The integration of artificial neural networks and geostatistical 3D geological block modelling: A case study on a mineral sand deposit

A. B. Jalloh, K. Sasaki, Yaguba Jalloh

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

1 被引用数 (Scopus)

抄録

In this research, an Artificial Neural Network model was developed to predict metal content from drillhole data using the back propagation algorithm. For validation purposes, results between the actual and predicted mineral grades were compared, and regression analysis of the compared results indicate that the predicted mineral grades were in close proximity to the actual grades. The validated model was used to predict mineral grades at unsampled locations in order to determine the feasibility of drilling in those areas. The optimum results obtained from the neural network were fed to geostatistical techniques for developing a geological 3D block model for mine design. The generalized data from the Neural Network show that Artificial Neural Networks can be used to complement exploration activities and is an effective approach for mineral reserve estimation without worrying about spatial variability or other assumptions.

本文言語英語
ホスト出版物のタイトルProceedings of the 24th International Mining Congress of Turkey, IMCET 2015
編集者Mehmet Karadeniz, Mehtap Gulsun Kilic, Elif Torun Bilgic, Hakan Basarir, Oznur Onel
出版社TMMOB Maden Muhendisleri Odasi
ページ257-263
ページ数7
ISBN(電子版)9786050107050
出版ステータス出版済み - 2015
イベント24th International Mining Congress of Turkey, IMCET 2015 - Antalya, トルコ
継続期間: 4月 14 20154月 17 2015

出版物シリーズ

名前Proceedings of the 24th International Mining Congress of Turkey, IMCET 2015

その他

その他24th International Mining Congress of Turkey, IMCET 2015
国/地域トルコ
CityAntalya
Period4/14/154/17/15

!!!All Science Journal Classification (ASJC) codes

  • 地盤工学および土木地質学

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