Araştırma Makalesi
BibTex RIS Kaynak Göster

Use of Moran’s I and robust statistics to separate geochemical anomalies in Jiurui area (Southeast China)

Yıl 2018, Cilt: 156 Sayı: 156, 179 - 192, 27.06.2018
https://doi.org/10.19111/bulletinofmre.351376

Öz

Separation of geochemical anomalies from background plays an important
role in the study of exploration geochemistry. The limitations of commonly used
methods are not taken into account spatial correlation, variability and the
unsatisfactory of the statistical assumption of the normality of geochemical
data. For solving these limitations, an indirect method for the separation of
geochemical anomalies is proposed based on anomaly separation of local Moran’s
Ii values using robust statistics in this study. The experiment was carried out
using 1481 samples collected from Jiurui copper prospect (southeast China). The
steps for the anomaly separation are (i) spatial association and variability
were fi rst analyzied by means of Moran scatterplots at six spatial scales (2,
4, 6, 8, 10 and 12 km) using both raw data and Box-Cox transformed data; (ii)
local Moran’s Ii was used to measure spatial autocorrelationat these six local
scales; (iii) anomalous separation was fi nally performed using the MEDIAN ±
1.5*IQR (IQR: interquartile range) rule on local Moran’s Ii values. The results
show that geochemical anomalies are mostly concentrated around known
ore-deposits, according the objective reality and a strong correlation with
known ore-deposits in Jiurui copper prospect.

