Exploration
Ahmed Ali Madani
Abstract
Innovation in mineral exploration occurs either in the construction of new ore deposit models or the development of new techniques used to locate the ore deposits. Band ratio is the image processing technique developed for mineral exploration. The present study presents a new approach used to evaluate ...
Read More
Innovation in mineral exploration occurs either in the construction of new ore deposit models or the development of new techniques used to locate the ore deposits. Band ratio is the image processing technique developed for mineral exploration. The present study presents a new approach used to evaluate the band ratio technique for discrimination and prediction of the Iron-Titanium mineralization exposed in the Khamal area, Western Saudi Arabia using the ensemble Random Forest model (RF) and SPOT-5 satellite data. SPOT-5 band ratio images are prepared and used as the explanatory variables. The target variable is prepared in which (70%) of the target locations are used for training and the rest are for validation. A confusion matrix and the precision-recall curves are constructed to evaluate the RF model performance and the Receiver Operating Characteristics curves (ROC) are used to rank the band ratio images. Results revealed that the 3/1, 2/1 & 3/2 band ratio images show excellent discrimination with AUC values of 0.986, 0.980 & 0.919 respectively. The present study successfully selects the 3/1 band ratio image as the best classifier and presents a new Fe-Ti mineralization image map. The present study proved the usefulness of the Random Forest classifier for the prediction of the Fe-Ti mineralization with an accuracy of 97%.
Exploration
Ali Aalianvari; shirin Jahanmiri; malihehe Abbaszadeh
Abstract
Groundwater inflow is a critical subject within the domains of hydrology, hydraulic engineering, hydrogeology, rock engineering, and related disciplines. Tunnels excavated below the groundwater table, in particular, face the inherent risk of groundwater seepage during both the excavation process and ...
Read More
Groundwater inflow is a critical subject within the domains of hydrology, hydraulic engineering, hydrogeology, rock engineering, and related disciplines. Tunnels excavated below the groundwater table, in particular, face the inherent risk of groundwater seepage during both the excavation process and subsequent operational phases. Groundwater inflows, often perceived as rare geological hazards, can induce instability in the surrounding rock formations, leading to severe consequences such as injuries, fatalities, and substantial financial expenditures. The primary objective of this research is to explore the application of machine learning techniques to identify the most accurate method of forecasting tunnel water seepage. The prediction of water loss into the tunnel during the forecasting phase employed a tree equation based on gene expression programming (GEP). These results were compared with those obtained from a hybrid model comprising particle swarm optimization (PSO) and artificial neural networks (ANN). The Whale Optimization Algorithm (WOA) was selected and developed during the optimization phase. Upon contrasting the aforementioned methods, the Whale Optimization Algorithm demonstrated superior performance, precisely forecasting the volume of water lost into the tunnel with a correlation coefficient of 0.99. This underscores the effectiveness of advanced optimization techniques in enhancing the accuracy of groundwater inflow predictions and mitigating potential risks associated with tunneling activities.
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 ...
Read More
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.
Exploration
Sepideh Ghasemi; Ali Imamalipur; Samaneh Barak
Abstract
This investigation centers on the Qarah Tappeh copper deposit, situated in the northern region of West Azerbaijan province, approximately 15 kilometers northeast of Maku city. The primary objective of the study is to comprehensively examine the study area through the analysis of 253 lithogeochemical ...
Read More
This investigation centers on the Qarah Tappeh copper deposit, situated in the northern region of West Azerbaijan province, approximately 15 kilometers northeast of Maku city. The primary objective of the study is to comprehensively examine the study area through the analysis of 253 lithogeochemical samples, and assessing reserves utilizing ordinary kriging, guided by subsurface data obtained from 14 boreholes totaling 909.2 meters. The concentration–volume (C–V) multifractal modeling approach was employed to estimate the deposit's reserve. The findings of this research project indicate an estimated 988,604 tons of the deposit with an average grade of 0.14%. Through the analysis of log–log plots within the C–V relationship, threshold values signifying various copper (Cu) concentrations were identified. These plots revealed a pronounced power-law correlation between Cu concentrations and their corresponding volumes, with arrows denoting four specific threshold values. Utilizing this analytical methodology, mineralized zones were classified into five distinct categories: high (>0.42%), above-average (0.35-0.42%), average (0.27-0.35%), below-average (0.14-0.27%), and low (<0.14%) mineralized zones.
