Coastal sediment type map has been widely used in marine economic and engineering activities, but the traditional mapping methods had some limitations due to their intrinsic assumption or subjectivity. In this paper, ...Coastal sediment type map has been widely used in marine economic and engineering activities, but the traditional mapping methods had some limitations due to their intrinsic assumption or subjectivity. In this paper, a non-parametric indicator Kriging method has been proposed for generating coastal sediment map. The method can effectively avoid mapping subjectivity, has no special requirements for the sample data to meet second-order stationary or normal distribution, and can also provide useful information on the quantitative evaluation of mapping uncertainty. The application of the method in the southern sea area of Lianyungang showed that much more convincing mapping results could be obtained compared with the traditional methods such as IDW, Kriging and Voronoi diagram under the same condition, so the proposed method was applicable with great utilization value.展开更多
Uncertainty on the geological contacts and the block volumes of the models along boundaries is often a major part of the global uncertainty of reserve estimation.This work introduces a geostatistical technique that ha...Uncertainty on the geological contacts and the block volumes of the models along boundaries is often a major part of the global uncertainty of reserve estimation.This work introduces a geostatistical technique that has been developed and tested in an iron ore deposit at Bafq mining district,in central Iran,and that,based on a probability criterion,helps to objectively model the geometry of this iron ore deposit.The main problem in reserve estimation of this ore body is its geometrical modeling and uncertainty in geological boundaries.This work deals with the geostatistical method of multiple indicator kriging,which is used to determine the real boundaries of ore body in different categories.This approach has potential to improve project performance and decrease operational risk.For this purpose,the ore body is separated into two categories including rich iron zone(w(Fe)>45%)and poor iron zone(20%<w(Fe)<45%).It significantly benefits to decrease the risk of reserve evaluation in the deposit.This case study also highlights the value of multiple indicator kriging as a tool for estimates the position of grade boundaries within the deposit.Comparison of the resultant probability maps with the real ore/waste contacts on the extracted levels shows that the first indicator model could separate the whole ore body(poor plus rich)from the waste zone by probability of more than 0.35,which concludes the total reserve of 53 million tons.The second indicator model applied to separate the rich and poor domains and the results show that the blocks with the estimated probability of equal to or more than 0.4 lay within the rich ore zone consisting of 15.8 million tons reserve.展开更多
Sequential indicator simulation is a commonly used method for discrete variable simulation in 3D geological modeling and a widely used stochastic simulation method, which can be used not only for continuous variable s...Sequential indicator simulation is a commonly used method for discrete variable simulation in 3D geological modeling and a widely used stochastic simulation method, which can be used not only for continuous variable simulation but also for discrete variable simulation. In this paper, the X Oilfield in the western South China Sea is taken as an example to compare the sequential indicator simulation method and the Indicator Kriging interpolation method. The results of the final comparison show that the results of the lithofacies model established by the Indicator Kriging deterministic interpolation method are overly smooth, and its coincidence rate with the geological statistical results is not high, thus cannot well reflect the heterogeneity of the underground reservoir, while the simulation results of the lithofacies model established by the sequential indicator stochastic simulation method can fit well with the statistical law of the well, which has eliminated the smoothing effect of Kriging interpolation, thus can better reflect the heterogeneity of the underground reservoir. Therefore, the sequential indicator simulation is more suitable for the characterization of sand bodies and the study of reservoir heterogeneity.展开更多
With rapid economic and social development, soil contamination arising from heavy metals has become a serious problem in many parts of China. We collected a total of 445 samples （0-20 cm） at the nodes of a 2 kmx2 km...With rapid economic and social development, soil contamination arising from heavy metals has become a serious problem in many parts of China. We collected a total of 445 samples （0-20 cm） at the nodes of a 2 kmx2 km grid in surface soils of Rizhao city, and analyzed sources and risk pattern of 10 heavy metals （As, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb and Zn）. The combination of Multivariate statistics analysis and Geostatistical methods was applied to identify the sources and hazardous risk of heavy metals in soils. The result indicated that Cr, Ni, Co, Mn, Cu, and As were mainly controlled by parent materials and came from natural sources. Cd and Hg originated from anthropogenic sources. Pb and Zn, belonging to different groups in multivariate analysis, were associated with joint effect of parent materials and human inputs. Ordinary Kriging and Indicator Kriging suggested that single element and elements association from the same principal components had similar spatial distribution. Through comprehensive assessment on all elements, we also found the high risk areas were located in the populated urban areas and western study area, which could be attributed to the higher geological background in the western part and strong human interference in the eastern part.展开更多