Kaynakça

  • Afzal, P., Khakzad, A., Moarefvand, P., Rashidnejad Omran, N., Esfandiari, B., Fadakar Alghalandis, Y., 2010. Geochemical anomaly separation by multifractal modeling in Kahang (Gor Gor) porphyry system, Central Iran, Journal of Geochemical Exploration, 104, 34-46.
  • Afzal, P., Harati, H., Fadakar Alghalandis, Y., Yasrebi, A.B., 2013. Application of spectrum–area fractal model to identify of geochemical anomalies based on soil data in Kahang porphyry-type Cu deposit, Iran. Chemie der Erde/Geochemistry, 73, 533-543.
  • Ahrens, L. 1953. A fundamental law of geochemistry. Nature, 172,1148.
  • Ahrens, L. 1954a. The lognormal distribution of the elements (a fundamental law of geochemistry and its subsidiary). Geochim Cosmochi Acta, 5,49-74.
  • Ahrens, L. 1954b. The lognormal distribution of the elements II. Geochim Cosmochi Acta, 6,121-132.
  • Ahrens, L. 1957. Lognormal-type distribution III. Geochim Cosmochi Acta, 11,205-213.
  • Anselin L. 1992. Spacestat Tutorial: A workbook for using SpaceStat in the analysis of spatial data. Urbana: University of Illinois.
  • Anselin, L. 1995. Local indicators of spatial association-LISA. Geographical Analysis, 27, 2, 93-115.
  • Anselin, L. 2005. Exploring spatial data with GeoDa™: A workbook. Urbana: Spatial Analysis Laboratory and Center for Spatially Integrated Social Science, Department of Agriculture and Consumer Economics, University of Illinois, Urbana-Champaign.
  • Bailey T. C., Gatrell A. C. 1995. Interactive Spatial Data Analysis. Harlow: Addison Wesley Longman, 413p.
  • Barnett, V., Lewis T. 1994. Outliers in statistical data. New York: Wiley & Sons, 582p.
  • Bolviken, B., Stokke, P.R., Feder, J.,Jossang, T. 1992. Journal of Geochemical Exploration, 43, 91-109.
  • Brody, S.D., Highfield, W.E., Thornton, S. 2006. Planning at the urban fringe: an examination of the factors influencing nonconforming development patterns in southern Florida. Environment and Planning B Planning and Desig, 2006, 33,1, 75-96.
  • Cheng, Q., Agterberg, F.P. 1996. Mathematical Geology, 28, 1-16.
  • Cocu N., Harrington R., Hulle M., Rounsevell, M.D.A. 2005. Spatial autocorrelation as a tool for identifying the geographical patterns of aphid annual abundance. Agricultural and Forest Entomology, 2005, 7, 1, 31-43.
  • Cliff, A.D., Ord, J.K. 1981. Spatial Processes, Models and Applications. London – Pion, 266p.
  • Davis, J.C., 2002. Statistics and data analysis in Geology (3th ed.). John Wiley & Sons Inc., New York, 656p.
  • Dutter, R., Filzmoser, P., Gather, U., Rousseeuw, P. 2003. Developments in Robust Statistics. International Conference on Robust Statistics, Vorai, Austria, 23-27 July, 2001. Physica Verlag, Heidelberg.
  • Garrett, R. 1989. A cry from the heart. Explore, 66, 18-20
  • Geary, R.C. 1954. The Contiguity Ratio and Statistical Mapping. The Incorporated Statistician, 5, 3, 115-145.
  • Getis, A., Ord, J.K. 1992. The analysis of spatial association by use of distance statistics. Geographical Analysis, 24, 3, 189-206.
  • Getis, A., Ord, J.K. 1996. Local spatial statistics: an overview. In Longley P, Batty M (eds) Spatial analysis: modelling in a GIS environment. Cambridge (UK), GeoInformation International, 269–285.
  • Haining R. 1990. Spatial Data Analysis in the Social and Environmental Sciences. Cambridge: Cambridge University Press, 432p.
