تخمین ذخیره کانسار سنگ آهن لکه سیاه با روش های زمین آماری و شبکه مصنوعی

نوع مقاله : علمی-پژوهشی

نویسندگان

1 دانشجوی دوره دکتری، گروه مهندسی معدن، دانشکده فنی و مهندسی، دانشگاه شهید باهنر کرمان، کرمان

2 دانشیار، گروه مهندسی معدن، دانشکده فنی و مهندسی، دانشگاه شهید باهنر کرمان، کرمان

3 دانشیار، دانشکده مهندسی معدن و متالورژی، دانشگاه صنعتی امیرکبیر، تهران

چکیده

سرمایه‌گذاری‌ها و پیشرفت پروژه‌های معدنی بستگی به کمیت و کیفیت منابع و ذخایر معدنی دارد، بنابراین اطلاع از اعتبار تخمین ذخیره معدنی بر اساس روش‌های گوناگون اهمیت فراوانی دارد. این پژوهش به بررسی نقش زمین‌آمار و شبکه عصبی مصنوعی در مدل‌سازی، تخمین عیار بلوک‌های اکتشافی و تخمین ذخیره کانسار پرداخته است. مدل‌سازی توزیع فضایی مقادیر آهن با استفاده از سه روش کریجینگ معمولی، شبیه‌سازی متوالی گاوسی و شبکه عصبی مصنوعی انجام شده است. برای ساخت مدل بلوکی کانسار، از 29 گمانه اکتشافی با میانگین عمق 75/142 متر و مجموع طول 9/4139 متر استفاده شد. تحلیل‏های آماری بر روی 1247 داده کامپوزیت‏ شده 2 متری انجام شد. پس از بررسی‌های آماری، واریوگرافی سه‌بعدی برای شناخت ناهمسانگردی منطقه و انتخاب بهترین واریوگرام و بیضوی جستجو برای متغیر آهن، انجام و مدل سه‌بعدی کانسار برای تخمین عیار به روش‌های کریجینگ معمولی و شبیه‌سازی متوالی گاوسی به دست آمد. همچنین مدل‌سازی و تخمین عیار به روش شبکه عصبی مصنوعی انجام گرفت. اعتبارسنجی نتایج به دست آمده از روش شبکه عصبی مصنوعی نشان داد که این تخمین اعتبار بسیار خوبی دارد و به علت سادگی استفاده و عدم نیاز به محاسبات سنگین واریوگرافی جایگزین مناسبی برای روش‏های زمین‏آماری کریجینگ و شبیه‏سازی متوالی گاوسی است. در ادامه نیز بر اساس عیار حدهای مختلف، میزان تناژ و عیار متوسط محاسبه و نمودار عیار-تناژ رسم شد. نتایج نشان می‌دهد که این کانسار به ازای عیار حد 20 درصد، 439 میلیون تن ماده معدنی با عیار متوسط 42 درصد دارد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Estimating the Reserve of Lake- Siah Iron Ore Body by Geostatistical and Artificial Network Methods

نویسندگان [English]

  • S.J. Mousavi 1
  • M.R. Shayestehfar 2
  • P. Maarefvand 3
1 Ph.D Student, Dept. of Mining Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
2 Associate Professor, Dept. of Mining Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
3 Assistant Professor, Dept. of Mining Engineering, Amirkabir University of Technology, Tehran, Iran
چکیده [English]

Investments and progress of mining projects depend on the quantity and quality of mineral resources and reserves; therefore, it is significant to know the reliability of the ore deposit estimation based on various methods. This research has investigated the role of geostatistics and artificial neural networks in modeling, estimating the concentration of exploration blocks, and estimating mining reserves. Spatial distribution modeling of iron values has been performed using three methods of ordinary kriging, Gaussian simulation, and artificial neural networks. To construct the block model of ore deposit, 29 exploratory boreholes with an average depth of 142.75 meters and a total length of 4139.9 meters were used. After drawing the variograms, the search ellipse was calculated, and a 3D model was obtained to estimate the concentration by ordinary kriging and Gaussian sequential simulation methods. Also, modeling and estimating the concentration was done by the artificial neural network method. Results showed that the artificial neural network technique has high validity. Also, due to its ease of use and no need for extra variographic calculations can be an appropriate alternative to geostatistical and simulation approaches. Finally, based on different cut-offs, the concentration-volume curve was drawn. The results show that this ore body has 439 million tons of ore with an average concentration of 42 % per 20% cut-off concentration.

کلیدواژه‌ها [English]

  • Ordinary kriging
  • Sequential Gaussian simulation
  • Artificial neural network
  • Concentration-volume curve
  • Lake-siah

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