International Journal of Mining & Geo-Engineering
Volume:57 Issue: 4, Autumn 2023

This study serves to demonstrate the application of geophysical data interpretation in order to recognize the structural features related to the mineralization. The aforementioned structures generate the evidence layers, which undergo augmentation and enhancement processes. This results in the production of evidence layers that, when integrated with other geo-exploration evidence layers, contribute to the delineation of mineral exploration targets with increased reliability. In this study, the utilization of aeromagnetic and radiometric data is illustrated for the recognition of structural features and host rocks associated with orogenic gold mineral systems. Furthermore, the integration of geophysical data interpretation, specifically the identification of mineralization-related features, with alterations and the geochemical signature of mineralization is demonstrated. This integration facilitates the delimitation of exploration targets with improved dependability.

The different methods for delineating favorable areas for mineral exploration utilize exploration criteria regarding targeted mineral deposits. The criteria are elicited according to conceptual model parameters of the targeted mineral deposits. The selection of indicator criteria, the evaluation of their comparative importance, and their integration are critical in mineral prospectivity modelling. In data-driven methods, indicator features are weighted using functions whereby the importance of certain indicator criteria may be ignored. In this paper, a data-driven method is described for recognizing and converting exploration criteria into quantitative coefficients representing favorability for the presence of the targeted mineral deposits. In this approach, all indicator features of the targeted mineral deposits are recognized and incorporated in the modelling procedure. The method is demonstrated for outlining favorable areas for a Mississippi valley-type fluorite deposit in an area, north of Iran. The method is developed by studying and modelling the geological characteristics of known mineral occurrences. The degree of prediction ability of each exploration criterion is quantified as a recognition coefficient, which can be used as a weight attributed to the criterion in mineral exploration targeting to outline favorable areas.

This research presents a case study that employs the Fuzzy Ordered Weighted Averaging (FOWA) method to develop mineral prospectivity/potential maps (MPM) for the Chahargonbad district in southeastern Iran. The primary objective of the study is to uncover intricate and concealed relationships between various evidence layers and known ore occurrences through a comprehensive analysis of multi-disciplinary geospatial data. Consequently, thirteen evidence layers were meticulously derived from existing databases, encompassing geological, geochemical, geophysical, and remote sensing data, which were then integrated using the FOWA multi-criteria decision-making approach to delineate favorable zones for porphyry Cu mineralization.The FOWA methodology employs a diverse array of decision strategies to synthesize input geospatial evidence by incorporating multiple values for an alpha parameter. This parameter serves as the cornerstone of the algorithm, influencing experts' perspectives on MPM risk. The methodology generates seven mineral potential maps to identify the most suitable one(s). By considering a prediction-area plot for data-driven weight assignment to each evidence map, the hybrid FOWA outputs were scrutinized to pinpoint the most appropriate map for targeting significant Cu occurrences. The resulting synthesized evidence map indicates an ore prediction rate of 77%, with known Cu deposits primarily located within favorable zones occupying 23% of the entire district area.

The Mineral Prospectivity Map (MPM) is a powerful tool for identifying target areas for the exploration of undiscovered mineral deposits. In this study, a knowledge-driven Index overlay technique was utilized to create the MPM on a regional scale. The complex distribution patterns of geological features associated with mineral deposits were mapped and correlations between these features and mineral deposits were revealed by integrating geological, geophysical, hydrothermal alteration, and fault density data layers. It was found that 23% of the study area was highly prospective, with 77% of the known porphyry copper occurrences located within this area. The normalized density was equal to 3.35, indicating a significant relationship between the known porphyry copper occurrences and their occupied area. The MPM also identified potential tracts outside the known mineralized areas that can be used for exploration and quantitative assessment of undiscovered resources. It is suggested that the MPM is a valuable tool for mineral exploration and could have significant implications for the mining industry.

