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بهکارگیری الگوریتمهای یادگیری ماشین SVM و RF در تهیه نقشه پتانسیل معدنی محدوده مس – طلای پورفیری شادان ، خراسان جنوبی، ایران | ||
| مدل سازی در مهندسی | ||
| دوره 24، شماره 84، فروردین 1405، صفحه 227-251 اصل مقاله (986.64 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2025.35411.2743 | ||
| نویسندگان | ||
| حسن حسین زاده* 1؛ غلامرضا نوروزی1؛ آرش گورابجیری پور2؛ معصومه دادپور3 | ||
| 1دانشکده فنی و مهندسی، دانشگاه بیرجند، بیرجند، ایران | ||
| 2دانشکده علوم، دانشگاه آزاد اسلامی واحد میانه، میانه، ایران | ||
| 3دانشکده علوم، دانشگاه صنعتی شاهرود، شاهرود، ایران | ||
| تاریخ دریافت: 21 مهر 1403، تاریخ بازنگری: 17 خرداد 1404، تاریخ پذیرش: 25 خرداد 1404 | ||
| چکیده | ||
| شناسایی مناطق با پتانسیل معدنی در اکتشافات تفصیلی بهمنظور طراحی دقیقتر برنامههای اکتشافی، از طریق تحلیل و ارزیابی دادههای موجود انجام میگیرد. این فرایند در چارچوب مدلسازی پتانسیل معدنی توسعه داده شده و به بهینهسازی عملیات اکتشاف کمک میکند. در این تحقیق، از الگوریتمهای یادگیری ماشین نظارتشده شامل ماشین بردار پشتیبان (SVM) و جنگل تصادفی (Random Forest) برای تهیه نقشه پتانسیل معدنی محدوده شادان استفاده شده است. منطقه شادان که بخشی از بلوک لوت و زون فلیشی - کمربند افیولیتی شرق ایران است. در این پژوهش، با بهکارگیری الگوریتمهای یادگیری ماشین بر روی دادههای اکتشافی منطقه، شامل دادههای زمینشناسی (سنگشناسی و ساختاری)، ژئوشیمی نمونههای سنگی، تصاویر ماهوارهای و ژئومغناطیسی، مناطق هدف برای حفاریهای بیشتر شناسایی شدند. سپس، عملکرد مدلهای تولیدشده با استفاده از شاخصهایی مانند Accuracy، Sensitivity، نمودار ROC و نمودار P-A ارزیابی و مقایسه شد. نتایج نشان داد که مدل پتانسیل معدنی حاصل از روش SVM عملکرد بهتری در شناسایی اهداف اکتشافی مس و طلای پورفیری داشته است. این مدل توانست 13 درصد از مساحت ناحیه موردبررسی را بهعنوان نواحی مطلوب برای حفاریهای آتی که با حفاریهای انجامشده قبلی نیز مطابقت داشت، بهدرستی پیشبینی کند. نواحی شناساییشده عمدتاً در مناطقی با تکتونیزه بالا و مرتبط با واحدهای سنگی آندزیت، گرانیت و گرانودیوریت قرار دارند. این پژوهش نشاندهنده برتری روش SVM در شناسایی نواحی مطلوب اکتشافی است و میتواند بهعنوان مبنایی برای برنامهریزی مراحل بعدی فرایند اکتشاف استفاده شود. | ||
| کلیدواژهها | ||
| نقشه پتانسیل معدنی؛ یادگیری ماشین؛ الگوریتم SVM؛ الگوریتم RF؛ شادان | ||
| عنوان مقاله [English] | ||
| Assessment of Copper-Gold Mineral Potential in the Shadan Porphyry Area Using SVM and RF Machine Learning Algorithms | ||
| نویسندگان [English] | ||
| Hassan Hosseinzadeh1؛ Gholamreza Nowrouzi1؛ Arash Gourabjeri2؛ Masoumeh Dadpour3 | ||
| 1Department of Mining Engineering, Birjand University, Birjand, Iran | ||
| 2Department of Geology, Faculty of Science, Miyaneh Branch Azad University, Miyaneh, Iran | ||
| 3Department of Geology, Faculty of Science, Shahrood University of Technology, Shahrood, Iran | ||
| چکیده [English] | ||
| This study applied machine learning algorithms, namely Support Vector Machine (SVM) and Random Forest (RF), to develop a mineral potential map for the Shadan region, situated within the Lut Block and Flysch-Ophiolite Belt of Eastern Iran. The research integrated multiple exploration datasets, including geological, geochemical, satellite imagery, and geomagnetic data, to identify promising areas for mineral exploration, specifically targeting porphyry copper and gold deposits. The performance of the models was evaluated using metrics like Accuracy, Sensitivity, ROC curves, and P-A plots. The SVM model demonstrated superior accuracy, successfully predicting 13% of the study area as high-potential zones for future drilling, which corresponded closely with existing drilling results. These identified target zones were predominantly located in regions with intense tectonic activity and were associated with rock units such as andesite, granite, and granodiorite. The study underscores the effectiveness of the SVM model in accurately delineating mineral-rich areas, providing a valuable basis for future exploration programs. | ||
| کلیدواژهها [English] | ||
| Mineral potential map, machine learning, SVM algorithm, RF algorithm, Shadan | ||
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آمار تعداد مشاهده مقاله: 502 تعداد دریافت فایل اصل مقاله: 166 |
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