Arsenic poses a danger to public health and drinking arsenic-rich groundwater is the main route for human exposure to this element.The focus of this study is to assess the risk magnitude and likelihood for arsenic-ric...Arsenic poses a danger to public health and drinking arsenic-rich groundwater is the main route for human exposure to this element.The focus of this study is to assess the risk magnitude and likelihood for arsenic-rich groundwater in Huaihe River Plain,China using Risk Magnitude and Indicator Kriging methods.It has been found that 481 in 5515 investigated samples exceed the drinking water standards of the World Health Organization,which present a high exposure risk for public health.Arsenic concentrations range from 0.001 to 356.00 mg/L,with a median of 2.10 mg/L.The proportion of contaminated shallow groundwater samples is 9.77%,and the counterpart from deep layer is 2.85%,respectively.Arsenic concentrations are obviously higher in plain areas than those in hilly areas.High Risk Magnitude and Very High Risk Magnitude samples are sporadically positioned in inland and coastal plain.According to the prediction of Risk Probability maps over shallow and deep groundwater,high arsenic Risk Probability areas is scattered in the inland and coastal portion,and both Risk Probability peaks are similar.Some high arsenic hazard areas have been found to possess high cancer rates,and high Risk Probability peaks are correlated with cancer cluster.The potential high arsenic hazard areas over shallow groundwater encompass more than 4709 km^(2),while the counterpart over deep groundwater is 1446 km2.2.88 million people are estimated to be potentially exposed to High Risk Probability of arsenic.This paper carried out research on exposure risk of arsenic contamination from Huaihe River Plain,China,which may provide guidance for regionalization of drinking groundwater safety.展开更多
There are various occasions where simple, ordinary, and universal kriging techniques may find themselves incapable of performing spatial prediction directly or efficiently. One type of application concerns quantificat...There are various occasions where simple, ordinary, and universal kriging techniques may find themselves incapable of performing spatial prediction directly or efficiently. One type of application concerns quantification of cumulative distribution function (CDF) or probability of occurrences of categorical variables over space. The other is related to optimal use of co-variation inherent to multiple regionalized variables as well as spatial correlation in spatial prediction. This paper extends geostatistics from the realm of kriging with uni-variate and continuous regionalized variables to the territory of indicator and multivariate kriging, where it is of ultimate importance to perform non-parametric estimation of probability distributions and spatial prediction based on co-regionalization and multiple data sources, respectively.展开更多
基金supported by Natural Science Fund for colleges and universities in Jiangsu Province(No. 07KJD170012)Natural Science Fund of Huaihai Institute of Technology (No. Z2008009)
文摘Coastal sediment type map has been widely used in marine economic and engineering activities, but the traditional mapping methods had some limitations due to their intrinsic assumption or subjectivity. In this paper, a non-parametric indicator Kriging method has been proposed for generating coastal sediment map. The method can effectively avoid mapping subjectivity, has no special requirements for the sample data to meet second-order stationary or normal distribution, and can also provide useful information on the quantitative evaluation of mapping uncertainty. The application of the method in the southern sea area of Lianyungang showed that much more convincing mapping results could be obtained compared with the traditional methods such as IDW, Kriging and Voronoi diagram under the same condition, so the proposed method was applicable with great utilization value.
基金supported by Iron Ore Research Center of Yazd University
文摘Uncertainty on the geological contacts and the block volumes of the models along boundaries is often a major part of the global uncertainty of reserve estimation.This work introduces a geostatistical technique that has been developed and tested in an iron ore deposit at Bafq mining district,in central Iran,and that,based on a probability criterion,helps to objectively model the geometry of this iron ore deposit.The main problem in reserve estimation of this ore body is its geometrical modeling and uncertainty in geological boundaries.This work deals with the geostatistical method of multiple indicator kriging,which is used to determine the real boundaries of ore body in different categories.This approach has potential to improve project performance and decrease operational risk.For this purpose,the ore body is separated into two categories including rich iron zone(w(Fe)>45%)and poor iron zone(20%<w(Fe)<45%).It significantly benefits to decrease the risk of reserve evaluation in the deposit.This case study also highlights the value of multiple indicator kriging as a tool for estimates the position of grade boundaries within the deposit.Comparison of the resultant probability maps with the real ore/waste contacts on the extracted levels shows that the first indicator model could separate the whole ore body(poor plus rich)from the waste zone by probability of more than 0.35,which concludes the total reserve of 53 million tons.The second indicator model applied to separate the rich and poor domains and the results show that the blocks with the estimated probability of equal to or more than 0.4 lay within the rich ore zone consisting of 15.8 million tons reserve.
