with place names, offer new opportunities to explore the trajectory and mobility patterns of social media users. However, trajectory data captured by social media are sparsely and irregularly spaced and therefore have varying degrees of resolution in both space and time. Previous studies on next location prediction are mostly applicable for detecting the upcoming location of a moving object using dense GPS trajectories where locations are recorded at regular time intervals (e.g., 1 minute). Additionally, point features are commonly used to represent the locations of visits, but using point features cannot capture the variability of human mobility. This article introduces a new methodology to predict an individual’s next location based on sparse footprints accumulated over a long time period using social networks, and uses polygons to represent the location corresponding to the physical activity area of individuals. First, the density-based spatial clustering algorithm is employed to discover the most representative activity zones that an individual frequently visits on a daily basis, and a polygon-based region is then derived for each representative activity zone. A sparse mobility Markov chain model considering both the movements and online behaviors of the social media user is trained and used to predict the user’s next location. Initial experiments with a group of Washington DC Twitter users demonstrate that the proposed methodology successfully discovers the activity regions and predicts the user’s next location with accuracy approaching 78.94%. 相似文献
10.
Variability in diatom and chrysophyte assemblages and inferred pH: paleolimnological studies of Big Moose Lake,New York,USA 总被引：1，自引：0，他引：1
Donald F. Charles Sushil S. Dixit Brian F. Cumming John P. Smol 《Journal of Paleolimnology》1991,5(3):267-284
We measured variability in the composition of diatom and chrysophyte assemblages, and the pH inferred from these assemblages, in sediment samples from Big Moose Lake, in the Adirondack Mountains of New York. Replicate samples were analyzed from (1) a single sediment core interval, (2) 12 different intervals from each of 3 separate cores, and (3) 10 widely spaced surface sediment samples (0–1 cm). The variability associated with sample preparation (subsampling, processing, and counting) was relatively small compared to between-core and within-lake variability. The relative abundances of the dominant diatom taxa varied to a greater extent than those of the chrysophyte scale assemblages. Standard deviations of pH inferences for multiple counts from the same sediment interval from diatom, chrysophyte, and diatom plus chrysophyte inference equations were 0.04 (n=8), 0.06 (n=32), and 0.06 (n=8) of a pH unit, respectively. Stratigraphic analysis of diatoms and chrysophytes from three widely spaced pelagic sediment cores provided a similar record of lake acidification trends, although with slight differences in temporal rates of change. Average standard deviations of pH inferences from diatom, chrysophyte and diatom plus chrysophyte inference equations for eight sediment intervals representing similar time periods but in different cores were 0.10, 0.20, and 0.09 pH unit, respectively. Our data support the assumption that a single sediment core can provide an accurate representation of historical change in a lake. The major sources of diatom variability in the surface sediments (i.e., top 1.0 cm) were (1) differences in diatom assemblage contributions from benthic and littoral sources, and (2) the rapid change in assemblage composition with sediment depth, which is characteristic of recently acidified lakes. Because scaled chrysophytes are exclusively planktonic, their spatial distribution in lake sediments is less variable than the diatom assemblages. Standard deviations of pH inferences for 10 widely spaced surface sediment samples from diatom, chrysophyte and diatom plus chrysophyte inference equations were 0.21, 0.09, and 0.16 of a pH unit, respectively. 相似文献
11.
A. Geddes D.A. Elston M.E.A. Hodgson R. V. Birnie 《International journal of geographical information science》2013,27(4):785-803
Handling of uncertainty in the estimation of values from source areas to target areas poses a challenge in areal interpolation research. Stochastic model-based methods offer a basis for incorporating such uncertainty, but to date they have not been widely adopted by the GIS community. In this article, we propose one use of such methods based in the problem of interpolating count data from a source set of zones (parishes) to a more widely used target zone geography (postcode sectors). The model developed also uses ancillary statistical count data for a third set of areas nested within both source and target zones. The interpolation procedure was implemented within a Bayesian statistical framework using Markov chain Monte Carlo methods, enabling us to take account of all sources of uncertainty included in the model. Distributions of estimated values at the target zone level are presented using both summary statistics and as individual realisations selected to illustrate the degree of uncertainty in the interpolation results. We aim to describe the use of such stochastic approaches in an accessible way and to highlight the need for quantifying estimation uncertainty arising in areal interpolation, especially given the implications arising when interpolated values are used in subsequent analyses of relationships. 相似文献
12.
One of the uses of geostatistical conditional simulation is as a tool in assessing the spatial uncertainty of inputs to the
Monte Carlo method of system uncertainty analysis. Because the number of experimental data in practical applications is limited,
the geostatistical parameters used in the simulation are themselves uncertain. The inference of these parameters by maximum
likelihood allows for an easy assessment of this estimation uncertainty which, in turn, may be included in the conditional
simulation procedure. A case study based on transmissivity data is presented to show the methodology whereby both model selection
and parameter inference are solved by maximum likelihood. 相似文献
13.
