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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 multidisciplinary 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 datadriven 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Utilizing magnetic susceptibility and density contrast models, the primary aim of analyzing potential field data is to determine essential source parameters, including depth, structural index, horizontal expansion, and physical characteristics. In geophysical explorations, the accurate determination of subsurface depth is of utmost importance. This study employs the Depth from Extreme Points (DEXP) automatic transformation technique to interpret potential field data. DEXP relies on the precise analysis of local wavenumbers at different scales and the extreme points of the DEXP field to extract source depth, horizontal position, and structural index. This method is highly stable and less sensitive to noisy data due to the inclusion of an upward continuation filter and a potential field derivative operator. Additionally, the results are more reliable compared to alternative techniques. Moreover, the DEXP method is fully automated and does not require prior knowledge of the data collection area, making it a rapid imaging approach. As multiscale methods are less reliant on magnetic induction fields, they have become increasingly popular in magnetic field computations. To validate the proposed approach, synthetic scenarios are initially simulated. Subsequently, the DEXP technique is applied to demonstrate the depth extension of assumed models using synthetic gravity and magnetic data. This method is then implemented on data from the Ghareh-Aghaj potash exploratory area in Zanjan Province, Iran. The outcomes indicate that the potash deposit has minimal lateral expansion and is more pronouncedly extended in depth. Notably, these findings exhibit a satisfactory level of agreement with results obtained from exploratory borehole investigations. [ABSTRACT FROM AUTHOR]

The Saqez-Sardasht region (~2000) is located in the north Sanandaj-Sirjan Zone (SSZ), between longitudes of 46°00′67 00″ E to 46°30′00″ and latitudes of 36°00′00″ N to 36°30′00″, northwest Iran. The region was fully studied to recognize the promising areas for gold deposits using various methods of fuzzy fusion techniques. Accordingly, six evidential layers (i.e., lithological, tectonic, alteration, with Au, Sb, and W geochemical anomalies have been derived from three geo-data sets of geology, geochemistry, and remote sensing. A concentration–number (C–N) fractal method was used to determine the geochemical threshold values. The outcome was then combined, using the multiple indicator kriging (MIK) geochemical methods to improve the mineral potential mapping of gold deposits. In this study, four various fuzzy mineral prospectivity mapping (MPM) methods consisting of conventional VIKOR, modified VIKOR, multi-class index overlay, and Geo Fuzzy Inference System (GeoFIS) have been employed to detect the most promising areas in the Saqez-Sardasht region. The MPMs were numerically compared to each other based on the MPM efficiency index for the seven gold prospects in the study region. The Geo Fuzzy Inference System (GeoFIS) acquired 91.84% agreement, so it is selected as the superior technique to lead the prospect selection. With affiliation to the outcome of MPM maps, promising mineralized areas, controlled by shear zones, were located in the southwestern part of the Saqez region. [ABSTRACT FROM AUTHOR]

The Birjand region is a part of the South Khorasan province, situated in the structural-magmatic zone of eastern Iran. As a part of the continental shelf, it forms from subduction during the Cenozoic and subsequent continental collisions. This region is favorable for copper and gold mineralization for various geological reasons. The ultimate goal of this study is to create a Cu geochemical potential map to delimit prone regions for further mining activities. A total of 2468 geochemical samples were gathered to run a 20-element analysis. Taking data preprocessing approaches such as correction of outlier data and data normalization into consideration, a fractal graph through Concentration-Number (C-N) model was produced to isolate different geochemical populations of Cu, Pb, Zn, Ag, Ba, and Ni for Cu targeting. Then, a Prediction-Area (P-A) graph was plotted for each geochemical variable to determine the weight of each evidence map. The results show that Barium map indicates a prediction rate of 72% and specifying 28% of the studied areas as mineralization prone areas. The zinc geochemical map presents an ore prediction rate of 65% and 35% of area as potential zone. In addition, copper with an ore prediction of 56% covered 44% of the Birjand region. Finally, a hybrid evidence map was overlaid. Accordingly, the geochemical potential areas are further located towards the south and south-east of Birjand, which are closely related. Moreover, there are highly favorable areas in the middle part. It is noteworthy that the copper potential map has higher efficiency over each individual geochemical evidence, with an ore prediction rate of 75% and occupying 25% of the area as favorable zones. [ABSTRACT FROM AUTHOR]

