استفاده از مدل تركيبي فاكتوري- فركتالي جهت شناسايي پتانسيل‌هاي معدني طلا، آرسنيك و آنتيموان در ورقه 1:100000 بصيران، خراسان جنوبي، شرق ايران

Using hybrid factor-fractal model for identification of Au, As, Sb mineral potential in Basiran 1:100000 sheet, south Khorasan, east of Iran

انتخاب مناسب ترين روش براي شناسايي مقادير آنومالي از زمينه يكي از چالش هاي مهم در زمينه اكتشافات ژيوشيميايي مي باشد. در اين خصوص روش هايي همچون روش هاي آماري، هوش مصنوعي، محاسبات نرم و روش هاي تركيبي توسعه يافته اند كه هريك از اين روش ها داراي مزايا و معايبي مي باشند. هدف اين تحقيق شناسايي پتانسيل هاي معدني عناصر طلا، آرسنيك و آنتيموان در برگه 1:100000 بصيران با استفاده از مدل تركيبي فاكتوري- فركتالي است. براي اين منظور تعداد 585 نمونه ژيوشيميايي اخذ شده از رسوبات آبراهه اي كه براي 20 عنصر آناليز شده است مورد مطالعه قرار گرفت. براي رسيدن به هدف ابتدا داده ها توسط روش فاكتوري R مد مطالعه و تعداد 5 فاكتور استخراج گرديد كه با توجه به هدف، فاكتور سوم كه تركيب خطي از عناصر آنتيموان، آرسنيك و طلا بود انتخاب شد. مقادير امتيازات فاكتوري محاسبه و نقشه امتيازات فاكتوري رسم شد. سپس روش فركتالي غلظت -مساحت بر روي امتيازات فاكتوري مولفه سوم اعمال و مقادير حد آستانه تعيين گرديدند و در نهايت براساس اين مقادير نقشه مدل تركيبي فاكتوري- فركتالي رسم شد. مقايسه اين نقشه با نقشه فركتالي غلظت - مساحت نشان داد كه شدت بخشي آنومالي ها با مدل تركيبي بهتر انجام شده است. به منظور اعتبارسنجي نتايج حاصله از مدل تركيبي، تعداد 6 نمونه كاني سنگين از منطقه اخذ و مطالعه شد. نتايج حاصله از مطالعات كاني سنگين بيانگر وجود ذرات طلا در اين نمونه ها بود. مقايسه نتايج مطالعات كاني سنگين و مدل هاي آماري نشان مي دهد كه انطباق خوبي (بيش از 90 درصد) بين نتايج وجود دارد زيرا كه نمونه هاي كاني سنگين منطبق بر نواحي آنومالي مي باشد.

IntroductionOne of the main challenges about geochemical exploration is the selection of an appropriate method for anomaly separation from background. In this field, many approaches including statistical methods, artificial intelligence, soft computing and compositional methods have been developed. Each method has some weak and strong points. Research literature showed the statistical hybrid models were used less in this field.Materials and MethodsNowadays, the hybrid models have been considered to enhance the intensity of geochemical anomalies. Until now, only some hybrid models were used. The most important of them are neuro-fuzzy and genetic algorithm model, multifractal-neural network model, fractal-U statistic model, fractal-wavelet model and factor analysis-neural network model. The previous studies indicate that the univariate and multivariate statistic methods and artificial neural networks were mainly used as hybrid model. The main goal of this research is the identification of mineral potential of Au, Sb and As in Basiran 1:100000 sheet, which is located in 180 Km of West of Birjand, through using factor-fractal hybrid model. For this purpose, the 585-stream sediment geochemical data, which were taken by Trade, Mine and Industry Organization of South Khorasan, were analyzed for 20 elements including of Sb, As, Au, Ba, Se, Mn, Ti, Cr, Zn, Cu, Fe, Co, Ag, Sn, Mo, Ni, Pb, W, Hg and Bi.Results and Discussion In first step, the data have been studied by R-mode factor analysis and 5 factors were extracted. With respect to the goal, the third factor with the best linear combination of Sb, Au and As elements, was elected. Afterwards, the factor scores were calculated and depicted on the map of factor’s scores. Consequently, fractal method was applied on the concentration area based on third factor scores, and the thresholds values were determined. Finally, based on threshold values, the map of hybrid factor-fractal model was illustrated. Comparison of both obtained results using hybrid model and concentration-area fractal model indicated that the hybrid model is more effective. As indicated in the following figure, the position of heavy metal samples are located on identified mineralized zone using factor-fractal hybrid model.Conclusion In this paper, recognition of mineral potential of Au, As and Sb using factor-fractal hybrid model, is a new idea and innovation. For validation of hybrid model results, 6 heavy metal samples were gained and studied. The results of heavy metal studies confirmed the existence of gold particles in samples. The drawn results stated a good agreement (more than 90 percent) between heavy metal locations and identified mineral potential area by using the hybrid model.