Any mine planning requires careful prediction of both the head grade andtonnage ofmineralization. There are various methods of interpolation that attempt to provide reasonable estimatesat unsampled locations. All of these give realizations that are unduly smooth and extremevalues that occur in reality are not reflected in these estimates. Such methods, therefore,provide limited scope for accurate risk assessment. An alternative approach that is rapidlygaining popularity is the method of conditional simulation. This approach attempts to reproduceboth the grade distributions of the sample data as well as its spatial variability. In this paper,a case study is presented on a platinum mineralization to demonstrate and compare sequentialGaussian and sequential conditional simulation techniques and to quantify and discuss therelevant sensitivities. 相似文献
14.
The factors determining the suitability of limestone for industrial use and its commercial value are the amounts of calcium oxide (CaO) and impurities. From 244 sample points in 18 drillhole sites in a limestone mine, southwestern Japan, data on four impurity elements, SiO2, Fe2O3, MnO, and P2O5 were collected. It generally is difficult to estimate spatial distributions of these contents, because most of the limestone bodies in Japan are located in the accretionary complex lithologies of Paleozoic and Mesozoic age. Because the spatial correlations of content data are not clearly shown by variogram analysis, a feedforward neural network was applied to estimate the content distributions. The network structure consists of three layers: input, middle, and output. The input layer has 17 neurons and the output layer four. Three neurons in the input layer correspond with x, y, z coordinates of a sample point and the others are rock types such as crystalline and conglomeratic limestones, and fossil types related to the geologic age of the limestone. Four neurons in the output layer correspond to the amounts of SiO2, Fe2O3, MnO, and P2O5. Numbers of neurons in the middle layer and training data differ with each estimation point to avoid the overfitting of the network. We could detect several important characteristics of the three-dimensional content distributions through the network such as a continuity of low content zones of SiO2 along a Lower Permian fossil zone trending NE-SW, and low-quality zones located in depths shallower than 50 m. The capability of the neural network-based method compared with the geostatistical method is demonstrated from the viewpoints of estimation errors and spatial characteristics of multivariate data. To evaluate the uncertainty of estimates, a method that draws several outputs by changing coordinates slightly from the target point and inputting them to the same trained network is proposed. Uncertainty differs with impurity elements, and is not based on just the spatial arrangement of data points. 相似文献
15.
K. David Newell 《Natural Resources Research》2007,16(1):55-66
Drill cuttings can be used for desorption analyses but with more uncertainty than desorption analyses done with cores. Drill
cuttings are not recommended to take the place of core, but in some circumstances, desorption work with cuttings can provide
a timely and economic supplement to that of cores. The mixed lithologic nature of drill cuttings is primarily the source of
uncertainty in their analysis for gas content, for it is unclear how to apportion the gas generated from both the coal and
the dark-colored shale that is mixed in usually with the coal. In the Western Interior Basin Coal Basin in eastern Kansas
(Pennsylvanian-age coals), dark-colored shales with normal (∼100 API units) gamma-ray levels seem to give off minimal amounts
of gas on the order of less than five standard cubic feet per ton (scf/ton). In some cuttings analyses this rule of thumb
for gas content of the shale is adequate for inferring the gas content of coals, but shales with high-gamma-ray values (>150
API units) may yield several times this amount of gas. The uncertainty in desorption analysis of drill cuttings can be depicted
graphically on a diagram identified as a “lithologic component sensitivity analysis diagram.” Comparison of cuttings desorption
results from nearby wells on this diagram, can sometimes yield an unique solution for the gas content of both a dark shale
and coal mixed in a cuttings sample. A mathematical solution, based on equating the dry, ash-free gas-contents of the admixed
coal and dark-colored shale, also yields results that are correlative to data from nearby cores. 相似文献
16.
整合已有土壤样点的数字土壤制图补样方案 总被引：1，自引：0，他引：1
我国多数地区经过两次土壤普查以及科研工作者的野外调查, 积累了一定数量的土壤样点“( 已有样点”)。本文在充分整合已有样点的基础上, 提出了逐次、高效地设计补充样点的方案, 它包括3 个步骤:首先, 确定已有样点集的空间代表范围“( 可推测范围”);然后, 将已有样点集不能代表范围内的每一个栅格都看作一个候选样点, 计算每一个候选样点的可扩推范围, 选择可扩推范围最大的点作为第一个补充样点;最后, 基于已有样点和补充样点更新样点集的空间代表范围图。重复以上过程, 直至新样点集的空间代表范围能够覆盖整个研究区。该方法在充分利用已有样点资源的基础上, 不仅能够确定补充样点的数量、位置, 而且能够给定补充样点的重要性次序, 在采样资源有限的情况下, 为采样者合理地选择样点提供了重要依据。 相似文献
17.