One of the specific features of porphyry copper (Cu) mineralization is the distinct occurrence of hydrothermal alteration zones, which can be mapped by processing various satellite images. Among free multispectral images, Landsat 8 OLI and ASTER data are known to be efficient in mapping different geological features, such as alteration zones and tectonic lineaments. This study aims to show the potential of these data types in mapping porphyry Cu mineralization by proposing a framework for employing and integrating different image processing methods. These methods include principal component analysis (PCA), spectral angle mapper (SAM), and matched filtering (MF) employed on these satellite images to map target al.teration zones. Moreover, PCA and directional filtering are applied to the ASTER dataset to enhance and map structural features. The results are evaluated and then combined to provide a potential map of Cu mineralization in the Chahargonbad area, located within the Urumieh-Dokhtar magmatic belt (UDMB) in Kerman province, Iran. The prediction-area plot and normalized density, which are data-driven methods, are used to assign the relative weight of each layer and evaluate them. Finally, using the calculated weights, data-driven multi-index overlay (DMIO) and fuzzy ordered weighted averaging (FOWA) methods are applied to combine the evidential layers. The potential mineralization maps created by the DMIO and FOWA provide a prediction rate of 80% and 82%, respectively. Furthermore, the accuracy of the integrated maps is investigated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curves. The AUC scores obtained from the ROC curves of DMIO and FOWA methods are 0.85 and 0.88, respectively, representing powerful positive spatial relationships with mineralization areas. Based on the results, the proposed framework can be applied to provide a potential map of porphyry Cu mineralization, particularly in arid regions, with reasonable accuracy. [ABSTRACT FROM AUTHOR]

Landslide, as a geohazard issue, causes enormous threats to human lives and properties. In order to characterize the subsurface prone to the landslide which is occurred on the Tehran-North freeway, Iran, a comprehensive study focused on geological field observations, and a geoelectrical survey as a cost-effective and fast, non-invasive geophysical measurement was conducted in the area. As a result of road construction, problems in this region have increased. The Vertical Electrical Sounding (VES) investigation in the landslide area has been carried out by the Schlumberger array for data acquisition, implementing eight survey profiles varying in length between 60 and 130 m. Based on the electrical resistivity models through a smoothness-constrained least-square inversion methodology, the landslide structure (i.e., depth of the mobilized material and potential sliding surface) is better defined. The inferred lithological units, accompanied by stratigraphical data from a borehole and geological investigations for the prone landslide region, consisted of a discontinuous slip surface, having a wide range of resistivity, observed to be characterized by tuff with silt. Electrical resistivity values above 150 Ωm indicate a basement of weathered marlstone and sand. Values between 15 and 150 Ωm illustrate a shale-content layer with outcrops in the area that is the reason for movement. The sliding surface is at a depth of about 12 m. The method used in this study is a good candidate to investigate the risk of landslides in this region and can be applied to other landslide areas where borehole exploration is inefficient and expensive due to local complications. [ABSTRACT FROM AUTHOR]

Geoelectric surveys are among popular subsurface geophysical imaging techniques that provide significant insights into the electrical properties of subsurface targets. In this research, geoelectric modeling of travertine deposits at the Atashkohe region in Iran is performed through discretizing the physical model domain through structured (quadrilateral) and unstructured (triangular) meshes. This meshing tool captures the accurate borders of a rugged topography area along with any complex-shaped travertine sources. The modeling process was accomplished through utilizing an open-source python-based software called "ResIPy", which handles all processing steps necessary for reliable forward and inversion of geoelectrical data (i.e., electrical resistivity and induced polarization). Three synthetic electrical models according to the geological background of the studied area were simulated to examine the efficiency of the 2D electrical survey in imaging the travertine building stone. Two types of structured and unstructured meshing were designed to cope with the rough topography surface and any intricate geometry of subsurface target. The inversion results of the synthetic models approved the accuracy and efficiency of both geoelectrical survey and data modeling in travertine exploration. Finally, as a case study, tomography data of electrical resistivity and chargeability taken from the Atashkohe travertine mine were geophysically modeled to infer geological sections along three 2D profiles surveyed in the region. Data were collected with an electrode spacing of 15 or 10 m through a combination of dipole-dipole and pole-dipole arrays. The results of inverse modeling indicated that the provided geological and electrical sections at depth could be related to travertine lenses and further survey can help delineation of these building rocks. Comparison of electrical models and stratigraphic column obtained from two drillings along one profile showed a relatively good agreement as well. [ABSTRACT FROM AUTHOR]