  • Hampel, F.R., Ronchetti, E.M., Rousseeuw, P.J. 1986. Robust Statistics: The Approach Based on Influence Functions. New York: Wiley.
  • Hashemi, M., Afzal, P., Ras, I. Abedini, M.V. 2010. Geochemical anomaly separation by Concentration-Area fractal model in Bardaskan area, NE Iran. Journal of Mining and Metallurgy, 46A, 1-10.
  • Hawkes, H.E., Webb, J.S. 1962. Geochemistry in mineral exploration. New York: Harper.
  • Hoang, A.H., Vu, D.T., Nguyen, T.T. 2017. Spatial Variability Analysis of Cu Content: A Case Study in Jiurui Copper Mining Area. International Journal of Applied Geospatial Research, 8,1, 81-93.
  • Huber, P.J. 1981. Robust statistics. New York: Wiley & Sons, 1981.
  • Ishioka, F., Kurihara, K., Suito, H., Horikawa, Y., Ono, Y. 2007. Detection of hotspots for three-dimensional spatial data and its application to environmental pollution data. Journal of Environmental Science for Sustainable Society, 1, 15-24.
  • Legendre, P., Gauthier, O. 2014. Statistical methods for temporal and space –time analysis of community composition data. Proc. R. Soc. B, 281, 20132728. http://dx.doi.org/10.1098/rspb.2013.2728.
  • Levine, N. 2004. CrimeStat III: A spatial statistics program for the analysis of crime incident locations. Ned Levine & Assocaitions, Houston, TX, and the National Institute of Justice, Washington, DC.
  • Li, Ch., Ma, T., Shi, J.. 2003. Journal of Geochemical Exploration, 77, 167-175.
  • Liu, Y., Cheng, Q., Xia, Q., Wang, X. 2013. Application of singularity analysis for mineral potential identification using geochemical data - A case study: Nanling W–Sn–Mo polymetallic metallogenic belt, South China. Journal of Geochemical Exploration, 134, 61-72.
  • Liu, Y., Zhou, K., Cheng, Q. 2017. A new method for geochemical anomaly separation based on the distribution patterns of singularity indices. Computers & Geosciences, 105, 139-147.
  • Martins-Melo, F.R., Ramos, A.N., Alencar, C.H., Heukelbach, J. 2016. Trends and spatial patterns of mortality related to neglected tropical diseases in Brazil. Parasite Epidemiology and Control, 1, 2, 56-65.
  • Martins-Melo, F.R. Ramos, A.N., Alencar, C.H, Lima, M.S. 2017. Epidemiology of soil-transmitted helminthiases-related mortality in Brazil. Parasitology, 144, 5, 669-679.
  • McGrath, S.P., Loveland, P.J. 1992. The Soil Geochemical Atlas of England and Wales. Blackie Academic and Professional, Glasgow, 101p.
  • Momeni, S., Shahrokhi, S.V., Afzal, P., Sadeghi, B., Farhadinejad, T., Nikzad, M.R., 2016. Delineation of the Cr mineralization based on the stream sediment data utilizing fractal modeling and factor analysis in the Khoy 1:100,000 sheet, NW Iran. Bulletin of the Mineral Research and Exploration, 152, 1-17.
  • Nazarpour, A., Omran, N.R., Paydar, G.R., Sadeghi, B., Matroud, F., Nejad, A.M. 2015. Application of classical statistics, logratio transformation and multifractal approaches to delineate geochemical anomalies in the Zarshuran gold district, NW Iran. Chemie der Erde-Geochemistry, 75,1, 117-132.
  • Nazarpour, A., Paydar, G.R., Carranza, E.J.M. 2016. Stepwise regression for recognition of geochemical anomalies: Case study in Takab area, NW Iran. Journal of Geochemical Exploration, 168, 150-162.
  • Nguyen, T. T., Liu, X. G., Ren, Z. 2014. A Study Of Geochemical Exploration Spatial Cluster Identificaton Based On Local Spatial Autocorrelation. Geophysical and Geochemical Exploration, 38, 2, 370-376.
  • Nguyen, T.T, Vu, D.T., Trinh, L.H., Nguyen, T.L.H. 2016. Spatial Cluster and Outlier Identification of Geochemical Association of Elements: A Case Study in Juirui Copper Mining Area. Bull. Min. Res. Exp., 153, 159-167.
  • Philip, G.M., Watson, D.F. 1987. Probabilism in geological data analysis. Geological Magazine, 124, 6, 577-583.
  • Rajabzadeh, M.A., Yazadni, S., Nazarpour, A., Ahmadi, A. 2015. Application Of Fractal Concentration-Area Method In Identification Of Geochemical Anomalies In Stream Sediments From Mazayejan Area, Suorian 1: 100000 Sheet, Fars Province.. Geochemistry, 4, 1, 15-20.
  • Reimann, C., Filzmoser, P. 2000. Normal and lognormal data distribution in geochemistry: death of a myth. Consequences for the statistical treatment of geochemical and environmental data. Environmental geology, 39, 9, 1001-1014.
  • Reimann, C., Filzmoser P., Garrett, R.G.2005. Background and threshold: critical comparison of methods of determination. Science of the Total Environment, 346, 1-3,1-16.
  • Rock, N., 1988. Numerical geology. New York Berlin Heidelberg: Springger Verlag, 427p.
  • Rousseeuw, P. J., Leroy, A. M. 1987. Robust regression and outlier detection. John Wiley and Sons - New York, 329p.
  • Ruiz-Rivera, N., Paul V.L. 2016. Urban segregation in Latin America. Habitat International, 54, 1-2.
  • Sinclair, A.J. 1974. Selection of threshold in geochemical data using probability graphs. Journal of Geochemical Exploration, 3,129-149.
  • Sinclair, A.J. 1976. Application of probability graphs in mineral exploration. Association of Applied Geochemists, 4, 95.
  • Sinclair, A.J. 1991. A fundamental approach to threshold estimation in exploration geochemistry. Probability plots revisited. Journal of Geochemical Exploration, 41,1-22.
  • Spatial Analysis Laboratory. 2007. GeoDa: an introduction to spatial data analyses. Spatial Analysis Laboratory Department of Geography. Urbana: University of Illinois.
  • Stanley, C.R., Sinclair, A.J. 1989. Comparison of probability plots and gap statistics in the selection of threshold for exploration geochemistry data. Journal of Geochemical Exploration, 32, 355-357.
  • Tango, T. 1995. A class of test for detecting ‘general’ and ‘focused’ clustering of rare diseases. Statistics in Medicine, 14, 21-22, 2323-2334.
  • Tobler, W.R. 1979. Smooth Pychnophylactic Interpolation for Geographical Regions. Journal of the American Statistical Association, 74, 367, 519-530.
  • Tukey, J.W. 1977. Exploratory Data Analysis. Reading: Addison-Wesley, 1977. Waller, L., Gotway, C.A. 2004. Applied Spatial Statistics for Public Health Data. John Wiley and Sons - New Jersey, 520p.
  • Wang, F., Hu, Y., Wang, S., Li, X. 2016. Local indicator of colocation quotient with a statistical significance test: examining spatial association of crime and facilities. The Professional Geographer, 69, 22-31.
  • Zhang, T.L., Lin G. 2006. A supplemental indicator of high-value or low-value spatial clustering. Geographical Analysis, 38, 2, 209-225.
  • Zhang, C.S., McGrath, D. 2004. Geostatistical and GIS analyses on soil organic carbon concentrations in grassland of southeastern Ireland from two different periods. Geoderma, 119, 3-4, 261-275.
Yıl 2018, Cilt: 156 Sayı: 156, 179 - 192, 27.06.2018
https://doi.org/10.19111/bulletinofmre.351376

