Exploitation
B. Tokhmechi; S. Ebrahimi; H. Azizi; Seyed R. Ghavami-Riabi; N. Farrokhi
Abstract
Recognition of ore deposit genesis is still a controversial challenge for economic geologists. Here, this task was addressed by the virtue of Bayesian data fusion (BDF) implementing available proofs: semi-schematic examples with two (Cu and Pb + Zn) and three (Cu, Pb + Zn and Ag) evidences. The data, ...
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Recognition of ore deposit genesis is still a controversial challenge for economic geologists. Here, this task was addressed by the virtue of Bayesian data fusion (BDF) implementing available proofs: semi-schematic examples with two (Cu and Pb + Zn) and three (Cu, Pb + Zn and Ag) evidences. The data, in current paper are just concentrations of indicated elements, were collected from Angouran’s deposit in Iran at prospecting and general exploration stages. BDF was used for discrimination between three geneses of Massive Sulfide, Mississippi and SEDEX types. Better genesis recognition with clear discrimination between the geneses was achieved by BDF as compared with earlier studies. The results showed that uncertainties were reduced from 50% to less than 30% and deposit recognition was improved greatly. Furthermore, we believe that using more properties can have a beneficial effect on the overall outcome. The comparison made between 2 and 3 properties showed that the amount of probable belonging values to any type of deposit was greater in 3 properties. It was also confirmed that using the completed information from the various stages of exploration progress can be amplified and be used for genesis recognition via BDF.
Exploitation
M. Jahangiri; Seyed R. Ghavami Riabi; B. Tokhmechi
Abstract
Bearing in mind that lack of data is a common problem in the study of porphyry copper mining exploration, our goal was set to identify the hidden patterns within the data and to extend the information to the data-less areas. To do this, the combination of pattern recognition techniques has been used. ...
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Bearing in mind that lack of data is a common problem in the study of porphyry copper mining exploration, our goal was set to identify the hidden patterns within the data and to extend the information to the data-less areas. To do this, the combination of pattern recognition techniques has been used. In this work, multi-layer neural network was used to estimate the concentration of geochemical elements. From 1755 surface and boreholes data available, analyzed by ICP, 70% was used for training, and the rest for testing. The average accuracy of estimators for 22 geochemical elements when using all data was equal to 75%. Based on validation, the optimal number of clusters for the total data was identified. The Gustafson-Kessel (GK) clustering was used to design the estimator for the geochemical element concentrations in different clusters, and the clusters were selected for estimation. The results obtained show that using GK, the estimator's average accuracy increase up to 84%. The accuracy of the elementsZn, As, Pb, Mo, and Mn with low accuracies of 0.51, 0.62, 0.64, 0.65, and 0.68 based on all data were developed to 0.76, 0.86, 0.76, 0.80, and 0.71 with the clustered data, respectively. The mean square error using all the data was 0.079, while in the case of hybrid developed method, it decreased to 0.048. There were error reductions in Al from 0.022 to 0.012, in As, from 0.105 to 0.025, and from 0.115 to 0.046 for S.
Exploitation
S. Talesh Hosseini; O. Asghari; Seyed R. Ghavami Riabi
Abstract
Due to the existence of a constant sum of constraints, the geochemical data is presented as the compositional data that has a closed number system. A closed number system is a dataset that includes several variables. The summation value of variables is constant, being equal to one. By calculating the ...
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Due to the existence of a constant sum of constraints, the geochemical data is presented as the compositional data that has a closed number system. A closed number system is a dataset that includes several variables. The summation value of variables is constant, being equal to one. By calculating the correlation coefficient of a closed number system and comparing it with an open number system, one can see an increase in the values of the closed number system, which is false. Such features of this data prevent the application of standard statistical techniques to process the data. Therefore, several methods have been proposed for transforming the data from closed to open number systems. There are various geostatistical methods consisting of estimation and simulation methods in order to model a deposit. Geostatistical simulations can produce various models for a deposit with different probability percentages. The most applicable geostatistical simulation method is the sequential Gaussian simulation technique, which is highly flexible. In this work, 392 Litho-geochemical data of the Baghqloom region of Kerman in Iran consisting of 20 elements were at first converted using an open number system. Afterwards, the elements that were helpful for exploring the area and were normally standard were simulated for 100 times. After the simulations, the valid output was chosen using geostatistical validation. The maps derived from the simulations revealed the enriched concentrations of mineralization elements in the central regions.
Reza Ghavami-Riabi; H.F.J Theart
Abstract
The trace element contents on the surface originated from mineralization would depend to the thickness of the calcrete layer above the ore deposit on the surface. A very thick layer of calcrete may not allow for much dispersion of the elements of interest in the surface. These elements may be concentrated ...
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The trace element contents on the surface originated from mineralization would depend to the thickness of the calcrete layer above the ore deposit on the surface. A very thick layer of calcrete may not allow for much dispersion of the elements of interest in the surface. These elements may be concentrated in non-magnetic and magnetic part of calcrete. Based on the current research, mineralogical composition of the non-magnetic part of the calcrete consists of calcite, quartz and microcline and the magnetic part comprises of magnetite, hematite, calcite and albite (at Kantienpan). It could be demonstrated that calcrete samples close to the ore zone have higher contents of Cu, Zn and CaCO3 when compared to the calcrete samples further away from the ore zone. Lithogeochemical exploration program based on the visually cleaned calcrete samples may lead to the successful identification of underlying mineralization, but the dispersion of the interest elements may be severely restricted. It is however evident that these elements are available at the calcrete-sand interface and could then be dispersed by ground and rain water as in the case of mobile metal ions.
