Exploration and Mining
Mehdi Talkhablou; Mehdi Kianpour; Seyed Mahmoud Fatemi Aghda
Abstract
In this study, the efficiency of the compressive wave velocity (Vp) geophysical method for predicting the quality of limestone mass in areas of Zagros formation, has been investigated. For qualitative classification of limestone rock masses, the Q classification system and its modified classification ...
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In this study, the efficiency of the compressive wave velocity (Vp) geophysical method for predicting the quality of limestone mass in areas of Zagros formation, has been investigated. For qualitative classification of limestone rock masses, the Q classification system and its modified classification system for sedimentary rocks (Qsrm) have been used. For this purpose, the data related to Vp, Q and Qsrm were extracted at the site of Karun 2, Karun 4, Khersan 3 and Tangeh Manshoureh dam sites and by using software interpolation methods in ArcGIS has been transformed into information layers. Using simple and multivariable regression analysis on data extracted from information layers and using Fuzzy Inference System (FIS), models for predicting Q and Qsrm in calcareous rock masses are presented. Also, to evaluate the accuracy of the obtained models, in addition to R2, performance indicators (VAF) and root mean square error (RMSE) were used. The results show that since the Qsrm index considers a wider range of massive properties, the prediction of the Qsrm value is closer to reality using geophysical methods than the Q index.
S Alaei Moghadam; M Karimi; M Mesgari; N Saheb Zamani
Abstract
Due to the extensive areas of potential mineral reserves in the country, it seems necessary to have a systematic approach to identify and convert indices of mineral deposits into mines. Existing various conceptual models of mineral deposits, variety of both quantitative and qualitative data to explore ...
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Due to the extensive areas of potential mineral reserves in the country, it seems necessary to have a systematic approach to identify and convert indices of mineral deposits into mines. Existing various conceptual models of mineral deposits, variety of both quantitative and qualitative data to explore mineral deposits and the expertise and different interests, cause the mineral potential mapping process to be very complicated. So far, various methods such as the overlap index, fuzzy logic, neural networks and weights of evidence are used for modeling this complexity. Consideration the fuzzy nature of mineral exploration in the process of modeling exploratory data, applying expert knowledge and flexibility for all types of mineral deposits in the form of an integrated system is essential. Compared with other methods fuzzy inference system has stated characteristics. To verify this, in this study, a fuzzy inference system for modeling mineral potential was proposed and for the Chah Firoozeh copper deposit was implemented. The main stages of this research include fuzzifying factor maps using the appropriate membership functions and linguistic variables, combining factor maps using fuzzy inference (by creating if_then fuzzy rules database and using an appropriate decision-making model) and generating mineral potential map with defuzzification output.
In the resulted mineral potential map, porphyry copper mineralization prone area is located in the central regions with north-south extension. For evaluation, 24 exploration boreholes in the area are complying with the mineral potential map. Based on the four classification types of mineral potential map, the compliance rate was calculated as 63.64%, 75%, 63.95% and 80.23%. Obtained mineral potential map is more accurate in the very low potential areas and 81.52% of the holes with very low state are located properly. In addition, resulted mineral potential map was compared with the mineral potential map generated using only fuzzy operators and without fuzzifying factor maps. The comparison shows that the mineral potential map that was generated using fuzzy inference system, in four classifications used in this study has 6% greater compliance with the exploration boreholes in average.
M Kianpour; M Sayari; A Uromeihy; M.R Nikudel
Abstract
Shear strength is one of the most important properties of mudrocks and shales in rock engineering and engineering geology. Because of the difficulty to obtain undisturbed samples of shales as required for determination of shear strength parameters, it is also the most difficult to evaluate. This research ...
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Shear strength is one of the most important properties of mudrocks and shales in rock engineering and engineering geology. Because of the difficulty to obtain undisturbed samples of shales as required for determination of shear strength parameters, it is also the most difficult to evaluate. This research investigated properties that can be used to predict the shear strength parameters of Shemshak formation shales. Thirty samples of shales from various depths were collected from boreholes in Shemshak formation in the site of Siahbishe pumped storage powerhouse. Shear strength parameters (c and ϕ), tensile strength (TS), quartz percent (Qz %), porosity (n) and density (ρ) were determined in lab for each sample. Data were analyzed statistically and with fuzzy inference system to determine the relationships between shear strength parameters with other properties. Results show that cohesion and friction angle of shales can be meaningfully predicted from a few engineering properties by fuzzy inference system. The adjusted R2 values between measured and predicted values for cohesion and friction angle are 0.95 and 0.84 respectively. Also the variation of regression coefficient (R2), performance indices (VAF) and root mean square error (RMSE) with were calculated as for the shear strength parameters, obtained from the multiple regression modeland the fuzzyinference system, revealed that the prediction performance and accuracy of the fuzzy models are high and multiple regression equations not have performance in prediction of shear strength parameters of shales.
M. Kianpour; M. Sayari; A. Oromiea
Abstract
The uniaxial compressive strength and modulus of deformability of intact rocks are highly important parameters for rock engineering and engineering geology projects. Because of the difficulty of measuring these parameters and the need for laboratory equipments for their prediction, indirect methods ...
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The uniaxial compressive strength and modulus of deformability of intact rocks are highly important parameters for rock engineering and engineering geology projects. Because of the difficulty of measuring these parameters and the need for laboratory equipments for their prediction, indirect methods are often used. In this study, some predictive models using regression analysis and fuzzy inference system have been developed for the Shales cropping out in the Shemshak formation in Siahbishe area. For this purpose, a series of easy measurable parameters such as density, porosity and point load index were applied. Both multiple regression analyses and the fuzzy inference system exhibited good performance in prediction of the uniaxial compressive strength and modulus of deformability. The variation of regression coefficient (R2), performance indices (VAF) and root mean square error (RMSE) were calculated as for the uniaxial compressive strength and the modulus of deformability obtained from the multiple regression model and the fuzzy inference system revealed that the prediction performances and accuracy of the fuzzy model are higher than those of multiple regression equations in prediction of uniaxial compressive strength and modulus of deformability.