Exploration
Hossein Mahdiyanfar; Mirmahdi Seyedrahimi-Niaraq
Abstract
In this investigation, the hybrid approach of wavelet transforms and fractal method named Wavelet-Fractal model has been utilized for geochemical contamination mapping as a novel application. For this purpose, the distribution maps of pollutant elements were transformed to the position-scale domain using ...
Read More
In this investigation, the hybrid approach of wavelet transforms and fractal method named Wavelet-Fractal model has been utilized for geochemical contamination mapping as a novel application. For this purpose, the distribution maps of pollutant elements were transformed to the position-scale domain using two-dimensional discrete wavelet transformation (2DDWT). The Symlet2 and Haar mother wavelets were applied for two-dimensional signal analysis of elemental concentrations of As, Pb, and Zn based on soil samples taken from the Irankuh mining district, Central Iran. The Symlet2 and Haar wavelet coefficients of approximate and detail components were obtained at one level frequency decomposition using 2DDWT. The wavelet coefficients of approximate component (WCAC) were modeled using a fractal method for delineating the geochemical contamination populations of toxic elements. Based on the results of wavelet-fractal models, the As, pb, and Zn were classified into three and four populations. Two areas contaminated with metals have been found in the district. These areas are within the limit of mining operations and its surroundings. The wavelet-fractal proposed model has been able to separate environmental areas contaminated with toxic metals accurately. Anomalously intense pollution has spread to one kilometer outside the mining operation limit. This dispersion in the case of Pb and Zn elements is well seen in the geochemical map prepared with the Haar class.
Exploration
Babak Sohrabian; Erhan Tercan
Abstract
Mineral Resources have commonly been estimated through the kriging method that assigns weights to the samples based on variogram distance to the estimation point without considering their values. More robust estimators such as spatial copulas are promising tools because they consider both distance ...
Read More
Mineral Resources have commonly been estimated through the kriging method that assigns weights to the samples based on variogram distance to the estimation point without considering their values. More robust estimators such as spatial copulas are promising tools because they consider both distance and sample values in determining weights. The purpose of this study is to demonstrate the effectiveness of the Gaussian copulas (GC) by estimating the copper grade values in the Sungun porphyry copper deposit located in Iran. Performance of the method was compared to ordinary kriging (OK) and indicator kriging (IK) by running the Jackknife test of cross-validation. The metrics used in measuring performance of the methods are global accuracy and precision of the distribution of the estimates, error statistics, and variability for globally accurate and precise estimates. The case study shows advantages of GC over OK and IK by producing globally accurate and precise estimates with acceptable error statistics and variability.
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 ...
Read More
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
Rashed Pourmirzaee; Hadi Jamshid Moghaddam
Abstract
In recent years, hyperspectral data have been widely used in earth sciences because these data provide accurate spectral information of the earth's surface. This research aims to apply match filtering (MF) on Hyperion hyperspectral imagery for mapping alteration mineral in the Astarghan area, NW Iran. ...