Airborne potential field geophysical survey is employed for a variety of purposes to cover in a cost-effective manner large prospect areas. Despite the many advantages of airborne data measurements, due to the height from the ground, the received response is weakened (signal attenuation) and causes inadequacies in data representation. Accordingly, it is expected that the inversion results are far from reality and there are shortcomings in the retrieved model. This study investigates the impact of airborne survey on small-sized magmatic units, where directly inverting airborne data suffer from signal attenuation and lead to the loss of the causative model. In this study we improved the airborne data inversion by mixing a two-step cooperative approach which enhances the potential field data by a stable downward continuation to the ground surface in the spectral domain, and subsequently running a physical property modelling. The efficiency of the method over one-step airborne data inversion is examined for a synthetic multi-source case (magnetic and gravity) and then is used to find out the close spatial link between magnetometry signatures and iron–phosphate sources in the Esfordi district in Iran. The results showed that the proposed method performed better than direct inversion of airborne data and could satisfactorily identify the sources of the anomaly. [ABSTRACT FROM AUTHOR]

As a powerful tool for decision analysis, fifteen distance measures are incorporated into the technique for order preference by similarity to ideal solution (TOPSIS) to carry out a knowledge-driven approach in mineral exploration, whereby a multi-criterion decision making problem is solved for spatial data analysis. The kernel of the original version of the TOPSIS method as an integral part of the analysis will be defined on Euclidean distance. This research investigates to define the kernel on each of 15 distance measures from four general families of functions including L1, intersection, inner product and fidelity. To check the performance of each distance measure, the North Narbaghi porphyry copper mine in Iran was chosen. The significance of kernel substitution lies in the improvement of synthesized evidence maps in comparison to the Euclidean-based TOPSIS method in this study. [ABSTRACT FROM AUTHOR]

This study illustrates the application of a geometric average integration of aeromagnetic, radiometric and satellite imagery data over a region prone to Cu‐bearing mineralization at Chahargonbad area in Kerman province of Iran. Processing aeromagnetic, radiometric and Advanced Spaceborne Thermal Emission and Reflection Radiometer satellite data can provide exploratory insights about favourable zones in association with porphyry‐type ore occurrences, which can be synthesized through a combination of knowledge‐ and data‐driven approaches as a geometric average and be represented in a mineral prospectivity map. The existence of known deposits in a prospect region can facilitate the investigation of significant exploratory footprints extracted from airborne data by calculating each indicator layer's weight by plotting a prediction–area curve accompanied by a concentration–area fractal curve. Among various indicators, the most important ones are determined based on derived weights from the prediction–area plots to be synthesized in a single Cu favorability map. To fulfil this aim, indicator layers from airborne geophysics (magmatic bodies, magnetic lineaments and potassium radiometry) and remote‐sensing data (alterations such as argillic, phyllic, propylitic and iron oxide along with geological lineaments) were prepared and evaluated using the known porphyry Cu mineralization by the simultaneous plot of the concentration–area fractal model and the prediction–area curve to attain the ore prediction rate and the relevant occupied area of each map for weight assignment of indicators. The geometric average prospectivity model was applied to synthesize the leading indicators, and the result was compared with a multi‐class index overlay map. This study's significance lies in improvement of the performance of the mineral prospectivity/potential mapping after running a geometric average by a higher ore prediction rate of 79%, which has occupied 21% of the area as potential zones for further mining investigations. [ABSTRACT FROM AUTHOR]

