تخمین عیار طلا در کانسار خونی با استفاده از بررسی رفتار عناصر طلا، آرسنیک و آنتیموان به روش خوشه بندی K-means.

نوع مقاله : مقاله پژوهشی

نویسندگان

دانشکده مهندسی معدن و متالورژی، دانشگاه صنعتی امیرکبیر

چکیده

محدوده اکتشافی خونی در270 کیلومتری استاناصفهان در منطقه انارک و در زون ایران مرکزی واقع شده است. با توجه به وجود شواهدی حاکی از کانی‌سازی طلا در این ناحیه، شناسایی نواحی امید بخش معدنی در این منطقه ضروری است. بنابراین ضرورت یافتن اطلاعاتی در مورد ارتباط و چگونگی رفتار عناصر طلا، آرسنیک و آنتیموان نسبت به یکدیگر در این محدوده، برای پیدایش و تعیین وسعت هاله­های ژئوشیمیایی، تعیین امیدبخش بودن منطقه و تخمین عیار اهمیت می­یابد و باید مورد بررسی قرار گیرند. بدین منظوردر تحقیق حاضر برای تعیین رفتار عناصر نام برده، با استفاده از روش معروف و کارآمد K-means انجام می‌شود که یکی از روش­های خوشه­بندی بوده و مبتنی بر کمینه نمودن مجموع فواصل اقلیدسی هر یک از نمونه­ها از مرکز دسته­ای که به آن تخصیص می‌یابد، است. در این پژوهش از تابع کیفیت خوشه­بندی و میزان مطلوبیت نمونه در خوشه مورد نظر (S(i))، برای تعیین تعداد خوشه بهینه استفاده شده است و در پایان با توجه به مراکز خوشه­ها و نتایج حاصل، معادله­ای جهت تخمین عیار عنصر طلا بر حسب چهار پارامتر عیار آرسنیک، عیار آنتیموان، طول و عرض نقاط نمونه‌برداری ارائه می‌شود.

تازه های تحقیق

  1. معرفی یک روش جدید و کارآمد برای تخمین عیار
  2. بررسی نقش روش k-means در محاسبه عیار
  3. بررسی نقش عناصر همراه در محاسبه عیار
  4. بررسی رفتار عناصر طلا و آرسنیک و آنتیموان

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Estimation of grade Gold in khooni deposit using the behavior of gold, Arsenic and Antimony elements by clustering k-means method.

نویسندگان [English]

  • Neda Mahvash Mohammadi
  • Ardeshir Hezarkhani
چکیده [English]

Summary
Khooni exploratory area is located at 270 kilometers of Esfahan, and belongs to Central Iran geological zone. According to some instances expressing gold mineral forming in this area, make reconnaissance of favorable area as an essential fact. A well-known algorithm of clustering is K-Means by which the data are divided into K clusters on the basis of distance. In this analysis, using the K-Means method to classify the sampling of khooni district for Gold, Arsenic and Antimony elements. The optimal K value was determined and then the data were clustered and the behavioral characteristics were analyzed, and at the end relationships and equations with correlation coefficients were identified and the grade of gold were estimated based on four parameters Arsenic, Antimony content; length and width of the sampling points.
 
Introduction
There are many methods to estimate the grade because of mining projects have high dependency to determine the tonnage accurately. One of the new methods of estimating the grade is clustering. Thus, it is necessary to find information about the relationships and behavior elements of Gold, Arsenic and Antimony to each other. in this area to determine the extent of the geochemical halo and to identify favorable area as well as estimation.
 
Methodology and Approaches
In this study fordetermination the behavior of the mentioned elements, the famous and efficient method of K-Means, which is one of the clustering methods, are used, that is based on minimizing total of Euclidean distance from the center each class. For this purpose, the quality function and silhouette criterion have been used to determine the optimal number of clusters. All data ranged the number of classes from k=3 to k=10 and afterwards the optimumnumber of clusters were selected by mentioned methods. At the end, according to the Center of clusters, the equation for estimating the grade of gold is provided. In this study, MATLAB and SPSS software are used to achieve results.
 
Results and Conclusions
The relationship of gold, arsenic and antimony according to length and width of the sampling points were determined for estimation grade of gold, Results showed that clustering with k=5 in case of Au, As and Sb were better than other classes number in each case. Obtained equation for estimating grade of gold element based on four parameters Arsenic, Antimony content; length and width of the sampling points, is  and the correlation coefficient 73% is reported. Also to evaluate the accuracy of the estimator, the results were validated. Estimated gold element values almost match with actual values of gold element that represents the accuracy of the used method.

کلیدواژه‌ها [English]