Guofeng Cao Phaedon C. Kyriakidis Michael F. Goodchild 《International journal of geographical information science》2013,27(11):1773-1791
Categorical spatial data, such as land use classes and socioeconomic statistics data, are important data sources in geographical information science (GIS). The investigation of spatial patterns implied in these data can benefit many aspects of GIS research, such as classification of spatial data, spatial data mining, and spatial uncertainty modeling. However, the discrete nature of categorical data limits the application of traditional kriging methods widely used in Gaussian random fields. In this article, we present a new probabilistic method for modeling the posterior probability of class occurrence at any target location in space-given known class labels at source data locations within a neighborhood around that prediction location. In the proposed method, transition probabilities rather than indicator covariances or variograms are used as measures of spatial structure and the conditional or posterior (multi-point) probability is approximated by a weighted combination of preposterior (two-point) transition probabilities, while accounting for spatial interdependencies often ignored by existing approaches. In addition, the connections of the proposed method with probabilistic graphical models (Bayesian networks) and weights of evidence method are also discussed. The advantages of this new proposed approach are analyzed and highlighted through a case study involving the generation of spatial patterns via sequential indicator simulation. 相似文献
18.
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
19.
Reservoir models have large uncertainty because of spatial variability and limited sample data. The ultimate aim is to use simultaneously all available data sources to reduce uncertainty and provide reliable reservoir models for resource assessment and flow simulation. Seismic impedance or some other attribute provides a key source of data for reservoir modeling. These seismic data are at a coarser scale than the hard well data and it not an exact measurement of facies proportions or porosity. A requirement for data integration is the cross-covariance between the well and seismic data.The size-scaling behavior of the cross correlation for different measurement scales was nvestigated. The size-scaling relationship is derived theoretically and validated by numerical studies (including an example with real data). The limit properties of the cross-correlation coefficient when the averaging volume becomes large is shown. After some averaging volume, the volume-dependent cross-correlation coefficient reaches a limit value. This plateau value is controlled mainly by the large-scale behavior of the cross and direct variograms.The cross correlation can increase or decrease with volume support depending on the relative importance of long- and short-scale covariance structures. If the direct and cross variograms are proportional, there is no change in the cross correlation as the averaging volume changes. Our study shows that the volume-dependent cross-correlation coefficient is sensitive to the shape of the cross variogram and differences between the direct variograms of the well data and seismic data. 相似文献
20.
Neural Network-Based Estimation of Principal Metal Contents in the Hokuroku District,Northern Japan,for Exploring Kuroko-Type Deposits 总被引：7，自引：0，他引：7
Koike Katsuaki Matsuda Setsuro Suzuki Toru Ohmi Michito 《Natural Resources Research》2002,11(2):135-156
The Hokuroku district, extending over 40 × 40 km2 in northern Japan, is known to be dominated by kuroko-type massive sulfide deposits that have a genetic relation to submarine volcanic activity. The deposits are hosted in a specific stratigraphic zone of Miocene volcanic rocks. Because kuroko-type deposits are under exploration in several countries, it is important to integrate the geologic and geochemical data that have been accumulated in the Hokuroku district to characterize the distribution of deposits and produce a map of mineral potential. Thus, we collected data on multiple chemical components from 1917 rock cores at 143 drillhole sites and concentrated on components with relatively large amounts of data, which are SiO2, Al2O3, and Fe2O3 as major elements and Cu, Pb, and Zn as trace elements. Although frequencies of these data can be approximated by normal or lognormal distributions, spatial correlation structures cannot be extracted from the semivariograms of each component nor from the cross-semivariograms between two components of the major or minor elements. To handle such complexity, a spatial method of modeling content distribution, SLANS, is developed by applying a feedforward neural network. The principle of SLANS is to train a network repeatedly to recognize the relation between the data value and the location and lithology of a sample point. One-hundred outputs for each element are obtained by changing the numbers of neurons in a middle layer from 1 to 10 and sample data used for training from 3 to 12, and finally one output is selected based on the estimation precision of the network which is restricted near the target point. After constructing a geologic distribution model from the geological column classified into 25 rock codes, three-dimensional distributions of Cu, Pb, and Zn contents are estimated over the study area. The content models are considered to be valid because high-content zones are located on the known mine sites and the margins of ancient volcanoes or calderas. Some zones are distributed along strikes of major deep-seated fractures in the district. 相似文献