One of the well-known geothermal potential areas in Iran is the Abgarm in Mahallat region. Due to existence of several hot springs, the Abgarm area is of particular importance in geothermal studies. To investigate the geothermal mechanism at Abgarm, geophysical surveys of magnetometry and magnetotelluric (MT) were utilized near hot springs to image the geometry of all geological requisites for trapping a geothermal reservoir. The ground magnetic data were inverted in 3D to construct a magnetic susceptibility model, where a deep intrusive igneous rock (as a heat source) and a shallow magmatic lava were inferred from the physical model. Euler depth estimation also provided insight to a shallow granodiorite unit embedded in a thick sequence of sediments. Dimensionality and strike analysis of the MT data were performed through the phase tensor method to investigate the general geometry of the sought reservoir for running a 2D TE + TM mode inversion scheme. Through 2D inversion of the MT data along three profiles across the favorable zones, the electrical resistivity models were used to image sedimentary units (i.e., cap rock, top soil, bedrock and reservoir) and magmatic rocks. Considering both geophysical properties led to illustrate a plausible geothermal reservoir in the area. [ABSTRACT FROM AUTHOR]

Decreasing the uncertainty and increasing the prediction rate of evidential maps are two important objectives in mineral prospectivity mapping (MPM). In this paper, we proposed weighted photolineaments factor (WPF) to generate a continuous weighted structural evidential map with high prediction rate and low uncertainty in Chahrgonbad area. The WPF which has four components was defined as weighted sum of the values related to four following characteristics of lineaments: (a) number, (b) length, (c) number of intersection and (d) number of directional sets. We used the extracted lineaments from geological map and processing of aeromagnetic data in our analysis to enhance the prediction rate of WPF map. The process was implemented using an effective multistage algorithm including: texture analysis, phase symmetry analysis, non-maximal suppression, thresholding, skeletonization and line fitting process. Afterward, values related to various characteristics of lineaments were extracted and transformed by a logistic function to improve their predictive ability. Then, using the concentration-area fractal model and prediction-area plot, the prediction rate of WPF components was derived and their importance weights were calculated using normalized density. Finally, the WPF map was generated and evaluated using location of 18 known mineral occurrences in the study area. The results demonstrated a good concordance with our previous geological knowledge and experience about Cu mineralization in Chahrgonbad area. The number of lineaments directional sets and the number of lineaments were recognized as structural criteria with the highest prediction rate for Cu prospectivity. The generated WPF map has a higher prediction rate (80%) in comparison with each individual component and delineated 20% of the study area as prospective target. Eventually, three zones with high structural permeability which are fertile environment for emplacement of intrusions, circulation of magmatic-hydrothermal fluids and Cu mineralization were delineated on the WPF map and proposed for further exploration. [ABSTRACT FROM AUTHOR]

In mineral potential mapping, supervised machine learning algorithms have shown great promise in delineating and prioritizing potential areas. However, since mineralization being a relatively rare geological event, most supervised machine learning-based models face substantial challenges in properly identifying prospective areas. Data sets with strongly imbalanced distributions of the target variable (deposits) and insufficient training data sets impose obstacles to these kinds of models which can significantly impact adversely on the performance of the models. Moreover, in some cases, negative training data sets as the non-deposit locations aren't really true negative data, which cause higher uncertainty in a mineral potential map. In this study, for handling these challenges the deep autoencoder neural network is adopted. The autoencoder can be trained to reconstruct geospatial data set in totally unsupervised manner and identify prospective areas based on the reconstruction error, where higher error corresponds with areas of higher mineral potential. In order to confirm the efficiency of the autoencoder algorithm in mineral potential modeling, the model was compared with a popular data-driven approach that assigned a weight to the evidence layer by using a concentration-area (C-A) fractal model and a prediction-area (P-A) plot, and combined them using a multi-class index overlay method. Receiver operating characteristic (ROC) curve, success-rate curve, and P-A plot were adopted to evaluate the predictive ability of Fe prospectivity models pertaining to the Esfordi district of Iran. Also, we use an area under the ROC curve (AUC) and partial AUC (pAUC) to quantitatively evaluate the overall and sensitivity performance of models, respectively. • Unsupervised mineral potential mapping. • A deep autoencoder neural network is adopted. • Reconstructing geospatial data to identify prospective areas. • Concentration-area fractal model and prediction-area plot. • Fe prospectivity model of the Esfordi district. [ABSTRACT FROM AUTHOR]

