جستجوی مقالات مرتبط با کلیدواژه "reserve estimation" در نشریات گروه "مهندسی معدن"

This research work aims to discuss the methodology of using the drone-based data in the initial steps of the exploration program for the dimension stone deposits. A high-resolution imaging is performed by a low-cost commercial drone at the Emperador marble quarry, Kerman province, Iran. A ground resolution of 3 cm/pix is achieved by imaging at an altitude of 70 m in order to ensure the precise lithological and structural mapping. An accuracy of less than 5 cm is promised for the 3D photogrammetric products. Hence, the flight is performed with an 80% front and a 70% lateral image overlap. Furthermore, 18 ground control points (GCPs) are used in order to meet the required accuracy. Photogrammetric processing is done by the Agisoft PhotoScan software. The geology map is prepared through the visual geo-interpretation of the orthophoto image. The faults and fractures are delineated using the high-resolution orthophoto and hill-shade model in the ArcGIS software. Accordingly, the density map of fractures is produced, and the deposit is divided into five structural zones. The 3D deposit model with an accuracy of 2.8 cm is reconstructed based on the digital elevation model (DEM). A primary block model is generated using the 3D deposit model in the Datamine software in order to determine the resource for each structural zone. Finally, considering the amount of resource and situation of fractures, the priority of exploration for developing activities and appropriate methods is defined for each structural zone. The research work results have convinced us to include drone-based imagery in the initial steps of dimension stone exploration to consume the time and cost of the operation.

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, an optimum Multi Layer Perceptron (MLP) was constructed to estimate the Fe grade within orebody using the whole ore data of the deposit. Sensitivity analysis was applied for a number of hidden layers and neurons, different types of activation functions and learning rules. Optimal architectures for iron grade estimation were 3-20-10-1. In order to improve the network performance, the deposit was divided into four homogenous zones. Subsequently, all sensitivity analyses were carried out on each zone. Finally, a different optimum network was trained and Fe was estimated separately for each zone. Comparison of correlation coefficient (R) and least mean squared error (MSE) showed that the ANNs performed on four homogenous zones were far better than the nets applied to the overall ore body. Therefore, these optimized neural networks were used to estimate the distribution of iron grades and the iron resource in Choghart deposit. As a result of applying ANNs, the tonnage of ore for Choghart deposit is approximately estimated at 135.8 million tones with average grade of Fe at 56.14 percent. Results of reserve estimation using ANNs showed a good agreement with the geo-statistical methods applied to this ore body in another work.