Exploration
Kamran Mostafaei; Mohammad Nabi Kianpour; Mahyar Yousefi; Meisam Saleki
Abstract
Discrimination of geochemical anomalies from background is a challenge in that elemental dispersion patterns are affected by a variety of geological factors, which vary from one to another area. While statistical and fractal methods are commonly employed for anomaly detection, they struggle with selecting ...
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Discrimination of geochemical anomalies from background is a challenge in that elemental dispersion patterns are affected by a variety of geological factors, which vary from one to another area. While statistical and fractal methods are commonly employed for anomaly detection, they struggle with selecting optimal thresholds. This study proposes the Grey Wolf Optimizer (GWO) algorithm as a novel approach for identifying the optimal boundary between anomalies and background. Stream sediment geochemical data from a copper-mineralized area of the Sarduiyeh-Baft sheets in southeast Iran were utilized for analysis. The Geochemical Mineralization Probability Index (GMPI) was first calculated for Cu-Au, Mo-As, Pb-Zn, and porphyry distributions. Subsequently, fractal methods were used to identify anomalous populations within each GMPI. The GWO algorithm was then applied to these distributions to determine the optimal thresholds. Risk analysis, calculated as the ratio of covered copper occurrences to the covered area, revealed superior reliability for the GWO-derived limit compared to those obtained using fractal methods. For porphyry GMPI values, while the fractal reliability indices are 0.127, 0.44, and 0.5, the GWO limit achieved a value of 0.66. Risk analysis for Cu-Au distribution also caused more desired result for GWO limit rather that fractal ones. This demonstrates the enhanced performance and superior reliability of the GWO algorithm for optimizing anomaly detection thresholds in GMPI data.
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.
Mahyar Yousefi; Samaneh Barak; Amir Salimi; Saeed Yousefi
Abstract
In this paper, we discuss the concepts behind dispersion patterns of geochemical anomalies when applied for prospecting mineral deposits in different exploration scales. The patterns vary from regional to local scale geochemical surveys, which is due to the differences in the corresponding underlying ...
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In this paper, we discuss the concepts behind dispersion patterns of geochemical anomalies when applied for prospecting mineral deposits in different exploration scales. The patterns vary from regional to local scale geochemical surveys, which is due to the differences in the corresponding underlying processes. Thus the ways for modelling the dispersion patterns and driving significant geochemical signatures should consider the variety when the area under study are delimited from regional to deposit scales exploration. Subsequently, this paper faces with two questions, namely (1) should various geochemical indicators be integrated in different exploration scales aiming at introducing stronger signatures of mineral deposits? and (2) how does the exploration scale affect dispersion patterns of geochemical indicator elements? We demonstrate that the exploration scale plays an important role on the reliability and usefulness of geochemical anomaly models. In this regard, although fusion may achieve reputable outcomes at regional scale exploration, we demonstrate that integration doesn’t gain accurate results for exploration at local scale, which is due to the diversities of the elemental distributions in the two different scales. This achievement is approved by comparing two geochemical signatures, one obtained by integration of two different indicator factors and the other one that used a single factor. The former produces almost the whole studied area as prospective, while the later recognizes ~10% of the area for further exploration, which is closely related to the porphyry Cu mineralization and is verified by drilling results.
E. Bahri; A. Alimoradi; M. Yousefi
Abstract
There are different exploration methods, each of which may introduce a number of promising exploration targets. However, due to the financial and time constraints, only a few of them are selected as the exploration priorities. Instead of the individual use of any exploration method, it is common to integrate ...
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There are different exploration methods, each of which may introduce a number of promising exploration targets. However, due to the financial and time constraints, only a few of them are selected as the exploration priorities. Instead of the individual use of any exploration method, it is common to integrate the results of different methods in an interdependent framework in order to recognize the best targets for further exploration programs. In this work, the continuously-weighted evidence maps of proximity to intrusive contacts, faults density, and stream sediment geochemical anomalies of a set of porphyry copper deposits in the Jiroft region of the Kerman Province in Iran are first generated using the logistic functions. The weighted evidence maps are then integrated using the union score integration function in order to model the deposit type in the studied area. The weighting and integration approaches applied avoid the disadvantages of the traditional methods in terms of carrying the bias and error resulting from the weighting procedure. Evaluation of the ensuing prospectivity model generated demonstrate that the prediction rate of the model is acceptable, and the targets generated are reliable to follow up the exploration program in the studied area.
Exploitation
P. Afzal; M. Yusefi; M. Mirzaie; E. Ghadiri-Sufi; S. Ghasemzadeh; L. Daneshvar Saein
Abstract
The aim of this work was to delineate the prospects of podiform-type chromite by staged factor analysis and geochemical mineralization prospectivity index in Balvard area, SE Iran. The stream sediment data and fault density were used as the exploration features for prospectivity modeling in the studied ...
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The aim of this work was to delineate the prospects of podiform-type chromite by staged factor analysis and geochemical mineralization prospectivity index in Balvard area, SE Iran. The stream sediment data and fault density were used as the exploration features for prospectivity modeling in the studied area. In this regard, two continuous fuzzified evidence layers were generated and integrated using fuzzy operator. Then fractal modeling was used for defuzzification of the prospectivity model obtained. Furthermore, the prediction-area plot was used for evaluation of the predictive ability of the generated target areas. The results obtained showed that using the prospectivity model, 82% of mineral occurrences was predicted in 18% of the studied area. In addition, the target areas were correlated with the geological particulars including ultrabasic and serpentinization rocks, the host rocks of the podiform-type chromite deposit type.