In this paper, an application of a knowledge-driven mineral prospectivity mapping (MPM) approach so-called “the evidential belief functions (EBFs) using Dempster-Shafer's rule of combination” is proposed. This technique is used to weight and integrate a large scale exploration dataset in order to localize prospects for definition of further exploration drilling sites. In this study, exploration datasets of Seridune copper deposit in the Kerman province, SE Iran used for the methodology. In this regard, geophysical evidence layers extracted from interpretation of magnetic and electrical surveys, geological evidence layers derived via the geological datasets (i.e. lithology, fault and alteration), and geochemical evidence maps were generated and integrated for MPM. Furthermore, various MPM approaches including outranking, index overlay and fuzzy logic methods were examined for comparison with the introduced method. To evaluate and compare the efficiency of the methods, the productivity of the drilled boreholes (Cu concentration multiplied by its ore thickness along each drilled borehole) was used to validate the generated prospectivity models. The results showed higher efficiency of the Dempster-Shafer's model in comparison with the prospectivity models generated using other MPM approaches. [ABSTRACT FROM AUTHOR]

This paper describes the application of the knowledge-based fuzzy logic method to integrate various exploratory geo-dataset in order to prepare a mineral prospectivity map (MPM) for copper exploration. Different geophysical layers which are derived from the magnetic and the electrical surveys, along with the ones extracted from the background geology (i.e., lithology, fault and alteration) and geochemical data are incorporated in such process. Seridune copper deposit located in the Kerman province of Iran is the case study to delineate its high potential zones of Cu-bearing mineralization for drilling additional boreholes. Four layers from the magnetic data involving upward continuation, analytic signal, reduced to pole and pseudo gravity are assigned in the multi-disciplinary geo-dataset to locate the intrusive complexes responsible for Cu mineralization. The apparent resistivity, chargeability and sulfide factor layers acquired from geo-electrical data are also included in the final preparation of MPM. Then the normalized weights of seven geophysical, three geological and one geochemical evidential layers as main criteria are determined based upon the knowledge of expert decision makers. Fuzzy operators (i.e., Sum and Gamma) are applied to integrate these exploratory features. To evaluate the performance and applicability of the approach, the productivity of the drilled boreholes (Cu concentration multiplied by ore thickness) are used to validate the produced MPMs. It is shown that an optimum correlation coefficient of 0.86 exists between the MPM values and Cu productivity criterion along drilled boreholes. [ABSTRACT FROM AUTHOR]

This paper describes the successful application of an integrated geophysical survey to investigate the depth of underground water table and the location of probable cavity occurrences at a developing residential area to design the foundation of under constructing urban buildings located at Hesarak-Karaj, Alborz province of Iran. The integrated shallow and non-invasive geophysical survey consists of Electrical Resistivity Tomography (ERT) and Ground Penetrating Radar (GPR). These methods could appropriately indicate the existence of the water table at a depth of approximately 12 m while processed GPR radargrams localized no cavity occurrences in the studied site. A 100-MHz shielded GPR antenna has been used to conduct a high resolution shallow subsurface survey in order to delineate probable cavities of the area, and two electrical arrays of gradient and Wenner have been used as well to image resistivity variations of subsurface layers especially for prospecting water saturated zone. The acquired results which were in good accordance with the reality of the studied area at locating water table and cavity occurrences could effectively provide guidance to builders in order to tackle water flow issue and not to concern about filling cavities during constructing any building in the site. [ABSTRACT FROM AUTHOR]

