Scientific Quarterly Journal of Geosciences

Scientific Quarterly Journal of Geosciences

Using U-spatial statistics method to identify and classify intensity of geochemical anomalies in the Neysian area

Document Type : Original Research Paper

Authors
Amir Salimi 1
Samaneh Barak 2
Mahyar Yousefi 3
1 Department of Mining Engineering, Faculty of Engineering, University of Zanjan, Zanjan, Iran
2 Department of Mining Engineering, Faculty of Engineering, Urmia University, Urmia, Iran
3 Department of Mining Engineering, Faculty of Engineering, Malayer University, Malayer, Iran
10.22071/gsj.2023.409411.2104
Abstract
An accurate threshold value makes a precise geochemical separation into anomaly and background areas. The threshold assignment using structural methods are preferred to non-structural methods. In this research, the U-spatial statistics, a structural based method, was used to study soil type geochemical data of the Neysian region. The optimal U-values obtained by this method for each sample were successfully utilized to separate the abnormal and background samples, accurately. In addition, based on the optimal distance of each sample, the abnormal samples identified in the previous step were classified in terms of geochemical intensity into strong, medium, and weak samples. The goodness of U-spatial statistics performance in identifying abnormal areas were validated using drilled boreholes in the area. The U-spatial statistics not only succeeded in correctly identifying anomalous samples, but it also correctly identified some samples as the background whiles they had been recognized as anomaly by a non-structural method. All results obtained were validated by the several drilled boreholes.
Keywords
Geochemical dataset
Threshold
Structural method
U-spatial statistics

