Analysis of Data in Kerver Area for Detection of Blind Mineralization Using Singularity Method

Document Type : Research Article

Authors

Dept. of Mining, Petroleum & Geophysics, Shahrood University of Technology, Shahrood, Iran

10.29252/anm.2020.8731.1302

Abstract

Summary
Evaluation of zone dispersed mineralization from blind mineralization in active and inactive mines are the main challenges in mining geochemistry. In this research a new model has been presented for detection of anomaly by integration of singularity and zonality methods. This method shows the depletion and enrichment of vertical zonality index in the study area. Singularity of vertical zonality index was mapped in Kerver2. Results showed blind mineralization in the west of this area.
 
Introduction
Successive non-linear processes generating frequency distributions with Pareto tails may be related causally such as rainfall and flooding. The total amount of ore and metals in hydrothermal ore deposits often have Pareto tails. Many researches were done for detection of weak anomalies with geophysical and geochemical data. In this paper detection of blind mineralization was done by integration of singularity and zonality methods for the first time in Kerver that located in Jebal- Barez zone.
 
Methodology and Approaches
Local anomalies of the sub-ore and supra-ore elements were detected in the study area. Area productivity and vertical zonality index were calculated in these local anomalies. Then zonality index was calculated with productivity mention and detection of blind mineralization was done with this index. In addition to the new method presented in this paper (integration of singularity and zonality) were done in this area. The calculation of singularity map was coded in MATLAB programming software. For this purpose a grid map was produced. Values of zonality index were calculated at each point. Then seven square windows set in ranging 100*100 m2,  300*300 m2, 500*500 m2, 700*700 m2, 900*900 m2, 1100* 1100 m2 and 1300*1300 m2. Then the average of zonality index (C[Ari]) was calculated in each window (ri [i=1,3,..., 13]). Straight line for C[Ari] and ri was fitted in log- log plot. Following that, the slope of the straight line was calculated that shows the value of α-2.
 
Results and Conclusions
Detection of the blind mineralization has been done with zonality method so far. But this method cannot detect the weak blind anomalies and this issue were observed in this study, there is blind mineralization in west of the study area that zonality method could not detect it, whereas integration of singularity and zonality could detect it. Thus   integration of singularity and zonality methods is benefit way for recognition of blind mineralization.

Keywords

Main Subjects

Full Text

در علم فیزیک عبارت تکین یا سینگولار به پدیده‌هایی اطلاق می‌شود که دارای شار شدید انرژی یا ماده در المان کوچک فضایی- زمانی باشند. تکینگی به عنوان یک ویژگی مهم در فرایندها یا سیستم‌های طبیعی غیرخطی در شاخه‌های مختلف علوم زمین مثل تشکیل ابرها، سیل، طوفان، رانش زمین و تشکیل ذخایر هیدروترمال است. از نقطه نظر کاربرد زمین‌شناسی، این ویژگی می‌تواند به عنوان یک پدیده خاص با مقدار بی هنجاری انرژی رها شده یا مواد تجمع یافته در فاصله زمانی- مکانی کوتاه تعریف شود. در فرایند کانی‌سازی معمولاً مقدار زیادی عناصر فلزی در مدت زمان کوتاه (در مقایسه با مقیاس زمانی زمین‌شناسی) و در محدوده کوچکی (نسبت به گسترش زمین) رخ می­دهند. همراه با کانی‌سازی هیدروترمال، فعالیت ماگما همراه با تخلیه و تجمع ماده معدنی است که با تعریف تکینگی مطابقت دارد. اضافه و کم شدن مواد از سنگ‌ها اغلب توزیع سینگولار غلظت فلزات را در سنگ میزبان به وجود می­آورد[1، 2].