Read More
In recent years, hyperspectral data have been widely used in earth sciences because these data provide accurate spectral information of the earth's surface. This research aims to apply match filtering (MF) on Hyperion hyperspectral imagery for mapping alteration mineral in the Astarghan area, NW Iran. Astarghan is located in the northwest of Iran where deposits of low-sulfide gold-bearing ore rocks occur as veins and stockworks. Therefore, at first, the Astarghan Hyperion scene was topographically and atmospherically corrected. Then, the data quality was surveyed to recognize bad bands and improve the accuracy of the subsequent processing steps. In MF analysis, it is a challenge to separate MF abundance images to target and background pixels. Therefore, to cope with this challenge, a moving threshold technique is proposed. The results indicated three indicative minerals including kaolinite, opal and jarosite. Then, the results were statistically verified by virtual verification and geological data. The verification was performed virtually using United States Geological Survey (USGS) spectral library data, which showed an agreement of 78.06%. Moreover, a comparison of the MF analysis results showed a good agreement with field investigations and overlaying with a detailed geological map of the study area. Finally, in this study the X-ray diffraction (XRD) of three indicative mineral samples was used to check the efficiency of the applied method.
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, ...
Read More
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.
Exploration
Prince Ofori Amponsah; Eric Dominic Forson
Abstract
This study was set out to delineate prospective zones of gold mineralization occurrence over the Julie tenement of Northwestern Ghana using two spatial statistical techniques, namely information value (IV) and weight of evidence (WofE) models. First, 110 locations, where gold (Au) mineralization has ...
Read More
This study was set out to delineate prospective zones of gold mineralization occurrence over the Julie tenement of Northwestern Ghana using two spatial statistical techniques, namely information value (IV) and weight of evidence (WofE) models. First, 110 locations, where gold (Au) mineralization has been observed, were identified by field survey results derived from highly anomalous geo-chemical assay datasets. Of these 110 locations, 77 (representing 70% of the known locations, where gold has been observed) were randomly selected for training the aforementioned models, and the remaining 33 (analogous to 30% of the known Au occurrence) were used for validation. Secondly, eleven mineral conditioning factors (evidential layers) comprising analytic signal, reduction-to-equator (RTE), lineament density (LD), porphyry density, potassium concentration, thorium concentration, uranium concentration, potassium-thorium ratio, uranium-thorium ratio, geology, and arsenic concentration layers were sourced from geo-physical, geological, and geo-chemical datasets. Subsequently, by synthesizing these eleven evidential layers using the two spatial statistical techniques, two mineral prospectivity models were created in a geographic information system (GIS) environment. Finally, the mineral prospectivity models produced were validated using the area under the receiver operating characteristics curve (AUC). The results obtained showed that the IV model produced had a higher prediction accuracy in comparison with the mineral predictive model produced by the WofE with their AUC scores being 0.751 and 0.743, respectively.
Exploration
Kaustubh Sinha; Priyangi Sharma; Anurag Sharma; Kanwarpreet Singh; Murtaza Hassan
Abstract
In this expansive study, a thorough analysis of land subsidence in the Joshimath area has been conducted, exercising remote sensing (RS) and Geographic Information System (Civilians) tools. The exploration encompasses colourful pivotal parameters, including Annual Rainfall, Geology, Geomorphology, and ...
Read More
In this expansive study, a thorough analysis of land subsidence in the Joshimath area has been conducted, exercising remote sensing (RS) and Geographic Information System (Civilians) tools. The exploration encompasses colourful pivotal parameters, including Annual Rainfall, Geology, Geomorphology, and Lithology, rounded by the integration of different indicators. Joshimath, a fascinating city nestled within the rugged geography of the Indian state of Uttarakhand, stands out for its unique geographical features and its vulnerability to environmental vulnerabilities. The disquisition is carried out with the backing of ArcMap software, a technical Civilians tool, while exercising data sourced from the recognized Indian Space Research Organisation (ISRO) and the National Remote seeing Centre (NRSC). This comprehensive approach aims to give inestimable perceptivity into the dynamic processes associated with land subsidence in the region, offering critical data for disaster mitigation strategies and sustainable land operation in the area. It's noteworthy that the region endured a significant case of land subsidence in late December 2022, emphasizing the punctuality and applicability of this study. This event not only emphasizes the urgency of comprehending land subsidence in Joshimath but also underscores the necessity for ongoing monitoring and mitigation sweats. The integration of these different data sources and logical ways promises to enhance the understanding of land subsidence dynamics and inform decision- makers in the pursuit of flexible and sustainable land use practices in Joshimath and other also vulnerable regions.