Potential mapping of Permeability is a crucial factor in determining the productivity of an oil and gas reservoirs. Accurately estimating permeability is essential for optimizing production and reducing operational costs. In this study, we utilized the CUDNNLSTM algorithm to estimate reservoir permeability. The drilling core data were divided into a training pool and a validation pool, with 80% of the data used for training and 20% for validation. Based on the high variation permeability along the formation, we developed the CUDNNLSTM algorithm for estimating permeability. First, due to the highly dispersed signals from the sonic, density, and neutron logs, which are related to permeability, we adjusted the algorithm to train for 1000 epochs. However, once the validation loss value reached 0.0158, the algorithm automatically stopped the training process at epoch number 500. Within 500 epochs of the algorithm, we achieved an impressive accuracy of 98.42%. Using the algorithm, we estimated the permeabilities of the entire set of wells, and the results were highly satisfactory. The CUDNNLSTM algorithm due to the large number of neurons and the ability to solve high-order equations on the GPU is a powerful tool for accurately estimating permeability in oil and gas reservoirs. Its ability to handle highly dispersed signals from various logs makes it a valuable asset in optimizing production and reducing operational costs, because it is much cheaper than the cost of core extraction and has very high accuracy.

Identification of geochemical anomalies is a critical task in mineral exploration targeting. Decades of research and technology have resulted in new algorithms and techniques for recognizing anomaly detection methods at various scales and sample media. However, algorithms cannot always reveal the true nature of geological processes. The mineral system concept may contribute to a better understanding of the geological processes required to form and preserve ore deposits at all spatial and temporal scales. The mineral systems concept investigates the geochemical processes occurring within mineral subsystems in soil samples from the porphyry prospect area. The Cu/(Al + Ca) index was used to compare Cu, Mo, and (Pb* Zn)/(Cu*Mo) to highlight the region of interest for mineral potential mapping and pioneer borehole drilling based on fluid-rock interaction and secondary processes (e.g., alteration, weathering, and leaching). Exploratory boreholes validate a better performing Cu/(Al + Ca) index for detecting and refining soil geochemical anomalies.

In mineral exploration programs, reducing uncertainty and increasing exploration success have always been challenging issues. To modulate the above-mentioned uncertainty and increase exploration accomplishment, integration, and prospectivity analysis techniques are used for mineral exploration targeting. This paper aims to model the mineral potential of porphyry copper deposits in the Jiroft region, Kerman province. To achieve this goal and overcome the aforementioned issues resulting from the operation of complex ore-forming geological processes, continuous weighting methods through logistic functions were used while training points and analyst’s opinions were not contributed to the weighting procedure. Then, to generate exploration targets, the weighted layers were combined with three different integration methods namely, artificial neural network, geometric average, and fuzzy gamma operators. The comparison of the model obtained from the application of an artificial neural network with those obtained by the geometric average and the fuzzy gamma operators using prediction rate-area plots indicated that all the models have good overall performance and acceptable prediction rate. However, the performance of the artificial neural network model is slightly less than that of the other two models. Thus, the targets generated using the geometric average and fuzzy gamma operators are more reliable for planning further exploration programs.

The objective of petrophysical studies is to assess the quality of hydrocarbon reservoir layers and to zone the reservoir for identifying optimal zones for exploitation and informed development of oil fields. In some regions, there are zones that exhibit lower electrical resistivity values than their actual values. These low-resistivity zones are often identified through petrophysical investigations and conventional well logs, where water saturation levels are estimated higher due to their reduced resistivity. These zones, despite their hydrocarbon potential, are often neglected during production cycles. To overcome this challenge, nuclear magnetic resonance (NMR) logging tools can be employed to provide accurate estimations of free fluid saturation, irreducible fluid saturation, permeability, and effective porosity in such low-resistivity zones, making them more identifiable.In this article, we utilized conventional well logs and NMR log from the A well in the Sarvak reservoir of one of the oil fields in southwestern Iran. Based on the obtained results, depth interval 9586 to 9783 ft in the Sarvak Formation, along with two intervals (10661-10815 ft) and (10830-11063 ft) in the Int zone, were identified as potential low-resistivity zones in the reservoir. By analyzing the high-resistivity logs, water saturation percentage was calculated for these zones, and the results from NMR logging confirmed their favorable reservoir potential (e.g., free fluid saturation, effective porosity, viscosity, and permeability).Furthermore, to extend the petrophysical parameters, such as free fluid saturation and porosity, throughout the entire hydrocarbon field, various approaches including single- attribute methods, multi attribute methods, and neural networks were evaluated. The neural network method demonstrated higher accuracy in determining the parameters. Ultimately, the values of porosity and free fluid saturation in the study area were determined with 91% and 95.8% matching accuracy, respectively. The final results were validated using unseen data, and the high precision of the obtained results was confirmed.

