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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 24th International Mining Congress of Turkey, IMCET 2015
EditorsMehmet Karadeniz, Mehtap Gulsun Kilic, Elif Torun Bilgic, Hakan Basarir, Oznur Onel
PublisherTMMOB Maden Muhendisleri Odasi
Pages257-263
Number of pages7
ISBN (Electronic)9786050107050
Publication statusPublished - 2015
Event24th International Mining Congress of Turkey, IMCET 2015 - Antalya, Turkey
Duration: Apr 14 2015Apr 17 2015

Publication series

NameProceedings of the 24th International Mining Congress of Turkey, IMCET 2015

Other

Other24th International Mining Congress of Turkey, IMCET 2015
Country/TerritoryTurkey
CityAntalya
Period4/14/154/17/15

All Science Journal Classification (ASJC) codes

  • Geotechnical Engineering and Engineering Geology

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