Abstract: Previous studies have shown that a well-known multi-criteria decision making (MCDM) technique called Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE II) to explore porphyry copper deposits can prioritize the ground-based exploratory evidential layers effectively. In this paper, the PROMETHEE II method is applied to airborne geophysical (potassium radiometry and magnetometry) data, geological layers (fault and host rock zones), and various extracted alteration layers from remote sensing images. The central Iranian volcanic-sedimentary belt is chosen for this study. A stable downward continuation method as an inverse problem in the Fourier domain using Tikhonov and edge-preserving regularizations is proposed to enhance magnetic data. Numerical analysis of synthetic models show that the reconstructed magnetic data at the ground surface exhibits significant enhancement compared to the airborne data. The reduced-to-pole (RTP) and the analytic signal filters are applied to the magnetic data to show better maps of the magnetic anomalies. Four remote sensing evidential layers including argillic, phyllic, propylitic and hydroxyl alterations are extracted from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images in order to map the altered areas associated with porphyry copper deposits. Principal component analysis (PCA) based on six Enhanced Thematic Mapper Plus (ETM+) images is implemented to map iron oxide layer. The final mineral prospectivity map based on desired geo-data set indicates adequately matching of high potential zones with previous working mines and copper deposits. [Copyright &y& Elsevier]

Abstract: This paper describes the application of a multicriteria decision-making (MCDM) technique called ELECTRE III, which is well-known in operations research, to mineral prospectivity mapping (MPM), which involves representation and integration of evidential map layers derived from geological, geophysical, and geochemical geo-data sets. In a case study, thirteen evidential map layers are used for MPM in the area containing the Now Chun copper prospect in the Kerman province of Iran. The ELECTRE III technique was applied for MPM, and the outputs are validated using 3D models of Cu and Mo concentrations from 21 drill hole data. This proposed method shows high performance for MPM. [Copyright &y& Elsevier]

Abstract: In this paper on mineral prospectivity mapping, a supervised classification method called Support Vector Machine (SVM) is used to explore porphyry-Cu deposits. Different data layers of geological, geophysical and geochemical themes are integrated to evaluate the Now Chun porphyry-Cu deposit, located in the Kerman province of Iran, and to prepare a prospectivity map for mineral exploration. The SVM method, a data-driven approach to pattern recognition, had a correct-classification rate of 52.38% for twenty-one boreholes divided into five classes. The results of the study indicated the capability of SVM as a supervised learning algorithm tool for the predictive mapping of mineral prospects. Multi-classification of the prospect for detailed study could increase the resolution of the prospectivity map and decrease the drilling risk. [Copyright &y& Elsevier]

Abstract: This paper describes the application of a well-known Multi Criteria Decision Making (MCDM) technique called Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE II) to explore porphyry copper deposits. Various raster-based evidential layers involving geological, geophysical, and geochemical geo-datasets are integrated to prepare a mineral prospectivity mapping (MPM). In a case study, thirteen layers of the Now Chun copper deposit located in the Kerman province of Iran are used to explore the region of interest. The PROMETHEE II technique is applied to produce the desired MPM, and the outputs are validated using twenty-one boreholes that have been classified into five classes. This proposed method shows a high performance when providing the MPM while reducing the cost of exploratory drilling in the study area. [Copyright &y& Elsevier]

Abstract: In this paper, three data-driven methods (i.e., Bayesian, k-nearest neighbour (k-nn) and neural network classifiers) are used to generate a prospectivity map for porphyry-Cu deposits. Different data layers of geological, geophysical and geochemical themes are integrated to evaluate the Now Chun porphyry-Cu deposit, located in the Kerman province of Iran, and to prepare a prospectivity map for mineral exploration. Both the Bayesian and k-nn methods showed correct classification rates (CCR) of 52.38% for 21 boreholes divided into five classes. Three types of the neural networks including multi-layer perceptron, radial based function (RBF) and probabilistic neural network are applied to evaluate the result. Among neural networks used, the RBF neural network generated the highest CCR equal to 80.95%. Multi-classification of the prospect for detailed study could increase the resolution of the prospectivity map and decrease the drilling risk. [Copyright &y& Elsevier]

