Author(s):
1. Fırat Atalay, Hacettepe University, Turkey
2. Gunes Ertunc, Hacettepe University, Turkey

Abstract:
Ore resource estimation is one of the primary step of pre-feasibility/feasibility studies of mine projects. Feasibility of projects related with distribution of the target content. For this reason, spatial distribution of the ore content has to be estimated. This paper aims to estimate the iron content of an iron deposit using deep neural networks. 42 drillholes are drilled at the deposit. In order to perform estimations solid model of the deposit is generated using sections method and block model of the deposit is also generated. Using Matlab® a deep neural network is trained for drillhole data. This trained model is used to estimate the iron content of the block model. Results of the estimates are compared with raw data and trend analysis is performed to validate the estimations. Results shows that estimations are unbiased and can be used for further for mine planning, pre-feasibility and feasibility studies.

Key words:
Iron ore,Deep neural networks,Geostatistics,Spatial estimation

Thematic field:
System engineering, risk management and modern trends

Date of abstract submission:
14.05.2017.

Conference:
7th Balkan Mining Congress

Final paper