Intelligent Borehole Simulation with python Programming | ||
| Journal of Mining and Environment | ||
| مقاله 18، دوره 15، شماره 2، تیر 2024، صفحه 707-730 اصل مقاله (1.36 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2023.13610.2527 | ||
| نویسندگان | ||
| Hassanreza Ghasemitabar* 1؛ Andisheh Alimoradi2؛ Hamidreza Hemati Ahooi2؛ Mahdi Fathi3 | ||
| 1Faculty of Mining, Petroleum & Geophysics Eng., Shahrood University of Technology, Shahrood, Iran | ||
| 2Department of Mining Eng., Faculty of Eng., Imam Khomeini International University, Qazvin, Iran | ||
| 3Department of Petroleum and Sedimentary Basins, Faculty of Earth Sci., Shahid Beheshti University, Tehran, Iran. | ||
| چکیده | ||
| Drilling of exploratory boreholes is one of the most important and costly steps in mineral exploration, which can provide us with accurate and appropriate information to continue the mining process. There are limitations on drilling the target boreholes, such as high costs, topographical problems in installation of drilling rigs, restrictions caused by previous mining operation etc. The advances in artificial intelligence can help to solve these problems. In this research, we used python as one of the most pervasive and the most powerful programming languages in the field of data analysis and artificial intelligence. In this method mean shift algorithms have been used to cluster data, random forest to estimate clusters, and gradient boosting to estimate iron grade. Finally, in the studied area of Choghart in Central Iran, more than 91% accuracy was achieved in detection of ore blocks. Also, the results of the neural network indicate the mean square error (MSE) and mean absolute error (MAE) in the training data, respectively equal to 0.001 and 0.029, in the test data is 0.002 and 0.03, and in the validation boreholes, we reached a maximum of 0.06 and 0.2. | ||
| کلیدواژهها | ||
| Ore Grade Estimation؛ Artificial Intelligence؛ Random Forests؛ Mean Shift؛ Gradient Boosting | ||
| مراجع | ||
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