Subjects

Afshooni, S.Z., Asadi Harooni, H., and Esmaili, D., 2011. The microthermometry study of fluid inclusions in quartz veins of Kahang deposit (northeastern of Isfahan). In: 2nd National Symposium of Iranian Society of Economic Geology. Lorestan University, p. 144.
Afshooni, S.Z., Mirnejad, H., Esmaeily, D., and Haroni, H. A., 2013. Mineral chemistry of hydrothermal biotite from the Kahang porphyry copper deposit (NE Isfahan), Central Province of Iran. Ore Geol. Rev. 54, 214–232. https://doi.org/10.1016/j.oregeorev.2013.04.004.
Alavi, M., 1994. Tectonic of the Zagros orogenic belt of Iran, new data and interpretations. Tectonophysics, 229(3-4): 211-238. https://doi.org/10.1016/0040-1951(94)90030-2.
Aliyari, F., Afzal, P., Harati, H., and Zengqian, H., 2020a. Geology, mineralogy, ore fluid characteristics, and 40Ar/39Ar geochronology of the Kahang Cu-(Mo) porphyry deposit, Urumieh-Dokhtar Magmatic Arc, Central Iran. Ore Geology Reviews, 116, 103238. https://doi.org/10.1016/j.oregeorev.2019.103238.
Aliyari, F., Afzal, P., Lotfi, M., Shokri, S., and Feizi, H., 2020b. Delineation of geochemical haloes using the developed zonality index model by multivariate and fractal analysis in the Cu–Mo porphyry deposits. Applied Geochemistry, 121, 104694. https://doi.org/10.1016/j.apgeochem.2020.104694.
Amidi, S.M., 1975. Contribution a'letude stratigraphique, petrologique et petrographique des roches magmatiques de la region Natanz-Nain-Surk (Iran central). Ph.D. Thesis, University of Grenoble France, Grenoble, France, 316 pp.
Barak, S., Bahroudi, A., and Jozanikohan, G., 2018a. Exploration of Kahang porphyry copper deposit using advanced integration of geological, remote sensing, geochemical, and magnetics data. Journal of Mining and Environment, 9(1), 19-39. https://doi.org/10.22044/jme.2017.5419.1357
Barak, S., Bahroudi, A., and Jozanikohan, G., 2018b. The use of fuzzy inference system in the integration of copper exploration layers in Neysian. Journal of Mining Engineering, 13(38), 97-112. (In Persian).
Barak, S., Bahroudi, A., Aslani, S., and Mohebi, A., 2016. The Geochemical Anomaly Separation by using the Soil Samples of Eastern of Neysian, Isfahan Province, Geochemistry, 5(1), 55-71. (In Persian).
Barak, S., Imamalipour, A., Abedi, M., Bahroudi, A., and Khalifani, F.M., 2021. Comprehensive modeling of mineral potential mapping by integration of multiset geosciences data. Geochemistry, 81(4), 125824. https://doi.org/10.1016/j.chemer.2021.125824
Barak, S., Imamalipour, A., and Abedi, M., 2023. Application of Fuzzy Gamma Operator for Mineral Prospectivity Mapping, Case Study: Sonajil Area. Journal of Mining and Environment, 14(3), 981-997. https://doi.org/10.22044/jme.2023.12954.2352.
Carranza, E.J.M., 2008. Geochemical anomaly and mineral prospectivity mapping in GIS. Elsevier.
Cheng, Q., Agterberg, F.P., and Bonham-Carter, G.F., 1996. A spatial analysis method for geochemical anomaly separation. Journal of Geochemical Exploration, 56(3), 183–195. https://doi.org/10.1016/S0375-6742(96)00035-0.
Darabi-Golestan, F., Ghavami-Riabi, R., Khalokakaie, R., Asadi-Haroni, H., and Seyedrahimi-Nyaragh, M. 2013. Interpretation of lithogeochemical and geophysical data to identify the buried mineralized area in Cu-Au porphyry of Dalli-Northern Hill. Arabian Journal of Geosciences, 6(11), 4499-4509. https://doi.org/10.1007/s12517-012-0686-3.
Farhadi, S., Afzal, P., Boveiri Konari, M., Daneshvar Saein, L., and Sadeghi, B., 2022. Combination of Machine Learning Algorithms with Concentration-Area Fractal Method for Soil Geochemical Anomaly Detection in Sediment-Hosted Irankuh Pb-Zn Deposit, Central Iran. Minerals 12 (6), 689. https://doi.org/10.3390/min12060689.
Forster, H., 1974. Continental drift in Iran in relation to the Afar structures. International Symposium on the Afar Region and Related Rift Problems, Bad Bergzabern, Germany.
Ghasemzadeh, S., Maghsoudi, A., Yousefi, M., and Mihalasky, M.J., 2019. Stream sediment geochemical data analysis for district-scale mineral exploration targeting: Measuring the performance of the spatial U-statistic and CA fractal modeling. Ore Geology Reviews, 113, 103115. https://doi.org/10.1016/j.oregeorev.2019.103115.
Ghavami-Riabi, R., Seyedrahimi-Niaraq, M. M., Khalokakaie, R., and Hazareh, M. R., 2010. U-spatial statistic data modeled on a probability diagram for investigation of mineralization phases and exploration of shear zone gold deposits. Journal of Geochemical exploration, 104(1-2), 27-33.
Hasanipak, A.A., and Sharafadin, M., 2012. 3rd Edition, Exploration data analysis, University of Tehran Press. (In Persian).
Hosseini, S.A., Khah, N.K.F., Kianoush, P., Afzal, P., A Ebrahimabadi, A., and Shirinabadi, R., 2023. Integration of Fractal modeling and Correspondence Analysis Reconnaissance for Geochemically High-Potential Promising Areas, NE Iran. Results in Geochemistry, 100026. https://doi.org/10.1016/j.ringeo.2023.100026.