Öz


Kaynakça

  • Afzal, P., Khakzad, A., Moarefvand, P., Rashidnejad Omran, N., Esfandiari, B., Fadakar Alghalandis, Y., 2010. Geochemical anomaly separation by multifractal modeling in Kahang (Gor Gor) porphyry system, Central Iran, Journal of Geochemical Exploration, 104, 34-46.
  • Afzal, P., Harati, H., Fadakar Alghalandis, Y., Yasrebi, A.B., 2013. Application of spectrum–area fractal model to identify of geochemical anomalies based on soil data in Kahang porphyry-type Cu deposit, Iran. Chemie der Erde/Geochemistry, 73, 533-543.
  • Ahrens, L. 1953. A fundamental law of geochemistry. Nature, 172,1148.
  • Ahrens, L. 1954a. The lognormal distribution of the elements (a fundamental law of geochemistry and its subsidiary). Geochim Cosmochi Acta, 5,49-74.
  • Ahrens, L. 1954b. The lognormal distribution of the elements II. Geochim Cosmochi Acta, 6,121-132.
  • Ahrens, L. 1957. Lognormal-type distribution III. Geochim Cosmochi Acta, 11,205-213.
  • Anselin L. 1992. Spacestat Tutorial: A workbook for using SpaceStat in the analysis of spatial data. Urbana: University of Illinois.
  • Anselin, L. 1995. Local indicators of spatial association-LISA. Geographical Analysis, 27, 2, 93-115.
  • Anselin, L. 2005. Exploring spatial data with GeoDa™: A workbook. Urbana: Spatial Analysis Laboratory and Center for Spatially Integrated Social Science, Department of Agriculture and Consumer Economics, University of Illinois, Urbana-Champaign.
  • Bailey T. C., Gatrell A. C. 1995. Interactive Spatial Data Analysis. Harlow: Addison Wesley Longman, 413p.
  • Barnett, V., Lewis T. 1994. Outliers in statistical data. New York: Wiley & Sons, 582p.
  • Bolviken, B., Stokke, P.R., Feder, J.,Jossang, T. 1992. Journal of Geochemical Exploration, 43, 91-109.
  • Brody, S.D., Highfield, W.E., Thornton, S. 2006. Planning at the urban fringe: an examination of the factors influencing nonconforming development patterns in southern Florida. Environment and Planning B Planning and Desig, 2006, 33,1, 75-96.
  • Cheng, Q., Agterberg, F.P. 1996. Mathematical Geology, 28, 1-16.
  • Cocu N., Harrington R., Hulle M., Rounsevell, M.D.A. 2005. Spatial autocorrelation as a tool for identifying the geographical patterns of aphid annual abundance. Agricultural and Forest Entomology, 2005, 7, 1, 31-43.
  • Cliff, A.D., Ord, J.K. 1981. Spatial Processes, Models and Applications. London – Pion, 266p.
  • Davis, J.C., 2002. Statistics and data analysis in Geology (3th ed.). John Wiley & Sons Inc., New York, 656p.
  • Dutter, R., Filzmoser, P., Gather, U., Rousseeuw, P. 2003. Developments in Robust Statistics. International Conference on Robust Statistics, Vorai, Austria, 23-27 July, 2001. Physica Verlag, Heidelberg.
  • Garrett, R. 1989. A cry from the heart. Explore, 66, 18-20
  • Geary, R.C. 1954. The Contiguity Ratio and Statistical Mapping. The Incorporated Statistician, 5, 3, 115-145.
  • Getis, A., Ord, J.K. 1992. The analysis of spatial association by use of distance statistics. Geographical Analysis, 24, 3, 189-206.
  • Getis, A., Ord, J.K. 1996. Local spatial statistics: an overview. In Longley P, Batty M (eds) Spatial analysis: modelling in a GIS environment. Cambridge (UK), GeoInformation International, 269–285.
  • Haining R. 1990. Spatial Data Analysis in the Social and Environmental Sciences. Cambridge: Cambridge University Press, 432p.
  • Hampel, F.R., Ronchetti, E.M., Rousseeuw, P.J. 1986. Robust Statistics: The Approach Based on Influence Functions. New York: Wiley.
  • Hashemi, M., Afzal, P., Ras, I. Abedini, M.V. 2010. Geochemical anomaly separation by Concentration-Area fractal model in Bardaskan area, NE Iran. Journal of Mining and Metallurgy, 46A, 1-10.
  • Hawkes, H.E., Webb, J.S. 1962. Geochemistry in mineral exploration. New York: Harper.
  • Hoang, A.H., Vu, D.T., Nguyen, T.T. 2017. Spatial Variability Analysis of Cu Content: A Case Study in Jiurui Copper Mining Area. International Journal of Applied Geospatial Research, 8,1, 81-93.
  • Huber, P.J. 1981. Robust statistics. New York: Wiley & Sons, 1981.
  • Ishioka, F., Kurihara, K., Suito, H., Horikawa, Y., Ono, Y. 2007. Detection of hotspots for three-dimensional spatial data and its application to environmental pollution data. Journal of Environmental Science for Sustainable Society, 1, 15-24.
  • Legendre, P., Gauthier, O. 2014. Statistical methods for temporal and space –time analysis of community composition data. Proc. R. Soc. B, 281, 20132728. http://dx.doi.org/10.1098/rspb.2013.2728.
  • Levine, N. 2004. CrimeStat III: A spatial statistics program for the analysis of crime incident locations. Ned Levine & Assocaitions, Houston, TX, and the National Institute of Justice, Washington, DC.
  • Li, Ch., Ma, T., Shi, J.. 2003. Journal of Geochemical Exploration, 77, 167-175.
  • Liu, Y., Cheng, Q., Xia, Q., Wang, X. 2013. Application of singularity analysis for mineral potential identification using geochemical data - A case study: Nanling W–Sn–Mo polymetallic metallogenic belt, South China. Journal of Geochemical Exploration, 134, 61-72.