Exploration
Ashraf Ismael; Abdelrahem Khalefa Embaby; Faissal Ali; Hussin Farag; Sayed Gomaa; Mohamed Elwageeh; Bahaa Mousa
Abstract
The mineral resource estimation process necessitates a precise prediction of the grade based on limited drilling data. Grade is crucial factor in the selection of various mining projects for investment and development. When stationary requirements are not met, geo-statistical approaches for reserve estimation ...
Read More
The mineral resource estimation process necessitates a precise prediction of the grade based on limited drilling data. Grade is crucial factor in the selection of various mining projects for investment and development. When stationary requirements are not met, geo-statistical approaches for reserve estimation are challenging to apply. Artificial Neural Networks (ANNs) are a better alternative to geo-statistical techniques since they take less processing time to create and apply. For forecasting the iron ore grade at El-Gezera region in El- Baharya Oasis, Western Desert of Egypt, a novel Artificial Neural Network (ANN) model, geo-statistical methods (Variograms and Ordinary kriging), and Triangulation Irregular Network (TIN) were employed in this study. The geo-statistical models and TIN technique revealed a distinct distribution of iron ore elements in the studied area. Initially, the tan sigmoid and logistic sigmoid functions at various numbers of neurons were compared to choose the best ANN model of one and two hidden layers using the Levenberg-Marquardt pure-linear output function. The presented ANN model estimates the iron ore as a function of the grades of Cl%, SiO2%, and MnO% with a correlation factor of 0.94. The proposed ANN model can be applied to any other dataset within the range with acceptable accuracy.
Exploration
Irshad Khan; Afayou Afayou; Naeem Abbas; Asghar Khan; Numan Alam; Kausar Sultan Shah
Abstract
The study utilizes the Limit Equilibrium Method (LEM) to investigate slope movements. These movements were initially generated by construction activities at the slope's base, and subsequent events were driven by seismic activities, as the study studied area lies within the Main Karakoram Thrust (MKT) ...
Read More
The study utilizes the Limit Equilibrium Method (LEM) to investigate slope movements. These movements were initially generated by construction activities at the slope's base, and subsequent events were driven by seismic activities, as the study studied area lies within the Main Karakoram Thrust (MKT) and Main Mantle Thrust (MMT) zones. Soil samples, characterized by a moisture content of 13% and a dry unit weight of 18.14 kN/m³ were analyzed. The study revealed that an increase in saturation caused by rainwater infiltration, resulted in a reduction in unconfined compression strength, decreasing from 712 kPa to 349 kPa. The shear strength and deformation parameters (cohesion, angle of internal friction, and deformation modulus) were also examined with varied degrees of saturation. The results revealed a decrease in these parameters as the percentage of saturation increased from 30% to 90%. The slope stability study revealed that the Factor of Safety (FOS) reduced from 1.85 to 0.86 as the saturation of the material raised from 30% to 90%. To assess the influence of unit weight, cohesion, and angle of internal friction on the FOS, multiple cases were considered. The analysis revealed that the FOS increased with higher cohesion and angle of internal friction, while an increase in unit weight resulted in a lower factor of safety. Furthermore, stability of the slope was evaluated by modifying the slope geometry such as lowering the height. According to the GeoStudio investigation, the slope remained steady even at saturation levels exceeding 80%.
Exploration
Jairo Jhonatan Marquina Araujo; Marco Antonio Cotrina Teatino; José Nestor Mamani Quispe; Eduardo Manuel Noriega Vidal; Juan Antonio Vega Gonzalez; Juan Vega-Gonzalez; Juan Cruz-Galvez
Abstract
The objective of this research work to employ machine learning techniques including Multilayer Perceptron Artificial Neural Networks (ANN-MLP), Random Forests (RFs), Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR) to predict copper ore grades in a copper deposit located in Peru. ...