Kavoshgaran, 2010, Geochemical explorations of soil in the area of Eastern Kahang, scale 1/1000, Report of the National Copper Industries Company of Iran, Development and Modernization Organization of Mines and Mining Industries of Iran. (In Persian).
Mahdiyanfar, H., and Salimi, A., 2022. Fractal Modeling of Geochemical Mineralization Prospectivity Index based on Centered Log-Ratio Transformed Data for Geochemical Targeting: A Case Study of Cu Porphyry Mineralization. Journal of Mining and Environment, 13(3), 821–838. https://doi.org/10.22044/jme.2022.12024.2197.
Mohammadi, N.M., Hezarkhani, A., and Saljooghi, B.S., 2016. Separation of a geochemical anomaly from background by fractal and U-statistic methods, a case study: Khooni district, Central Iran. Geochemistry, 76(4), 491–499. https://doi.org/10.1016/j.chemer.2016.09.001.
Nabavi, M., H. 1976. Introduction to the geology of Iran, 1rd Edition, Geological Survey and Mineral Exploration of Iran. (In Persian).
Plant, J., and Hale, M., 1994. Drainage geochemistry. Handbook of exploration geochemistry. Elsevier, Amsterdam.
Reimann, C., 2005. Geochemical mapping: technique or art? Geochemistry: Exploration, Environment, Analysis, 5(4), 359–370. https://doi.org/10.1144/1467-7873/03-051.
Salimi, A., and Rafiee, A., 2021. A grid interpolation technique for anomaly separation of stream sediments geochemical data based on catchment basin modelling, U-statistics and fractal. Earth Science Informatics. https://doi.org/10.1007/s12145-021-00712-4.
Seyedrahimi-Niaraq, M., and Hekmatnejad, A., 2021. The efficiency and accuracy of probability diagram, spatial statistic and fractal methods in the identification of shear zone gold mineralization: a case study of the Saqqez gold ore district, NW Iran. Acta Geochimica, 40, 78-88. https://doi.org/10.1007/s11631-020-00413-7.
Seyedrahimi-Niaraq, M., and Mahdiyanfar, H., 2021. Introducing a new approach of geochemical anomaly intensity index (GAII) for increasing the probability of exploration of shear zone gold mineralization. Geochemistry, 81(4), 125830. https://doi.org/10.1016/j.chemer.2021.125830.
Seyedrahimi-Niaraq, M., and Mahdiyanfar, H., 2022. Determination of geochemical anomalous areas using U-statistics modeling of multi-element principal factor values (U-PCA) related to gold mineralization of shear zone. Journal of Mining Engineering, 17(55), 88-10.  (In Persian). Doi: 10.22034/IJME.2021.534004.1872.
Seyedrahimi-Niaraq, M., Shahsavani, H., and Hekmatnejad, A., 2022. Application of U-spatial statistics for separating magnetic anomalies: a case study on the Galali iron ore deposit in western Iran. Arabian Journal of Geosciences, 15(21), 1629. https://doi.org/10.1007/s12517-022-10831-x.
Sillitoe, R. H., 2010. Porphyry copper systems. Economic geology, 105(1), 3-41. https://doi.org/10.2113/gsecongeo.105.1.3.
Yang, L., Wang, Q., and Liu, X., 2015. Correlation between mineralization intensity and fluid–rock reaction in the Xinli gold deposit, Jiaodong Peninsula, China: constraints from petrographic and statistical approaches. Ore Geology Reviews, 71, 29–39. https://doi.org/10.1016/j.oregeorev.2015.04.005.
Yousefi, M., 2017. Analysis of zoning pattern of geochemical indicators for targeting of porphyry-Cu mineralization: a pixel-based mapping approach. Natural Resources Research, 26(4), 429–441. https://doi.org/10.1007/s11053-017-9334-7.
Yousefi, M., Barak, S., Salimi, A., and Yousefi, S., 2023. Should geochemical indicators be integrated to produce enhanced signatures of mineral deposits? A discussion with regard to exploration scale. Journal of Mining and Environment. 14(3), 1011-1018.  https://doi.org/10.22044/jme.2023.13160.2398.
Yousefi, M., Carranza, E.J.M., Kreuzer, O.P., Nykänen, V., Hronsky, J.M.A., and Mihalasky, M.J., 2021. Data Analysis Methods for Prospectivity Modelling as applied to Mineral Exploration Targeting: State-of-the-Art and Outlook. Journal of Geochemical Exploration, 229. https://doi.org/10.1016/j.gexplo.2021.106839.
Yousefi, M., Kamkar-Rouhani, A., and Carranza, E.J.M., 2012. Geochemical mineralization probability index (GMPI): a new approach to generate enhanced stream sediment geochemical evidential map for increasing probability of success in mineral potential mapping. Journal of Geochemical Exploration, 115, 24–35. https://doi.org/10.1016/j.gexplo.2012.02.002.
Yousefi, M., Kreuzer, O.P., Nykänen, V., and Hronsky, J.M.A., 2019. Exploration information systems–A proposal for the future use of GIS in mineral exploration targeting. Ore Geology Reviews, 111, 103005. https://doi.org/10.1016/j.oregeorev.2019.103005.
Zuo, R., and Wang, J., 2016. Fractal/multifractal modeling of geochemical data: A review. Journal of Geochemical Exploration, 164, 33-41. https://doi.org/10.1016/j.gexplo.2015.04.010.
Scientific Quarterly Journal of Geosciences
Volume 34, Issue 3 - Serial Number 133
Autumn 2024, Vol. 34, Issue 3, Serial No. 133
Summer 2024
Pages 63-74