Integration of a neural ore grade estimation tool in a 3D resource modeling package
نویسندگان
چکیده
Ore grade estimation is a key aspect in the evaluation of a mineral deposit. In this paper an alternative approach to currently applied methods of ore grade estimation is presented. This alternative approach involves a modular neural network system integrated in a state of the art 3D resource modelling package. The need for a new method of ore grade estimation comes from the difficulties in applying conventional methods such as geostatistics. These methods require a lot of assumptions, knowledge, skills and time to be effectively applied while their results are not always easy to justify. The aim of the proposed system, called GEMNet II is to provide fast and reliable ore grade estimation, with minimum assumptions and minimum requirements for modelling skills. GEMNet II has been tested on a number of real deposits. The results obtained so far have shown that it can provide with a very fast and robust alternative to the existing time-consuming methodologies for ore grade estimation.
منابع مشابه
The Application of Artificial Neural Networks to Ore Reserve Estimation at Choghart Iron Ore Deposit
Geo-statistical methods for reserve estimation are difficult to use when stationary conditions are not satisfied. Artificial Neural Networks (ANNs) provide an alternative to geo-statistical techniques while considerably reducing the processing time required for development and application. In this paper the ANNs was applied to the Choghart iron ore deposit in Yazd province of Iran. Initially, a...
متن کاملDelineation of Alteration Zones Based on Wavelet Neural Network (WNN) and Concentration–Volume (C-V) Fractal Methods in the Hypogene Zone of Porphyry Copper Deposit, Shahr-e-Babak District, SE Iran
In this paper, we aim to achieve two specific objectives. The first one is to examine the applicability of wavelet neural network (WNN) technique in ore grade estimation, which is based on integration between wavelet theory and Artificial Neural Network (ANN). Different wavelets are applied as activation functions to estimate Cu grade of borehole data in the hypogene zone of porphyry ore deposi...
متن کاملCorrelation between IP and Rs and grade data in modeling and evaluation of a copper deposit, case study: the Sarbisheh copper deposit, Iran
This paper addresses the application of integrated chargeability and resistivity method and grade data in modeling and evaluation of copper deposits. We argue that the relationship between IP, Rs and grade data may be used for modeling and reserve estimation and tested this argument for Sarbisheh copper deposit that is located in eastern Iran. Geology and mineralization situation of Sarbisheh d...
متن کاملLimestone chemical components estimation using image processing and pattern recognition techniques
In this study based on image analysis, an ore grade estimation model was developed. The study was performed at a limestone mine in central Iran. The samples were collected from different parts of the mine and crushed in size from 2.58 cm down to 15 cm. The images of the samples were taken in appropriate environment and processed. A total of 76 features were extracted from the identified rock sa...
متن کاملDelineation of alteration zones based on artificial neural networks and concentration-volume fractal methods in the hypogene zone of porphyry copper-gold deposit, Masjed-Daghi, East Azerbaijan Province, Iran
In this paper, we aim to achieve two specific objectives. The first one is to examine the applicability of the Artificial Neural Networks (ANNs) technique in ore grade estimation. Different training algorithms and numbers of hidden neurons are applied to estimate Cu grade of borehole data in the hypogene zone of porphyry copper-gold deposit, Masjed-Daghi, East Azerbaijan Province (Iran). The ef...
متن کامل