  • Gold
  • khooni deposits
  • Clustering
  • estimated grade
  • K-Means

مراجع

[1] Majdifar, S., Kamali, GH. (2014). Iron Grade Estimation Using ANFIS Algorithm at Tappeghermez Anomaly of Sangan Mine. Journal of Analytical and Numerical Methods in Mining Engineering (ANM). No 5. (in Persian)
[2] Abbas zadeh, S., Rahimi pour, GH., Najmodini, M. (2013). Recognition of Cu-Porphyry Mineralization Areas by Using One and multivariate integration methods on drainage geochemical data in Ghale Askar area, Kerman province. Journal of Analytical and Numerical Methods in Mining Engineering (ANM). No, 6. (in Persian)
[3] ShayesteFar, M., Rasa, I. (2005). Multivariate Data Analysis of Qanat Marvan Lead-Zinc Deposit,. Kerman. Geosciences Scientific Quarterly Journal. No, 57. (in Persian)
[4] Malyszko, D., Wierzchon, S. T. (2007). Standard and Genetic K-means Clustering Techniques in Image Segmentation. In 6th International Conference on Computer Information Systems and Industrial Management Applicatins.
[5] Abolhassani, B., Salt, J. E. (2005). A Simplex K-means Algorithm for Radio-Port Placement in Cellular Networks. In Canadaian Conference on Electrical and Computer Engineering.
[6] Chen, T. W., Chien, S. Y. (2009). Bandwidth Adaptive Hardware Architecture of K-Means Clustering for Video Analysis. IEEE Transactions on VLSI Systems.
[7] Yang, J., Zhuang, Y., Wu, F. (2012). ESVC- based extraction and segmentation of texture features. Computers & Geosciences, Elsevier, 49, 238-247.
[8] Mora, J.L., Armas-Herrera, C.M., Guerra, J.A., Rodríguez-Rodríguez, A., Arbelo, C.D., (2012), Factors affecting vegetation and soil recovery in the Mediterranean woodland of the Canary Islands (Spain). Journal of Arid Environments, Elsevier, 87, 58-66.
[9] Meshkani, S. A., Mehrabi, B., Yaghubpur, A., Alghalandis, Y. F., (2011). The application of geochemical pattern recognition to regional prospecting: A case study of the Sanandaj–Sirjan metallogenic zone, Iran. Journal of Geochemical Exploration, Elsevier, 108, 183-195
[10] Sfidari, E., Kadkhodaie-Ilkhchi, A., Najjari, S., (2012), Comparison of intelligent and statistical clustering approaches to predicting total organic carbon using intelligent systems. Journal of Petroleum Science and Engineering, Elsevier,63, 190-205.
[11] Wegner, T., Hussein, T., Hämeri, K., Vesala, T., Kulmala, M., Weber, S., (2012). Properties of aerosol signature size distributions in the urban environment as derived by cluster analysis. Atmospheric Environment, Elsevier, 61, 350-360.
[12] Ahmadian, J., Michael, H., McDonald, I., Regelous, M., Ghorbani, M. R., and Murata, M. (2009). High magmatic flux during Alpine- Himalayan collision: Constraints from the Kal-e-Kafi complex, Central Iran. Geological Society of America Bulletin. 121, 857-868.
[13] Nezampoor, M.H., (2005). Determination of promise points in Kal-e-Kafi area with remote sensing technology, Anarak, central Iran, Mineraleum deposita conference, China.
[14] Kan Azin consulting engineers Co. (2005). Report of geology, mining and Lithogeochemical 1: 5000 to explore gold in the khooni district. )in Persian(
[15] Heydarian, N., Rassa, A., (2012). Characteristics and genesis of gold mineralization in Eocene volcanic units of khooni Cheshmeh, Anarak, the nature of mineralizing fluids and comparison with other types of gold deposits. Journal of Geology of Iran 17, 73-85. (in Persian)
[16] Pourjabar, A., (2005). Geochemical investigations on the polymetalic vein in Khouni (Esfahan Province), MSc Thesis, Amirkabir University of Technology, Tehran, 217.) in Persian(
[17] Egozcue, J.J., Pawlowsky-Glahn, V., Mateu-Figueras, G. (2003). Isometric Logratio Transformations for Compositional Data Analysis. Mathematical Geology, 35, 279-300.
[18] Jain, A. K. (2012). Data clustering: 50 years beyond K-Means, Pattern Recognition Letters, Elsevier, 31, 651-666.
[19] Saha, S., Bandy opadhyay, S. (2013). A generalized automatic clustering algorithm in a multi objective framework. Applied Soft Computing, Elsevier, 13, 89-108.
[20] Ghannad pour, S., Hezarkhani, A., Mahvash mohammadi, N. (2013). Investigation of Pb behavior circumstance than Zn in porphyry copper system of Parkam, Kerman using K-means method. First Geological Conference of Iranian plateau. )in Persian(
[21] Menard, J. J. (1995). Relationship between altered pyroxene diorite and the magnetite mineralization in the Chilean Iron Belt, with emphasis on the El Algarrobo iron deposits (Atacama region, Chile)., Mineral Deposita, Springer-Verlag, 30, 268-274.
[22] Leiluo, X., Xianwu, B., Ruizhong, H., Xingchun, Z. (2012). Relationships between porphyry Cu–Mo mineralization in the Jinshajiang–Red Rivermetallogenic belt and tectonic activity: Constraints from zircon U–Pb and molybdenite Re–Os geochronology., Ore Geology Reviews, Elsevier, 48, 460–473.
[23] Tarkian, M., Stribrny, B. (1999). Platinum-group elements in porphyry copper deposits: a reconnaissance study. Mineralogy and Petrology, Springer-Verlag, 65, 161-183.
[24] Yaghini, M., Ghannad pour, F., Khadmatlu, S., (2008). Offering an clustering innovative method in data mining by using genetic algorithms to solve a real case study in the rail transportation industry, Iran Data Mining Conference. Amirkabir university. Tehran.) in Persian(
[25] Shina, H.W. and Sohnb, S.Y. (2004). Segmentation of Stock Trading Customers According to Potential Value, Expert Systems with Applications, 27, 27–33.

فایل ها

سابقه مقاله

  • تاریخ دریافت: 01 مرداد 1394
  • تاریخ بازنگری: 22 بهمن 1394
  • تاریخ پذیرش: 12 اسفند 1394

هم رسانی

ارجاع به این مقاله

آمار