Exploitation
Moslem Jahantigh; Hamidreza Ramazi
Abstract
Various methods have been used for clustering big data. Pattern recognition methods are suitable methods for clustering these data. Due to the large volume of samples taken in the drilling of mines and their analysis for various elements, this category of geochemical data can be considered big data. ...
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Various methods have been used for clustering big data. Pattern recognition methods are suitable methods for clustering these data. Due to the large volume of samples taken in the drilling of mines and their analysis for various elements, this category of geochemical data can be considered big data. Examining and evaluating drilling data in the Lar copper mine in Sistan and Baluchistan province located in the southeast of Iran requires the use of these methods. Therefore, the main goal of the article is the clustering of the drilling data in the mentioned mine and its zoning of the geochemical data. To achieve this goal, 3500 samples taken from drilling cores have been used. Elemental analysis for six elements has been done using the ICP-Ms method. Pattern recognition methods including SOM and K-MEANS have been used to evaluate the relation between these elements. The silhouette method has been used to determine and evaluate the number of clusters. Using this method, 4 clusters have been considered for the mentioned data. According to this method, it was found that the accuracy of clustering is higher in the SOM method. By considering the 4 clusters, 4 zones were identified using clustering methods. By comparing the results of the two methods and using the graphical method, it was determined that the SOM method has a better performance for clustering geochemical data in the studied area. Based on that, zones 2 and 4 were recognized as high-grade zones in this area.
Exploration
Moslem Jahantigh; Hamid Reza Ramazi
Abstract
Fuzzy c-means (FCM) is an unsupervised machine learning algorithm. This method assists in integrating airborne geophysics data and extracting automatic geological map. This paper tries to combine airborne geophysics data consisting of aeromagnetic, potassium, and thorium layers to classify the lithological ...
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Fuzzy c-means (FCM) is an unsupervised machine learning algorithm. This method assists in integrating airborne geophysics data and extracting automatic geological map. This paper tries to combine airborne geophysics data consisting of aeromagnetic, potassium, and thorium layers to classify the lithological map of the Shahr-e-Babak area, a world-class porphyry area in the south of Iran. The resulting clusters with FCM show appropriate coincidence with the geological map of the study area. The clusters are adapted with high magnetic anomalies corresponding to the mafic volcanic rocks and the clusters with high radiometric signature associated with igneous rocks. The cluster is associated with low magnetic anomaly and low radioelements concentration representing sedimentary rocks. some clusters are associated with two or more lithological formations due to similar signatures of geophysics properties. The fuzzy score membership in all clusters is above 0.71 indicating a high correlation between geological signatures and multigeophysical data. This study shows geophysical signatures analyzed with the machine learning method can reveal geological units.
Exploration
Moslem Jahantigh; Hamid Reza Ramazi
Abstract
The present paper gives out data-driven method with airborne magnetic data, airborne radiometric data, and geochemistry data. The purpose of this study is to create a mineral potential model of the Shahr-e-Babak studied area. The studied area is located in the south-eastern of Iran. The various evidential ...
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The present paper gives out data-driven method with airborne magnetic data, airborne radiometric data, and geochemistry data. The purpose of this study is to create a mineral potential model of the Shahr-e-Babak studied area. The studied area is located in the south-eastern of Iran. The various evidential layers include airborne magnetic data, airborne radiometric data (potassium and thorium), lineament density map, cu geochemistry signature, and multi-variate geochemistry signature (PC1). High magnetic anomalies, lineament structures, and alteration zones (K/Th) were derived from airborne geophysics data. Geochemistry signatures (Cu and PC1) were derived from stream sediment data. The principal Component Analysis (PCA) as an unsupervised machine learning method and five evidential layers were used to produce a porphyry prospectivity model. As a result of this combination, mineral prospectivity model was produced. Then a plot of cumulative percent of the studied area versus pca prospectivity value was used to discrete high potential areas. Then to evaluate the ability of this MPM, the location of known cu indications was used. The results confirm an acceptable outcome for porphyry prospectivity modeling. Based on this model high-potential areas are located in south southwestern and eastern parts of the studied area.
