آمار

تعداد نشریات21
تعداد شماره‌ها301
تعداد مقالات3,173
تعداد مشاهده مقاله3,211,769
تعداد دریافت فایل اصل مقاله2,380,293
Simorgh, Mohsen, Alimoradi, Andisheh, Hemmati Ahooi, Hamidreza, Salsabili, Mohammad, Fathi, Mahdi, Ghasemi Tabar, Hassanreza, Rezakhani, Parisa. (1402). Application of Supervised Machine Learning Inversion in the Estimation of Iron Ore Grade from Geophysical Data: Comparative Investigation of GB, RF and SVM Algorithms. , 1(3), 123-136. doi: 10.22077/jgm.2024.7358.1018
Mohsen Simorgh; Andisheh Alimoradi; Hamidreza Hemmati Ahooi; Mohammad Salsabili; Mahdi Fathi; Hassanreza Ghasemi Tabar; Parisa Rezakhani. "Application of Supervised Machine Learning Inversion in the Estimation of Iron Ore Grade from Geophysical Data: Comparative Investigation of GB, RF and SVM Algorithms". , 1, 3, 1402, 123-136. doi: 10.22077/jgm.2024.7358.1018
Simorgh, Mohsen, Alimoradi, Andisheh, Hemmati Ahooi, Hamidreza, Salsabili, Mohammad, Fathi, Mahdi, Ghasemi Tabar, Hassanreza, Rezakhani, Parisa. (1402). 'Application of Supervised Machine Learning Inversion in the Estimation of Iron Ore Grade from Geophysical Data: Comparative Investigation of GB, RF and SVM Algorithms', , 1(3), pp. 123-136. doi: 10.22077/jgm.2024.7358.1018
Simorgh, Mohsen, Alimoradi, Andisheh, Hemmati Ahooi, Hamidreza, Salsabili, Mohammad, Fathi, Mahdi, Ghasemi Tabar, Hassanreza, Rezakhani, Parisa. Application of Supervised Machine Learning Inversion in the Estimation of Iron Ore Grade from Geophysical Data: Comparative Investigation of GB, RF and SVM Algorithms. , 1402; 1(3): 123-136. doi: 10.22077/jgm.2024.7358.1018

Application of Supervised Machine Learning Inversion in the Estimation of Iron Ore Grade from Geophysical Data: Comparative Investigation of GB, RF and SVM Algorithms

Journal of Geomine
دوره 1، شماره 3، آذر 2023، صفحه 123-136    اصل مقاله (1.35 M)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22077/jgm.2024.7358.1018
نویسندگان
Mohsen Simorgh1؛ Andisheh Alimoradi* 2؛ Hamidreza Hemmati Ahooi1؛ Mohammad Salsabili3؛ Mahdi Fathi4؛ Hassanreza Ghasemi Tabar5؛ Parisa Rezakhani1
1Department of Mining Engineering, Imam Khomeini International University
2Department of Mining and Petroleum Engineering, Imam Khomeini International University
3Department des Sciences Appliquees, Universite du Quebec a Chicoutimi
4Kavoshgaran Consulting Engineers
5Department of Mining, Petroleum & Geophysics, Shahrood University of Technology
چکیده
Magnetometry is one of the geophysical methods used to explore metal deposits, especially iron deposits and magnetite minerals. The two-dimensional model resulting from the magnetometric operation cannot estimate the grade in the depth of the deposit, so in this article, the attempt is made by using the magnetic outputs obtained after the magnetic survey operation and the two-dimensional model designed with the help of the data extracted from the borehole which is available in the studied area, and combining this information and obtaining relationships between them with the help of artificial intelligence, a three-dimensional numerical model can be obtained that can be generalized to other points that lack depth data. This method will be a new approach to numerical simulation in the field of investigation of mineral masses.

Finally, in the studied area of the Sechahoon deposit in central Iran, high precision was achieved in the ratio of zero iron grade data in the methods of Gradient Boosting and Random Forest. Also, the results of these two algorithms showed that the Maximum Mean Square Error (MSE) and Mean Absolute Error (MAE) in the training data are 0.007 and 0.05, respectively, and in the test data are 0.03 and 0.11, respectively, which these parameters reached the maximum of 0.03 and 0.1 in the inspection of validation boreholes.
کلیدواژه‌ها
Magnetometry؛ Gradient Boosting؛ Random Forest؛ Support Vector Machines (SVMs)؛ Three-dimensional Modeling؛ Iron Deposit
آمار
تعداد مشاهده مقاله: 139
تعداد دریافت فایل اصل مقاله: 140


نماد الکترونیک 

 

سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب