Exploration
Seyyed Saeed Ghannadpour; Samaneh Esmaelzadeh Kalkhoran; Maedeh Behifar; Hadi Jalili
Abstract
In this study, with the aim of identifying alteration zones related to the porphyry copper system, satellite images are processed in study area (the Zafarghand exploration area) in the NE of Isfahan. For this purpose, one of the common methods of separating geochemical anomalies from the background, ...
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In this study, with the aim of identifying alteration zones related to the porphyry copper system, satellite images are processed in study area (the Zafarghand exploration area) in the NE of Isfahan. For this purpose, one of the common methods of separating geochemical anomalies from the background, i.e. fractal Concentration-Number (C-N) model, has been employed. The C-N fractal model will normally be implemented on geochemical samples. While in this study, the digital number values belonging to the pixels of the ASTER sensor image are considered as a systematic sample network and also as input for this model. The output of this processing has been prepared in the form of maps of promising areas of the Zafarghand region. The correspondence of the resulting maps with the alteration map of the region shows that applying the proposed method in determining the propylitic and phyllic alteration zones has had acceptable performance. Finally, with the help of the aforementioned proposed method, a map of the promising areas of the study area has been prepared, and based on that, new zones of alterations have been introduced in the region.
Exploitation
Pouya Nobahar; Yashar Pourrahimian; Roohollah Shirani Faradonbeh; Fereydoun Mollaei Koshki
Abstract
Mineral reserve evaluation and ore type detection using data from exploratory boreholes are critical in mine design and extraction. However, preparing core samples and conducting chemical and physical tests is a time-consuming and costly procedure, slowing down the modeling process. This paper presents ...
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Mineral reserve evaluation and ore type detection using data from exploratory boreholes are critical in mine design and extraction. However, preparing core samples and conducting chemical and physical tests is a time-consuming and costly procedure, slowing down the modeling process. This paper presents a novel Deep Learning (DL)-based model to recognize the types of kaolinite samples. For this purpose, a dataset containing the images of drilled cores and their types determined from conventional chemical and physical analyses was used. Eight Convolutional Neural Network (CNN) topologies based on individual features were developed, named A, B, C, D, E, F, G, and H. Six of the eight proposed CNN topologies described above had accuracy below 80%, whereas two of them, model A and H, had higher accuracy than other topologies. Due to their similarity in results, both of them analyzed deeply. Model A was more efficient, with 90% accuracy, than model B, with 84% accuracy. Furthermore, the class detection performance of model A was further evaluated using different indices, including precision, recall, and F1-score, which resulted in values of 92%, 92%, and 90%, respectively, which are acceptable accuracies to identify the type of samples when using this approach on six different types of kaolinite.
F. Khorram; H. Memarian; B. Tokhmechi; H. Soltanian-zadeh
Abstract
In this study based on image analysis, an ore grade estimation model was developed. The study was performed at a limestone mine in central Iran. The samples were collected from different parts of the mine and crushed in size from 2.58 cm down to 15 cm. The images of the samples were taken in appropriate ...
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In this study based on image analysis, an ore grade estimation model was developed. The study was performed at a limestone mine in central Iran. The samples were collected from different parts of the mine and crushed in size from 2.58 cm down to 15 cm. The images of the samples were taken in appropriate environment and processed. A total of 76 features were extracted from the identified rock samples in all images. Neural network used as an intelligent tool for ore grade estimation and the features of every image were combined with weighted average method. In order to feature dimensional decrease, principal component analysis method was used. Six principal components, which were extracted from the feature vectors, captured 90.661% of the total feature variance. Components were used as the input to neural network and four grade attributes of limestone (CaCO3, Al2O3, Fe2O3 and MgCO3) were used as the output. The root of mean squared error between the observed values and the model estimated values for the test data set are 6.378, 4.847, 0.1513 and 0.0284, the R2 values are 0.7852, 0.8663, 0.7591and 0.8094 for the mentioned chemical composition respectively. The magnitude of R2 indicates the correlation between actual and estimated data. Therefore, it can be inferred that the model can successfully estimate the limestone chemical compositions percentage.