Read More
The objective of this research work to employ machine learning techniques including Multilayer Perceptron Artificial Neural Networks (ANN-MLP), Random Forests (RFs), Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR) to predict copper ore grades in a copper deposit located in Peru. The models were developed using 5654 composites containing available geological information (rock type), as well as the locations of the samples (east, north, and altitude) and secondary ore grade (Mo) obtained from drilling wells. The data was divided into 10% (565 composites) for testing, 10% (565 composites) for validation, and 80% (4523 composites) for training. The evaluation metrics included SSE (Sum of Squared Errors), RMSE (Root Mean Squared Error), NMSE (Normalized Mean Squared Error), and R² (Coefficient of Determination). The XGBoost model could predict the ore grade with an SSE of 15.67, RMSE = 0.17, NMSE = 0.34, and R² = 0.66, the RFs model with an SSE of 16.40, RMSE = 0.17, NMSE = 0.36, and R² = 0.65, the SVR model with an SSE of 19.94, RMSE = 0.19, NMSE = 0.43, and R² = 0.57, and the ANN-MLP model with an SSE = 21.00, RMSE = 0.19, NMSE = 0.46, and R² = 0.55. In conclusion, the XGBoost model was the most effective in predicting copper ore grades.
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 ...
Read More
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
Naman Chandel; Sushindra Kumar Gupta; Anand Kumar Ravi
Abstract
Groundwater is an essential resource for human survival, but its quality is often degraded by the human activities such as improper disposal of waste. Leachate generated from landfill sites can contaminate groundwater, causing severe environmental and health problems. Machine learning techniques can ...
Read More
Groundwater is an essential resource for human survival, but its quality is often degraded by the human activities such as improper disposal of waste. Leachate generated from landfill sites can contaminate groundwater, causing severe environmental and health problems. Machine learning techniques can be used to predict groundwater quality and leachate characteristics to manage this issue efficiently. This study proposes a machine learning-based model for the prediction of groundwater quality and leachate characteristics using the effective water quality index (EWQI). The leachate dataset used in this study was obtained from a landfill site, and the groundwater quality dataset was collected from literature review. The mean values of TDS, Ca, Mg, NO3-, and PO4- exceeded the prescribed limit for drinking water purposes. The proposed model utilizes a machine learning architecture based on a convolutional neural network (CNN) to extract relevant features from the input data. The extracted features are then fed into a fully connected network to estimate the EWQI of the input samples. The model, trained and tested on leachate and groundwater quality datasets, achieves a high accuracy and computational efficiency, aiding in predicting groundwater quality and leachate characteristics for waste management.
Exploration
Mojtaba Bazargani Golshan; Mehran Arian; Peyman Afzal; Lili Daneshvar Saein; Mohsen Aleali
Abstract
The aim is to use the Concentration-Volume (C-V) fractal model to identify high-quality parts of coal seams based on sulfur and ash concentrations. In the K1 and K7 coal seams in the North Kochakali coal deposit, 5 and 6 different populations of ash and sulfur content were obtained based on the results. ...
Read More
The aim is to use the Concentration-Volume (C-V) fractal model to identify high-quality parts of coal seams based on sulfur and ash concentrations. In the K1 and K7 coal seams in the North Kochakali coal deposit, 5 and 6 different populations of ash and sulfur content were obtained based on the results. According to this model, sulfur and ash concentrations below 1.81% and 33.1% for the K7 seam, and below 4.46% and 37.1% for the K1 seam, respective base on Russian standard for ash and high sulfur content of North Kochakali coals were considered as appropriate values. In order to identify the high-quality parts of K1 and K7 coal seams, plans at different depths were used based on the C-V fractal model. Plans at different depths suggests that the southern part of the K1 seam and the northern part of the K7 seam have the highest-quality based on sulfur and ash concentrations, which should be considered in the extraction operation. The logratio matrix was used to compare the results of the C-V fractal model with the geological data of pyrite veins and coal ash. This matrix indicates that sulfur content above 3.8% for the K7 seam and above 4.41% for the K1 seam have good and very good correlation with pyritic veins of geological data, respectively. There are good overall accuracy (OA) values in the correlation between parts of the seam with ash concentration above 37.1% and 45.7% for the K1 and K7 seams, respectively, and the coal ash obtained from the geological data.