This study presents a comprehensive geophysical investigation of the Sabalan geothermal area in Iran, utilizing magnetic, gravity, and magnetotelluric (MT) data. These data have been inverted to a depth of 5000 meters. Magnetic data inversion accurately identified faults or fractures. Gravity data inversion produced a density model distinguishing intrusive masses, reservoirs, and cover units. MT data inversion utilized apparent resistivity and phase data for both TM and TE modes. The resulting models were compared with geological cross-sections to assess their accuracy and consistency. The integration of geophysical models yielded a comprehensive geological conceptual model for the Sabalan region. Heat sources, hydrothermal reservoirs, and potential geothermal fluid pathways were identified, demonstrating the effectiveness of geophysical methods in subsurface mapping. Consistency with newer Sabalan models based on drilling and geological data increased confidence in findings.

This paper uses support vector machine (SVM), back propagation neural network (BPNN), and Multivariate Regression Analysis (MLA) methods to predict the gold in the Dalli deposit situated in the central province of Iran. After analyzing the data, the dataset was prepared. Subsequently, through comprehensive statistical analyses, Au was chosen as the output element for modelling, while Cu, Al, Ca, Fe, Ti, and Zn were considered input parameters. Then, the dataset was divided into two groups: training and testing datasets. For this purpose, 70% of the datasets were randomly entered into the data process, while the remaining data were assigned for the testing stage. The correlation coefficients for SVM, BPNN, and MLA were 94%, 75%, and 68%, respectively. A comparison of these coefficients revealed that all used methods successfully predicted the actual grade of Au. However, the SVM was more reliable and accurate than other methods. Considering the sensitivity of the gold data and the small number in the exploratory database, the results of this research are used to prepare the main layer in the mineral prospectivity mapping (MPM) of gold in 2 and 3D.

Delineating and mapping alteration zones in porphyry copper exploration is of special importance. In this study, satellite image processing techniques were employed to highlight alteration zones in the Zafarghand exploration area. The Zafarghand area is located in the southeastern part of Ardestan and the northwestern part of Isfahan. It is situated within the geological structural zones of central Iran and the intermediate magmatic arc of Urmia-Dokhtar. Various alteration haloes are present in this area, including phyllic, potassic, propylitic, argillic, and slightly siliceous alterations. In this study, the detection of related alterations was carried out using ASTER sensor imagery. 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. It could be observed the delineated propylitic alteration by the U-statistic method is closely associated with the defined zone of propylitic alteration, which is also consistent with the field and microscopic observation of the porphyry Cu mineralization in this alteration zone. It is also observed that the determined phyllic alteration by this image processing is spatially conformable with the sericitic alteration presented in the alteration map (based on field observations and geochemical sampling).

Geochemical exploration data play a vital role in mineral prospectivity modelling (MPM) for discovering unknown mineral deposits. In this study, the improved spatially weighted singularity mapping (SWSM) method is used to improve the practice of identifying geochemical anomalies related to copper mineralization in the Sarduiyeh district, Iran. Then, the random forest algorithm (RF) and geometric average function (GA) are used to integrate the resulting geochemical predictor map with other predictor maps. As demonstrated by the high area under the curve (AUC) values, this approach can effectively delineate prospective areas with RF and GA. However, compared to the GA approach (AUC=0.78), the RF technique (AUC = 0.98) offers superior prediction capabilities due to its enhanced ability to capture spatial correlations between predictive maps and known mineral deposits. The proposed procedure, a hybrid of the improved SWSM and RF has outstanding predictive capabilities for identifying prospective areas. A case in point is the new, highly prospective areas identified in this study, which present priority targets for future exploration in the Sarduiyeh district.