Exploration
Mobin Saremi; Saeed Yousefi; Mahyar Yousefi
Abstract
The Mineral Prospectivity Mapping (MPM) is a procedure of integrating various exploration data to identify promising areas for follow up mineral exploration programs. MPM facilitates identification of mineral deposit prospects through reducing search spaces for the purpose of mitigating cost and time ...
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The Mineral Prospectivity Mapping (MPM) is a procedure of integrating various exploration data to identify promising areas for follow up mineral exploration programs. MPM facilitates identification of mineral deposit prospects through reducing search spaces for the purpose of mitigating cost and time shortages. In this regard, geochemical anomaly maps constitute one of the most important evidential layers for MPM. In this research work, to produce an efficient geochemical evidential layer, the Staged Factor Analysis (SFA) method and Geochemical Mineralization Probability Index (GMPI) were performed on a dataset of 657 stream sediment samples. In addition to the mentioned maps, a layer of proximity to faults was used to efficiently identify the intended targets of copper hydrothermal deposits. The layers were then weighted and combined using logistic functions and the geometric average method. Based on the obtained results, the promising areas were found in three parts including western, central, and northern areas, which correspond to the faulted units of andesite, tuff, granite, and granodiorite intrusive masses. Finally, in order to evaluate the generated model, the prediction-area (P-A) plot was used, which shows the relative success of the generated map in specifying the desired exploration targets. The P-A plot showed that this model has a prediction rate of 64%. It seems that the proposed method by considering multi-element geochemical signatures and combination by another exploratory layer target the promising areas, those that are simultaneously present with other exploration evidence.
Exploration
Kamran Mostafaei; Mohammad Nabi Kianpour; Mahyar Yousefi
Abstract
Mineral prospectivity mapping (MPM) is a multi-staged process aiming at delimiting exploration targets. Experts’ knowledge is an indispensable component of MPM, and might be required (i) while translating signature features of ore-forming processes into a suite of maps, namely evidence layers, ...
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Mineral prospectivity mapping (MPM) is a multi-staged process aiming at delimiting exploration targets. Experts’ knowledge is an indispensable component of MPM, and might be required (i) while translating signature features of ore-forming processes into a suite of maps, namely evidence layers, (ii) while assigning weights to evidence layers, and (iii) while interpreting maps of mineral prospectivity. The latter is important as MPM integrates weighted evidence layers into a continuous map of mineral prospectivity. Although high values in prospectivity maps pertain to prospective zones, maps of mineral prospectivity are devoid of interpretation. One, therefore, should adopt a classification scheme to categorize or prioritize exploration targets from a map of mineral prospectivity. In addition to previous frameworks applied for interpreting maps of mineral prospectivity, this paper introduces an optimization-based framework, the Gray Wolf Optimizer (GWO) algorithm, for addressing this problem. In addition to GWO, we also used percentile maps of 85, 90, and 95% for interpreting the results of our prospectivity model. These methods were applied to a fuzzy-based map of mineral prospectivity derived for the Alut area, NW Iran. Overall, the map derived by the GWO has involved more Au occurrences, 66% of explored Au occurrences by GWO versus 33% by percentile maps; also introduces more targets as high-potential zones of Au mineralization that may be neglected by traditional methods like percentile maps.
Exploration
Bashir Shokouh Saljoughi; Ardeshir Hezarkhani
Abstract
The porphyry Cu-mineralization potential area studied in this article is located in the southern section of the Central Iranian volcano–sedimentary complex, contains large number of mineral deposits, and occurrences that are currently facing a shortage of resources. Therefore, prospecting potential ...