  • Liu, Y., Zhou, K., Cheng, Q. 2017. A new method for geochemical anomaly separation based on the distribution patterns of singularity indices. Computers & Geosciences, 105, 139-147.
  • Martins-Melo, F.R., Ramos, A.N., Alencar, C.H., Heukelbach, J. 2016. Trends and spatial patterns of mortality related to neglected tropical diseases in Brazil. Parasite Epidemiology and Control, 1, 2, 56-65.
  • Martins-Melo, F.R. Ramos, A.N., Alencar, C.H, Lima, M.S. 2017. Epidemiology of soil-transmitted helminthiases-related mortality in Brazil. Parasitology, 144, 5, 669-679.
  • McGrath, S.P., Loveland, P.J. 1992. The Soil Geochemical Atlas of England and Wales. Blackie Academic and Professional, Glasgow, 101p.
  • Momeni, S., Shahrokhi, S.V., Afzal, P., Sadeghi, B., Farhadinejad, T., Nikzad, M.R., 2016. Delineation of the Cr mineralization based on the stream sediment data utilizing fractal modeling and factor analysis in the Khoy 1:100,000 sheet, NW Iran. Bulletin of the Mineral Research and Exploration, 152, 1-17.
  • Nazarpour, A., Omran, N.R., Paydar, G.R., Sadeghi, B., Matroud, F., Nejad, A.M. 2015. Application of classical statistics, logratio transformation and multifractal approaches to delineate geochemical anomalies in the Zarshuran gold district, NW Iran. Chemie der Erde-Geochemistry, 75,1, 117-132.
  • Nazarpour, A., Paydar, G.R., Carranza, E.J.M. 2016. Stepwise regression for recognition of geochemical anomalies: Case study in Takab area, NW Iran. Journal of Geochemical Exploration, 168, 150-162.
  • Nguyen, T. T., Liu, X. G., Ren, Z. 2014. A Study Of Geochemical Exploration Spatial Cluster Identificaton Based On Local Spatial Autocorrelation. Geophysical and Geochemical Exploration, 38, 2, 370-376.
  • Nguyen, T.T, Vu, D.T., Trinh, L.H., Nguyen, T.L.H. 2016. Spatial Cluster and Outlier Identification of Geochemical Association of Elements: A Case Study in Juirui Copper Mining Area. Bull. Min. Res. Exp., 153, 159-167.
  • Philip, G.M., Watson, D.F. 1987. Probabilism in geological data analysis. Geological Magazine, 124, 6, 577-583.
  • Rajabzadeh, M.A., Yazadni, S., Nazarpour, A., Ahmadi, A. 2015. Application Of Fractal Concentration-Area Method In Identification Of Geochemical Anomalies In Stream Sediments From Mazayejan Area, Suorian 1: 100000 Sheet, Fars Province.. Geochemistry, 4, 1, 15-20.
  • Reimann, C., Filzmoser, P. 2000. Normal and lognormal data distribution in geochemistry: death of a myth. Consequences for the statistical treatment of geochemical and environmental data. Environmental geology, 39, 9, 1001-1014.
  • Reimann, C., Filzmoser P., Garrett, R.G.2005. Background and threshold: critical comparison of methods of determination. Science of the Total Environment, 346, 1-3,1-16.
  • Rock, N., 1988. Numerical geology. New York Berlin Heidelberg: Springger Verlag, 427p.
  • Rousseeuw, P. J., Leroy, A. M. 1987. Robust regression and outlier detection. John Wiley and Sons - New York, 329p.
  • Ruiz-Rivera, N., Paul V.L. 2016. Urban segregation in Latin America. Habitat International, 54, 1-2.
  • Sinclair, A.J. 1974. Selection of threshold in geochemical data using probability graphs. Journal of Geochemical Exploration, 3,129-149.
  • Sinclair, A.J. 1976. Application of probability graphs in mineral exploration. Association of Applied Geochemists, 4, 95.
  • Sinclair, A.J. 1991. A fundamental approach to threshold estimation in exploration geochemistry. Probability plots revisited. Journal of Geochemical Exploration, 41,1-22.
  • Spatial Analysis Laboratory. 2007. GeoDa: an introduction to spatial data analyses. Spatial Analysis Laboratory Department of Geography. Urbana: University of Illinois.
  • Stanley, C.R., Sinclair, A.J. 1989. Comparison of probability plots and gap statistics in the selection of threshold for exploration geochemistry data. Journal of Geochemical Exploration, 32, 355-357.
  • Tango, T. 1995. A class of test for detecting ‘general’ and ‘focused’ clustering of rare diseases. Statistics in Medicine, 14, 21-22, 2323-2334.
  • Tobler, W.R. 1979. Smooth Pychnophylactic Interpolation for Geographical Regions. Journal of the American Statistical Association, 74, 367, 519-530.
  • Tukey, J.W. 1977. Exploratory Data Analysis. Reading: Addison-Wesley, 1977. Waller, L., Gotway, C.A. 2004. Applied Spatial Statistics for Public Health Data. John Wiley and Sons - New Jersey, 520p.
  • Wang, F., Hu, Y., Wang, S., Li, X. 2016. Local indicator of colocation quotient with a statistical significance test: examining spatial association of crime and facilities. The Professional Geographer, 69, 22-31.
  • Zhang, T.L., Lin G. 2006. A supplemental indicator of high-value or low-value spatial clustering. Geographical Analysis, 38, 2, 209-225.
  • Zhang, C.S., McGrath, D. 2004. Geostatistical and GIS analyses on soil organic carbon concentrations in grassland of southeastern Ireland from two different periods. Geoderma, 119, 3-4, 261-275.
Toplam 60 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Thanh Nguyen Tien Bu kişi benim

