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.
Exploration
Jabar Habashi; Majid Mohammady Oskouei; Hadi Jamshid Moghadam
Abstract
The studied area located in eastern Iran shows a high potential for various mineralizations, especially copper due to its tectonic activity. Remote sensing data can effectively distinguish these areas because of the sparse vegetation. Therefore, in this study, the ASTER (Advanced Spaceborne Thermal Emission ...
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The studied area located in eastern Iran shows a high potential for various mineralizations, especially copper due to its tectonic activity. Remote sensing data can effectively distinguish these areas because of the sparse vegetation. Therefore, in this study, the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) multi-spectral data was used to recognize argillic, sericite, propylitic, and iron oxide alterations associated with copper mineralization. For this purpose, two categories (porphyry copper-iron and advanced argillic-iron) related alterations were considered to perform the classification of a 2617 square kilometer area using a neural network classification algorithm. To evaluate the accuracy of the classifier, the confusion matrix was computed, which provides overall accuracy and the kappa coefficient factors for assessing classification accuracy. As a result, 64.17% and 83.5% of overall accuracy, and 0.602 and 0.807 of the kappa coefficient were achieved for the advanced argillic alterations and porphyry copper categories, respectively. Ultimately, the validation of the classifications was carried out using the normalized score (NS) equation, employing quantitative criteria. Notably, the advanced argillic class emerged with the top normalized score of 2.25 out of 4, signifying a 56% alignment with the geological characteristics of the region. Consequently, this outcome has led to the identification of favorable areas in the central and northeastern parts of the studied area.
A. Owolabi
Abstract
In this paper, we report a geospatial assessment of the selected mine sites in the Plateau State, Nigeria. The aim of this work is to determine the impact of mining on the terrain as well as the Land Use/Land Cover (LULC) of the host communities. The Shuttle Radar Topographic Mission (SRTM) is used for ...
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In this paper, we report a geospatial assessment of the selected mine sites in the Plateau State, Nigeria. The aim of this work is to determine the impact of mining on the terrain as well as the Land Use/Land Cover (LULC) of the host communities. The Shuttle Radar Topographic Mission (SRTM) is used for the terrain mapping. The derived impact of mining on LULC between 1975 and 2014 is determined by classifying the relevant Landsat imageries. The digital terrain map reveal that the mining activity is not well-coordinated. Hence, the parts of the mine sites that are rich in the desired minerals are punctuated with low depth, while the other parts have high terrain as a result of the haphazard mining activity. The analysis of the LULC change show that the degraded land (DL), built-up area (BU), water bodies (WB), and exposed rock outcrop (RO) increase by 15.68%, 4.68%, 0.06%, and 14.5%, respectively, whereas the arable farmland (FL) and forest reserve (FR) decrease by 28.29% and 6.63%, respectively. Mining has adversely affected the natural ecology of the studied area. Therefore, the mine sites should be monitored, and their environmental damages should be pre-determined and mitigated. There should be regular inspections to keep these activities under control. The existing laws and regulations to conserve the natural ecosystems of the host communities should be enforced to curtail the excesses of the operators of the mining industries. Restoration of the minefields to reduce the existing hazards prevent further environmental degradation, and facilitating the socio-economic development of the area is also suggested.
Exploration
N. Mahvash Mohammadi; A. Hezarkhani
Abstract
Identification and mapping of the significant alterations are the main objectives of the exploration geochemical surveys. The field study is time-consuming and costly to produce the classified maps. Therefore, the processing of remotely sensed data, which provide timely and multi-band (multi-layer) data, ...
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Identification and mapping of the significant alterations are the main objectives of the exploration geochemical surveys. The field study is time-consuming and costly to produce the classified maps. Therefore, the processing of remotely sensed data, which provide timely and multi-band (multi-layer) data, can be substituted for the field study. In this study, the ASTER imagery is used for alteration classification by applying two new methods of machine learning, including Random Forest and Support Vector Machine. The 14 band ASTER and 19 derivative data layers extracted from ASTER including band ratio and PC imagery, are used as training datasets for improving the results. Comparison of analytical results achieved from the two mentioned methods confirmed that the SVM model has sufficient accuracy and more powerful performance than RF model for alteration classification in the study area.
Seyyed M. Hoseini; F. Sereshki; M. Ataei
Abstract
By evaluation of the blasting results, a proper blast pattern can be presented. It is, therefore, essential to employ a reliable method to evaluate blastings for the effective control and optimization of the main cycle operations. This paper aims to propose a criterion for evaluating the blasting results ...
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By evaluation of the blasting results, a proper blast pattern can be presented. It is, therefore, essential to employ a reliable method to evaluate blastings for the effective control and optimization of the main cycle operations. This paper aims to propose a criterion for evaluating the blasting results such as the fragmentation, muckpile condition, back-break, and fly rock, and to make a possible comparison between the blast parameters including the blasting pattern, explosives used, hole depths, and volume of the blasted rocks in the lead and zinc mine in Angouran (Iran). Using the global criterion, making the decision matrix dimensionless, and defining the appropriate conditions for the results obtained, a scalar value is devoted for the blasts, whose larger values denote a larger deviation from the proper blasting conditions and express undesirable blasts regarding the blasting results. By taking into consideration the mining operation conditions and weights of the results obtained, the influence of the results obtained on the mining operation index is also investigated using the genetic algorithm. Furthermore, by composing the weighted decision matrix, the blastings are evaluated and classified. Analyzing the results obtained for blastings in the Angouran mine reveals that the proposed method is an effective approach for evaluation of the blasting results and comparison of the blasts.
M. Koneshloo; Jean-Paul Chiles
Abstract
The kaolinitic clays have been exploited for more than a hundred years, in the western part of the Charentes Basin, France, and belong to a paleo-deltaic network. The recent deposits are relatively richer in alumina in comparison with the older ones. The genesis of the kaolin deposits of the Charentes ...
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The kaolinitic clays have been exploited for more than a hundred years, in the western part of the Charentes Basin, France, and belong to a paleo-deltaic network. The recent deposits are relatively richer in alumina in comparison with the older ones. The genesis of the kaolin deposits of the Charentes Basin follows simple geological rules, but their detailed geometry has a great complexity, reinforced by the fact that one must distinguish very different clay qualities. The exploitation of the complex deposits which are buried in the deeper level needs the more powerful tools. The paper aims at analyzing the adequacy of the traditional method used in the exploitations of the kaolin deposits of the Charentes Basin in comparison with another method based on geostatistics to define criteria of selection and classification of reserves.