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, ...
Read More
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
Khadijeh Validabadi Bozcheloei; Majid Hashemi Tangestani
Abstract
Evaporites are sediments that chemically precipitate due to the evaporation of an aqueous solution. Most evaporite formations, in addition to evaporite minerals, include detrital rocks such as mudstone, marl, and siltstone. Principal Component Analysis (PCA), Directed Principal Component Analysis (DPCA), ...
Read More
Evaporites are sediments that chemically precipitate due to the evaporation of an aqueous solution. Most evaporite formations, in addition to evaporite minerals, include detrital rocks such as mudstone, marl, and siltstone. Principal Component Analysis (PCA), Directed Principal Component Analysis (DPCA), and Band Ratio methods were applied to Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) data for mapping the Gachsaran evaporite formation and distinguishing its lithological units in the Masjed Soleiman oil field, located in southwestern Iran. This oil field was the first recognized oil field in the Middle East. Colour composites of PCs 4, 5, and 2, as RGB images, effectively discriminated this formation from other sedimentary formations. The gypsum spectrum, resampled to the 9 band centres of ASTER, exhibited reflectance in bands 4 and 8 and absorption in bands 6 and 9. As a result, these bands were selected for DPCA application. PC4 effectively highlighted gypsum outcrops as bright pixels, while the band ratio 2/1 accentuated ferric iron, appearing as bright pixels, which correlated with the red marls. The results of this study demonstrate that ASTER image processing is a cost- and time-effective method that can be utilized for mapping evaporite formations and distinguishing their lithological units.
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 ...
Read More
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.
Exploration
Seyyed Saeed Ghannadpour; Morteza Hasiri; Hadi Jalili; Somayeh Talebiesfandarani
Abstract
The Zafarghand area (as a porphyry Cu deposit) is located in the northeast of Isfahan and southeast of Ardestan, which is a part of the Iran-Central structural zone, and more precisely, it is located in the Urmia-Dokhtar volcanic belt. In the porphyry Cu deposits exploration, identifying and determining ...
Read More
The Zafarghand area (as a porphyry Cu deposit) is located in the northeast of Isfahan and southeast of Ardestan, which is a part of the Iran-Central structural zone, and more precisely, it is located in the Urmia-Dokhtar volcanic belt. In the porphyry Cu deposits exploration, identifying and determining the alteration zones is of special importance. The aim of the present study is to identify and highlight the alteration zones of Zafarghand area, with the help of the U-statistic method in the processing of ASTER sensor satellite images. Accordingly, considering the raster nature and digital form of satellite images, the digital number values of each pixel from the image matrices were considered as samples in a systematic network. Finally, the U spatial statistic algorithm was implemented as a moving window algorithm for determining anomaly samples in the set of digital number (DN) values of ASTER satellite image pixels. The results of this technique show that the application of the U-statistic method, considering its structural nature and neighboring samples in decision-making, has been successful and has proven to be very effective in determining the alteration zones in the Zafarghand area.
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 ...
Read More
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.
Exploration
Ajay Kumar
Abstract
Land use (LU) classification based on remote sensing images is a challenging task that can be effectively addressed using a learning framework. However, accurately classifying pixels according to their land use poses a significant difficulty. Despite advancements in feature extraction techniques, the ...