Yayımlanma Tarihi 27 Haziran 2018
Yayımlandığı Sayı Yıl 2018 Cilt: 156 Sayı: 156

Kaynak Göster

APA Nguyen Tien, T. (2018). Use of Moran’s I and robust statistics to separate geochemical anomalies in Jiurui area (Southeast China). Bulletin of the Mineral Research and Exploration, 156(156), 179-192. https://doi.org/10.19111/bulletinofmre.351376
AMA Nguyen Tien T. Use of Moran’s I and robust statistics to separate geochemical anomalies in Jiurui area (Southeast China). Bull.Min.Res.Exp. Haziran 2018;156(156):179-192. doi:10.19111/bulletinofmre.351376
Chicago Nguyen Tien, Thanh. “Use of Moran’s I and Robust Statistics to Separate Geochemical Anomalies in Jiurui Area (Southeast China)”. Bulletin of the Mineral Research and Exploration 156, sy. 156 (Haziran 2018): 179-92. https://doi.org/10.19111/bulletinofmre.351376.
EndNote Nguyen Tien T (01 Haziran 2018) Use of Moran’s I and robust statistics to separate geochemical anomalies in Jiurui area (Southeast China). Bulletin of the Mineral Research and Exploration 156 156 179–192.
IEEE T. Nguyen Tien, “Use of Moran’s I and robust statistics to separate geochemical anomalies in Jiurui area (Southeast China)”, Bull.Min.Res.Exp., c. 156, sy. 156, ss. 179–192, 2018, doi: 10.19111/bulletinofmre.351376.
ISNAD Nguyen Tien, Thanh. “Use of Moran’s I and Robust Statistics to Separate Geochemical Anomalies in Jiurui Area (Southeast China)”. Bulletin of the Mineral Research and Exploration 156/156 (Haziran 2018), 179-192. https://doi.org/10.19111/bulletinofmre.351376.
JAMA Nguyen Tien T. Use of Moran’s I and robust statistics to separate geochemical anomalies in Jiurui area (Southeast China). Bull.Min.Res.Exp. 2018;156:179–192.
MLA Nguyen Tien, Thanh. “Use of Moran’s I and Robust Statistics to Separate Geochemical Anomalies in Jiurui Area (Southeast China)”. Bulletin of the Mineral Research and Exploration, c. 156, sy. 156, 2018, ss. 179-92, doi:10.19111/bulletinofmre.351376.
Vancouver Nguyen Tien T. Use of Moran’s I and robust statistics to separate geochemical anomalies in Jiurui area (Southeast China). Bull.Min.Res.Exp. 2018;156(156):179-92.

Copyright and Licence
The Bulletin of Mineral Research and Exploration keeps the Law on Intellectual and Artistic Works No: 5846. The Bulletin of Mineral Research and Exploration publishes the articles under the terms of “Creatice Common Attribution-NonCommercial-NoDerivs (CC-BY-NC-ND 4.0)” licence which allows to others to download your works and share them with others as long as they credit you, but they can’t change them in any way or use them commercially.

For further details;
https://creativecommons.org/licenses/?lang=en