3D modeling and reserve estimation of a coal deposit using neural networks
| Autor: | A.G. Pasamehmetoglu, N. Çelebi, H. Akcakoca, K. Erarslan |
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| Přispěvatelé: | Panagiotou, G.N., Michalakopoulos, T.N. |
| Jazyk: | angličtina |
| Rok vydání: | 2000 |
| Předmět: | |
| Zdroj: | Mine Planning and Equipment Selection 2000 ISBN: 9780203747124 |
| Popis: | 9th International Symposium on Mine Planning and Equipment Selection -- NOV 06-09, 2000 -- -- ATHENS, GREECE WOS: 000166190800145 Neural network systems are powerful tools for data analysis, data structure learning and interpreting. This ability of them provides their use in extension of drill hole values and reserve estimation. In this study, a neural network system developed fur data extension and its application to a coal deposit is introduced. The system has training and estimation capabilities. Out of 119 drill holes, 107 drill holes are used for training and 12 drill holes for testing. After obtaining an acceptable estimation error for the level of learning, the system extends the sample data to 3D-block model. The results and tests show that the system is a reliable extension tool. Natl Tech Univ Athens, Dept Min Engn & Met, Univ Laval, Dept Mines & Met, Univ Studi Cagliari, Dept Geoingegneria & Tecnol Ambientali, Univ Politech Madrid, Atilim Univ, Natl Min Univ Ukraine, WH Bryan Min Geol Res Ctr, Univ Queensland, Western Australian Sch Mines, Curtin Univ Technol, Int Journal Surface Min Reclamat & Environm, World Min Equipment, Amer Soc Surface Min & Reclamat, World Min Assoc Soil & Water Conservat, CENTEK Int Training & Dev Ctr, Lulea Univ, VSB Tech Univ, Fac Min & Geol, Gluckauf Min Reporter |
| Databáze: | OpenAIRE |
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