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The porphyry Cu-mineralization potential area studied in this article is located in the southern section of the Central Iranian volcano–sedimentary complex, contains large number of mineral deposits, and occurrences that are currently facing a shortage of resources. Therefore, prospecting potential areas in the deeper and peripheral spaces has become a high priority in this region. Different direct and indirect methods try to predict promising areas for future explorations that most of them are very time-consuming and costly. The main goal of mineral prospecting is applying a transparent and robust approach for identifying high potential areas to be explored further in the future. This study presents the procedure taken to create two different Cu-mineralization prospectivity maps. This study aims to investigate the results of applying the ANN technique, and to compare them with the outputs of applying GEP method. The geo-datasets employed for creating evidential maps of porphyry Cu mineralization include solid geology map, alteration map, faults, dykes, airborne total magnetic intensity, airborne gamma-ray spectrometry data (U, Th, K and total count), and known Cu occurrences. Based on this study, the ANN technique (10 neurons in the hidden layer and LM learning algorithm) is a better predictor of Cu mineralization compared to the GEP method. The results obtained from the P-A plot showed that the ANN model indicates that 80% (vs. 70% for GEP) of the identified copper occurrences are projected to be present in only 20% (vs. 30% for GEP) of the surveyed area. The ANN technique due to capabilities such as classification, pattern matching, optimization, and prediction is useful in identifying anomalies associated with the Cu mineralization.
M. P. Sadr; M. Nazeri
Abstract
The Dolatabad area located in SE Iran is a well-endowed terrain owning several chromite mineralized zones. These chromite ore bodies are all hosted in a colored mélange complex zone comprising harzburgite, dunite, and pyroxenite. These deposits are irregular in shape, and are distributed as small ...
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The Dolatabad area located in SE Iran is a well-endowed terrain owning several chromite mineralized zones. These chromite ore bodies are all hosted in a colored mélange complex zone comprising harzburgite, dunite, and pyroxenite. These deposits are irregular in shape, and are distributed as small lenses along colored mélange zones. The area has a great potential for discovering further chromite resources. Therefore, the current work endeavors to delineate the favorable zones of podiform chromite mineralization to focus on the detailed exploration surveys. In order to achieve this goal, the machine learning random forests algorithm was adapted to integrate the footprints of mineralization in various exploration datasets. The genetic characteristics of podiform chromite deposits were used to define the exploration criteria. These defined criteria were then translated to a set of exploration evidence layers. The competent exploration evidence layers, i.e. those with remarkable positive spatial associations with mineralization, were then recognized using distance distribution analysis. Respecting the location of known chromite mineralizations and competent exploration evidence layers, a predictive random forests model was trained and then applied to predict the favorable zones of chromite prospectivity. The delineated targets were found to occupy 19% of the studied area, in which all the known chromite mineralizations were delimited. Consequently, it is worthy to follow up the detailed exploration surveys within the delineated zones.
Exploitation
S. Saadat
Abstract
Motivated by the recent successful results of using GIS modeling in a variety of problems related to the geosciences, some knowledge-based methods were applied to a regional scale mapping of the mineral potential, special for Cu-Au mineralization in the Feyz-Abad area located in the NE of Iran. Mineral ...
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Motivated by the recent successful results of using GIS modeling in a variety of problems related to the geosciences, some knowledge-based methods were applied to a regional scale mapping of the mineral potential, special for Cu-Au mineralization in the Feyz-Abad area located in the NE of Iran. Mineral Prospectivity Mapping (MPM) is a multi-step process that ranks a promising target area for more exploration. In this work, five integration methods were compared consisting of fuzzy, continuous fuzzy, index overlay, AHP, and fuzzy AHP. For this purpose, geological maps, geochemical samples, and geophysics data were collected, and a spatial database was constructed. ETM + images were used to extract the hydroxyl and iron-oxide alterations, and to identify the linear and fault structures and prospective zones in regional scale; ASTER images were used to extract SiO2 index, kaolinite, chlorite, and propylitic alterations in a district scale. All the geological, geochemical, and geophysical data was integrated for MPM by different analysis. The values were determined by expert knowledge or logistic functions. Based upon this analysis, three main exploration targets were recognized in the Feyz-Abad district. Based on field observation, MPM was proved to be valid. The prediction result is accurate, and can provide directions for future prospecting. Among all the methods evaluated in this work, which tend to generate relatively similar results, the continuous fuzzy model seems to be the best fit in the studied area because it is bias-free and can be used to generate reliable target areas.
