Exploration and Mining
Ghodratollah Rostami Paydar; Hani Asadi Hoveizian
Abstract
Determination of relationship between rock mass properties concerning to rock qualification design (RQD) has an important role in mine planning and designation. The aim of this study is separation of rock mass properties to designing the mine planning based on the 3267 RQD data analysis of 43 drill-core ...
Read More
Determination of relationship between rock mass properties concerning to rock qualification design (RQD) has an important role in mine planning and designation. The aim of this study is separation of rock mass properties to designing the mine planning based on the 3267 RQD data analysis of 43 drill-core within Zarshuran gold deposit in Orumieh- Dukhtar assemblage zone applying RQD-Number and RQD-Volume fractal modeling. The results of log-log plots for RQD-N model revealed four rock populations that divided by RQD thresholds 20.41, 47.86, 69.18 and 81.28. The results of log-log plots for RQD-V model release four populations divided by RQD thresholds 21.37, 43.65, 63.09, 79.43 respectively which represent very poor, poor, fair and good rocks based on Deere and Miller rock classification. In other hand the lithological units modeled based on the drill-core data to obtain the spatial distribution of Zarshuran deposit. The results of RQD-N and RQD-V fractal modeling versus lithological units modeling results revealed that Chaldagh limestone unit and Jaspiroid unit shows fair and good quality with RQD fractal value 69.18 till 81.28 and located at the center and western part of Zarshuran deposit. Therefore, in mine slop designing and planning have excellent conditions. The results of the RQD-N fractal modeling in Zarshuran deposit can usage as a practical method in similar districts.
A. Hossein Morshedy; H. Memarian
Abstract
Zoning is an important practice in earth sciences. In zonation, the study area is divided into separate parts and by compiling the results of these parts, a unique model is obtained. In this study, clustering methods are applied for zoning of Semilan dam site. Optimal number of clusters are measured ...
Read More
Zoning is an important practice in earth sciences. In zonation, the study area is divided into separate parts and by compiling the results of these parts, a unique model is obtained. In this study, clustering methods are applied for zoning of Semilan dam site. Optimal number of clusters are measured based on geotechnical parameters (lugeon, RQD), the importance of various dam structures and lithology indicators. By ranking of 7 clustering validity indexes, the optimum number of clusters found to be 4. In this paper, clustering was performed by faults locations and self-organizing neural network. In the former case, the study area was divided into four zones based on faults. This two dimensional zoning is independed of the third dimension (depth) and each sample belonged to a cluster. In the later case, a self-organizing map (SOM), which is a kind of neural network capable of clustering, was used. The SOM input data consists of, three dimensional parameters (X,Y,Z), geotechnical parameters (lugeon, RQD) and finally indicators of importance of various dam structures and lithology. Then, 7 input parameters were normalized between 0 to 1 and entered the network for training.The output data were allocated to four zones (clusters). For RQD spatial distribution realization, variography and anisotropy parameters for all four zones were calculated for both cases, Based on the main principal of clustering method which is maximum difference between clusters and maximum similarity between members of each cluster, performance and validation of two cases of clustering, RQD data were defined. Clustering quality index defined as sum of mean differences between two clusters divided by sum of standard deviation of clusters. Maximizing of this index is optimal solution. This study showed that clustering by SOM gives more accurate results than clustering by faults.