This work has evaluated the functionality of various fuzzy-based fusion methods in the mineral potential mapping (MPM), by which a multi-criteria decision-making problem was solved to design a layout for drilling complementary boreholes through a comprehensive analysis of geospatial datasets. The novel methods employed were fuzzy c-means clustering, fuzzy gamma operator, fuzzy inference system (FIS), fuzzy outranking, and fuzzy ordered weighted averaging (FOWA). Kahang porphyry Cu-Mo deposit in the Isfahan province of Iran was chosen as a case study to examine the performance of these fuzzy methods in MPM. Extracted geospatial indicator layers for assessing the potential of porphyry-type mineralization were derived from four criteria, namely geology (rock units and faults), remote sensing (alteration map), geochemistry (Cu, Mo, and factor maps), and geophysics (reduced to the pole and analytical signal of magnetic data). The concentration-area multifractal method was utilized to reclassify each synthesized fuzzy favorability map into five classes. To appraise and compare the efficiency of each employed method, a productivity measure assumed as a cumulative summation of Cu grade multiplied by its thickness above an economical cut-off value of 0.2% was calculated along with each drilling (totally 33 ones). According to fuzzy favorability maps derived from running all fuzzy methods, the FIS and FOWA had the highest efficiency with 80 and 78% of accuracy, respectively. Eventually, taking all fuzzy maps into account led to the delineation of some new favorable zones, whereby further exploratory investigations are envisioned for determining their mining potential. [ABSTRACT FROM AUTHOR]

Selection of non-deposit sites is a challenging issue affecting the application of supervised algorithms for modeling mineral exploration targets. For this, equal number of deposit and non-deposit sites has been widely applied for training purposes. In this paper, we investigated the effect of changes in the number of non-deposit sites on the effectiveness of exploration targeting models while the number of deposit sites is constant. The results obtained demonstrated that exploration targeting models are affected by the ratio of non-deposit and deposit sites. Thus, balancing between the number of deposit and non-deposit sites is an efficient way to produce more-reliable exploration targets when supervised algorithms are applied for modeling. The idea of this research came from the fact that mineralization is a rare event, and therefore, in a region of interest number of non-deposit sites is much more than that of deposit events. To illustrate the procedure proposed, we used an exploration dataset of porphyry Cu mineralization in Chahargonbad area, SE Iran. A sequence application of self-organizing map and multilayer perceptron neural network algorithm was applied to better illustration of the changing effects of the number of non-deposit sites on the ensuing exploration targeting models. • Counting of deposit and non-deposit sites is discussed for training purpose. • The number affects the ensuing exploration targeting models. • Making a balance between the two types of training data yields significant results. • Self-organizing map clustering approach leads to define non-deposit sites. [ABSTRACT FROM AUTHOR]

This work illustrates the performance of a focused algorithm for 2D inversion of potential field geophysics (gravity and magnetic) in regions with rugged topography. A non-structured model discretization mesh is performed through Delaunay triangular cells to accurately represent the rugged observation surface and recover complex-shaped sources. Several synthetic single and multi-source targets are simulated to evaluate the applicability of the focused algorithm for simultaneously preserving the edges, correct depth estimation and body distribution of the inverted models. The algorithm is written in such a way that it is able to improve the modeling result per iteration by imposing physical properties constraining (magnetic susceptibility or density contrast), depth weighting and focused term in the central system of linear equation. Whereby a preconditioned conjugate gradient scheme is used iteratively to solve efficiently the L 2 -norm cost function comprising of a data misfit norm and a model stabilizer. Potential field data pertaining to a real case study for iron-bearing targeting are also inverted along two profiles to investigate the geometry of the sought targets at the Shavaz deposit in Iran. Iron-oxide ore occurrence was approved by exploratory drilling indicating a shallow hematite target with minor compounds of magnetite. Of note is that the significant point of this study lies in accurate recovering of causative sources responsible for potential field anomaly when a severe undulating topography occurs. • A focused algorithm for 2D inversion of potential field geophysics. • A non-structured mesh through Delaunay triangular. • Rugged observation surface and complex-shaped sources. • Modeling the Shavaz Iron deposit in Iran. [ABSTRACT FROM AUTHOR]