References

[1]           Cheng, Q. 2006. Singularity-generalized self-similarity-fractal spectrum (3S) models. Earth Science–Journal of China University of Geoscience, 31, 337-348.
[2]           Turcotte, D.L., 1997. Fractals and Chaos in Geology and Geophysics. Cambridge University Press, Cambridge, 412pp
[3]           Chen, G., Cheng, Q., Zuo, R., Liu, T. and Xi, Y., 2015. Identifying gravity anomalies caused by granitic intrusions in Nanling mineral district, China: a multifractal perspective. Geophysical Prospecting, 63(1), 256-270
[4]           Yu, X., Xiao, F., Zhou, Y., Wang, Y., & Wang, K. 2019. Application of hierarchical clustering, singularity mapping, and Kohonen neural network to identify Ag-Au-Pb-Zn polymetallic mineralization associated geochemical anomaly in Pangxidong district. Journal of Geochemical Exploration, 203, 87-95.
[5]           Cheng, Q., 2007. Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China. Ore Geology Review, 32(1), 314-324.
[6]           Yilmaz, H., Yousefi, M., Parsa, M., Sonmez, F. N., & Maghsoodi, A. 2019. Singularity mapping of bulk leach extractable gold and− 80# stream sediment geochemical data in recognition of gold and base metal mineralization footprints in Biga Peninsula South, Turkey. Journal of African Earth Sciences, 153, 156-172.
[7]           Zuo, R., Cheng, Q., Agterberg, F. and Xia, Q., 2009. Application of singularity mapping technique to identify local anomalies using stream sediment geochemical data, a case study from Gangdese, Tibet, western China. Journal of Geochemical Exploration, 101(3), 225-235.
[8]           Wang, W. 2010. Application of spatial information fusion techniques in GeoDAS for mapping Sn mineralization associated intrusions in Gejiu ore district, Southern Yunnan, China. in: Masters Abstracts International, 176pp.
[9]           Sun, X., Gong, Q., Wang, Q., Yang, L., Wang, C. and Wang, Z., 2010. Application of local singularity model to delineate geochemical anomalies in Xiong'ershan gold and molybdenum ore district, Western Henan province, China. Journal of Geochemical Exploration. 107(1), 21-29.
[10]         Chen, G. and Cheng, Q., 2016. Singularity analysis based on wavelet transform of fractal measures for identifying geochemical anomaly in mineral exploration. Computer & Geoscience, 87, 56-66.
[11]         Gonçalves, M. A., & Mateus, A. 2019. "Delimiting geochemical anomalies in the exploration of covered deposits with multifractal methods and using stream sediment data from the Iberian Pyrite Belt, Southwest Iberia". Ore Geology Reviews, 112, 103018.
[12]         Ghasemzadeh, S., Maghsoudi, A., Yousefi, M., & 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, 103-115.
[13]         Hamedani, M.L., 2012. Orebody modelling for exploration: the western mineralization, Broken Hill, NSW. Natural Resources Research, 21(3), 325-345.
[14]         Li, H., Wang, Z. and Li, F., 1995. Ideal models of superimposed primary halos in hydrothermal gold deposits. Journal of Geochemical Exploration, 55(1-3), 329-336.
[15]         Li, H., Zhang, G. and Yu, B., 2006. Tectonic Primary Halo Model and the Prospecting Effect during Deep Buried Ore Prospecting in Gold Deposits, in, Geological Publishing House, Beijing.
[16]         Li, H., Zhang, W., Chang, F., Zheng, T., Liu, B., Wang, Z., Tang, L., Liu, Z., Li, F. and Wang, J., 1998 Primary Halo Model for Buried Ore Prospecting of Large and Super-large Gold Deposits, in, Metallurgical Industry Press, Beijing.
[17]         Beus, A.A and Grigorian, S.V., 1977. Geochemical Exploration Methods for Mineral Deposits. Applied Publishing Ltd.
[18]         Harraz, H. Z., 1995. Primary geochemical haloes, El Sid gold mine, Eastern Desert, Egypt. Journal of African Earth Sciences, 20(1):61-71.
[19]         Solovov, A. P. and Kuznetov, V., 1987. Geochemical prospecting for mineral deposits, Mir Publ. 282 pp.
[20]         Borna, B., 2004. "Primary exploration of copper in Kerver (Jebal Barez): scale 1:20000. National Iranian Copper Industries Company (NICICO) Reports". 151p (In Persian).
[21]         Borna, B., Sodishoar, P., 2005. "Primary exploration of copper in Kerver: scale 1:5000. National Iranian Copper Industries Company (NICICO) Reports", 70 p (In Persian).
[22]         Yousefi, J., Ranjbar, H., Dargahi, S., Taghizadeh, H., 2019." Mineralogy and geochemistry of gossan in Kerver porphyry copper deposit, Kerman copper belt, Iran", 10th symposium of Iranian society of economic geology. Esfahan.
[23]         Ziaii M., Carranza E. J. M. and Ziaei M. 2011. "Application of geochemical zonality coefficients in mineral prospectivity mapping" Computer & Geoscience., 37, 12, pp. 1935.
Journal of Analytical and Numerical Methods in Mining Engineering
Volume 9, Issue 21
February 2020
Pages 21-31

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History

  • Receive Date: 10 December 2017
  • Revise Date: 29 December 2019
  • Accept Date: 06 January 2020

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