文摘Sequential indicator simulation is a commonly used method for discrete variable simulation in 3D geological modeling and a widely used stochastic simulation method, which can be used not only for continuous variable simulation but also for discrete variable simulation. In this paper, the X Oilfield in the western South China Sea is taken as an example to compare the sequential indicator simulation method and the Indicator Kriging interpolation method. The results of the final comparison show that the results of the lithofacies model established by the Indicator Kriging deterministic interpolation method are overly smooth, and its coincidence rate with the geological statistical results is not high, thus cannot well reflect the heterogeneity of the underground reservoir, while the simulation results of the lithofacies model established by the sequential indicator stochastic simulation method can fit well with the statistical law of the well, which has eliminated the smoothing effect of Kriging interpolation, thus can better reflect the heterogeneity of the underground reservoir. Therefore, the sequential indicator simulation is more suitable for the characterization of sand bodies and the study of reservoir heterogeneity.
基金China State-Sponsored Postgraduate Study Abroad Program,No.201306190053National Natural Science Foundation of China,No.41101079+1 种基金The Program B for Outstanding PhD Candidate of Nanjing University,No.2014001B008The Program for Graduate Student’s Research Innovation of Jiangsu Province,No.CXLX13-051
文摘With rapid economic and social development, soil contamination arising from heavy metals has become a serious problem in many parts of China. We collected a total of 445 samples （0-20 cm） at the nodes of a 2 kmx2 km grid in surface soils of Rizhao city, and analyzed sources and risk pattern of 10 heavy metals （As, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb and Zn）. The combination of Multivariate statistics analysis and Geostatistical methods was applied to identify the sources and hazardous risk of heavy metals in soils. The result indicated that Cr, Ni, Co, Mn, Cu, and As were mainly controlled by parent materials and came from natural sources. Cd and Hg originated from anthropogenic sources. Pb and Zn, belonging to different groups in multivariate analysis, were associated with joint effect of parent materials and human inputs. Ordinary Kriging and Indicator Kriging suggested that single element and elements association from the same principal components had similar spatial distribution. Through comprehensive assessment on all elements, we also found the high risk areas were located in the populated urban areas and western study area, which could be attributed to the higher geological background in the western part and strong human interference in the eastern part.
基金the Science and Technology Programme of the Jiangsu Province(BK20151093)the Chinese Project of the National Geological Survey(DD20221756).
文摘Arsenic poses a danger to public health and drinking arsenic-rich groundwater is the main route for human exposure to this element.The focus of this study is to assess the risk magnitude and likelihood for arsenic-rich groundwater in Huaihe River Plain,China using Risk Magnitude and Indicator Kriging methods.It has been found that 481 in 5515 investigated samples exceed the drinking water standards of the World Health Organization,which present a high exposure risk for public health.Arsenic concentrations range from 0.001 to 356.00 mg/L,with a median of 2.10 mg/L.The proportion of contaminated shallow groundwater samples is 9.77%,and the counterpart from deep layer is 2.85%,respectively.Arsenic concentrations are obviously higher in plain areas than those in hilly areas.High Risk Magnitude and Very High Risk Magnitude samples are sporadically positioned in inland and coastal plain.According to the prediction of Risk Probability maps over shallow and deep groundwater,high arsenic Risk Probability areas is scattered in the inland and coastal portion,and both Risk Probability peaks are similar.Some high arsenic hazard areas have been found to possess high cancer rates,and high Risk Probability peaks are correlated with cancer cluster.The potential high arsenic hazard areas over shallow groundwater encompass more than 4709 km^(2),while the counterpart over deep groundwater is 1446 km2.2.88 million people are estimated to be potentially exposed to High Risk Probability of arsenic.This paper carried out research on exposure risk of arsenic contamination from Huaihe River Plain,China,which may provide guidance for regionalization of drinking groundwater safety.
基金Supported by the National 973 Program of China (No. 2007CB714402-5)
文摘There are various occasions where simple, ordinary, and universal kriging techniques may find themselves incapable of performing spatial prediction directly or efficiently. One type of application concerns quantification of cumulative distribution function (CDF) or probability of occurrences of categorical variables over space. The other is related to optimal use of co-variation inherent to multiple regionalized variables as well as spatial correlation in spatial prediction. This paper extends geostatistics from the realm of kriging with uni-variate and continuous regionalized variables to the territory of indicator and multivariate kriging, where it is of ultimate importance to perform non-parametric estimation of probability distributions and spatial prediction based on co-regionalization and multiple data sources, respectively.