Abstract The Mirgeh-Naghshineh region located at the northwest of Iran has a high potential for Au mineralization due to its adjacency to shear zone as a plausible environment responsible for ore-bearing occurrences. In this study, geochemical samples were collected over promising regions of the study area to delineate favorable zones for exploratory drillings. A number-size (N S) fractal approach was taken into account to separate anomalous regions from regional and background ones, and the prospect zone was divided into some indicators based on the extracted thresholds. Such results were incorporated in the multiple indicator kriging (MIK) approach to augment the interpolation of Au distribution map. It was observed that the highest favorable zones cover about 1.6% of the area, while the drilled holes prove such Au mineralization. The applicability of the proposed method was also compared with the conventional interpolation techniques, such as Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and Median Indicator Kriging (Median-IK). The Mean Error (ME) and Root Mean Square Error (RMSE) values for the proposed method were smaller than those of other methods. Moreover, cross validation indicated the superiority of the proposed method. Highlights • Developed geochemical mapping of Au in the Mirgah-Naqshineh. • Proposing Fractal-based Multiple Indicator Kriging (FMIK). • The estimation process in the FMIK is independent of data distribution function. • The results of geochemical mapping are evaluated by the drilled holes. [ABSTRACT FROM AUTHOR]

[Display omitted] • Surface exploration proxies related to distal porphyry mineralization were identified from geological, geochemical, and geophysical datasets. • Conventional cell-based association (CBA) applied to exploration proxies to define their association with Cu–Au mineralization. • By assigning proper weights to exploration proxies, an innovative CBA method was used to locate potential targets for drilling. The presence of various spatial exploration proxies (predictor patterns) as vectors to distal porphyry mineralization in surrounding areas of a known outcropping mineralized porphyry center at the Dalli Cu–Au deposit in Iran provided evidence for the occurrence of similar porphyry mineralization in the subsurface. Factor analysis was applied to soil geochemical data from the Dalli South Hill area to define favorable geochemical variables and to create surface multi-element anomaly maps reflecting the presence of porphyry Cu–Au mineralization in the subsurface. Reduction-to-pole and analytical signal filters were applied to ground total magnetic field data to define high magnetic signatures that could be associated with potassic alteration and porphyry mineralization in the subsurface. Then, a data-driven cell based association (CBA) data integration method was applied to the favorable spatial proxies to define their association with Cu–Au mineralization and locate subtle high potential targets (cells) for drilling. The conventional CBA algorithms did not consider weights for the spatial proxies, and by using the ascendant hierarchical clustering (AHC) algorithm the results were classified into a binary map to show favorable and unfavorable cells with scores of 1 and 0, respectively. Predicting high potential targets for high-priced drilling, just based on using the binary map, could be very risky. Therefore, an innovative CBA data integration approach was employed to assign proper weights between 0 and 1 to the spatial proxies by calculating the importance of the presence of an exploration proxy in a cell using training (labeled) trenching geochemical data. A final continuous predictor map was generated and it successfully mapped the known outcropping porphyry center and its subsurface extension to the northeast as a high prospectivity class. This map also identified a narrow NE-trending high potential target (a few cells) of 200 m long in the southern part of the known porphyry center for drilling. [ABSTRACT FROM AUTHOR]

Once inverting potential-field geophysical data an appropriate discretization of the model is required to accurately construct complicated geometries of the causative sources. Rectangular prisms (structured meshes) have limitations to recover and preserve the edges of real geological sources. Here an isoparametric finite-element (FE) methodology is used to design an unstructured mesh for use in 3D inverse modeling of gravity data. The calculation of the sensitivity kernel of the forward operator uses Gauss-Legendre quadrature rather than the analytic formulation. For the sake of instability of inversion operator in gravity data and to solve the Tikhonov norms term, the least-squares QR-factorization (LSQR) technique is used. This method is accompanied with weighted generalized cross-validation (WGCV) for selecting the optimum regularization parameter value. The depth weighting function is also incorporated in the formulation of the objective function to suppress the impact of shallow features and recover sources at an appropriate depth. The proposed algorithm was applied to noise-corrupted synthetic data along with a real case study where a gravity survey was used for iron exploration in Yazd province, central Iran. The obtained results of synthetic example indicated that the proposed 3D inversion method recovers the geometry and density contrast values which are similar to the true structures and its application on a real example data set recovers geologically reasonable complex structures. So, this modified algorithm is a type of smooth inversion for boundary detection and whole understanding of the physical property distribution of the subsurface. • Unstructured hexahedral mesh for physical model discretization. • Finite element formulation of the gravity forward operator. • An LSQR algorithm for solving the linear equation. • Complex-shaped target simulation of gravity anomaly. • Targeting a real iron-bearing occurrence in Iran. [ABSTRACT FROM AUTHOR]