Read More
Land use (LU) classification based on remote sensing images is a challenging task that can be effectively addressed using a learning framework. However, accurately classifying pixels according to their land use poses a significant difficulty. Despite advancements in feature extraction techniques, the effectiveness of learning algorithms can vary considerably. In this study conducted in Talcher, Odisha, India, the researchers proposed the use of Artificial Neural Networks (ANNs) to classify land use based on a dataset collected by the Sentinel-2 satellite. The study focused on the Talcher region, which was divided into five distinct land use classes: coal area, built-up area, barren area, vegetation area, and waterbody area. By applying ANNs to the mining region of Talcher, the researchers aimed to improve the accuracy of land use classification. The results obtained from the study demonstrated an overall accuracy of 79.4%. This research work highlights the importance of utilizing remote sensing images and a learning framework to address the challenges associated with pixel-based land use classification. By employing ANNs and leveraging the dataset from the Sentinel-2 satellite, the study offers valuable insights into effectively classifying different land use categories in the Talcher region of India. The findings contribute to the advancement of techniques for accurate land use analysis, with potential applications in various fields such as urban planning, environmental monitoring, and resource management.
Exploration
Eman M. Kamel; Mohamed S.H. Hammed; Osama E.A. Attia
Abstract
In the recent years, the use of ASTER and Landsat data have become prevalent for mapping different types of rock formations. Specifically, this study utilizes ASTER (L1B) and Landsat 8 (AOL) images to map outcrops of various gypsum facies in Ras Malaab area of west-central Sinai. These gypsum facies ...
Read More
In the recent years, the use of ASTER and Landsat data have become prevalent for mapping different types of rock formations. Specifically, this study utilizes ASTER (L1B) and Landsat 8 (AOL) images to map outcrops of various gypsum facies in Ras Malaab area of west-central Sinai. These gypsum facies are part of a lithostratigraphic group called Ras Malaab, estimated to have been formed during the Miocene period. A range of image processing techniques was employed to create the final facies map including quartz and sulphate indices, composite image band combinations, band ratios, principal component analyses, decorrelation stretching, and SAM mapping followed by supervised classification. By using band combinations, mineral indices, and principal component analyses, sulphate minerals were distinguished from their surroundings. Additionally, decorrelation stretches and band ratios were used to differentiate between primary, secondary, faulted gypsum, anhydrite, and carbonates. The SAM rapid mapping algorithm was also an effective tool to distinguish between the main facies in the studied area and to differentiate between primary massive and bedded gypsum. The results of this study were summarized by creating a facies map of the area using supervised classification, which, in addition to petrographic studies, greatly aided in understanding the distribution of the different gypsum facies.
Exploration
Abdelrahem Khalefa Embaby; Sayed Gomaa; Yehia Darwish; Samir Selim
Abstract
This study aims to develop an empirical correlation model for estimating the uranium content of the G-V in the Gabal Gattar area, northeastern desert of Egypt, as a function of the thorium content and the total gamma rays. Using the recent MATLAB software, the effect of selecting tan-sigmoid as a transfer ...
Read More
This study aims to develop an empirical correlation model for estimating the uranium content of the G-V in the Gabal Gattar area, northeastern desert of Egypt, as a function of the thorium content and the total gamma rays. Using the recent MATLAB software, the effect of selecting tan-sigmoid as a transfer function at various numbers of hidden neurons was investigated to arrive at the optimum Artificial Neural Network (ANN) model. The pure-linear function was investigated as the output function, and the Levenberg-Marquardt approach was chosen as the optimization technique. Based on 1221 datasets, a novel ANN-based empirical correlation was developed to calculate the amounts of uranium (U). The results show a wide range of uranium content, with a determination coefficient (R2) of about 0.999, a Root Mean Square Error (RMSE) equal to 0.115%, a Mean Relative Error (MRE) of -0.05%, and a Mean Absolute Relative Error (MARE) of 0.76%. Comparing the obtained results with the field investigation shows that the suggested ANN model performed well.
