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The gravity inversion methods play a fundamental role in subsurface exploration, facilitating the characterization of geological structures and economic deposits. In this study, we conduct a comparative analysis of two widely used regularization methods, Tikhonov (L2) and Sparse (L1) regularization, within the framework of the gravity inversion. To assess their performance, we constructed two distinct synthetic models by implementing tensor meshes, considering station spacing to discretize the subsurface environment precisely. Both methods have proven ability to recover density distributions, while minimizing the inherent non-uniqueness and ill-posed nature of the gravity inversion problems. The Tikhonov regularization yields stable results, presenting smooth model parameters even with limited prior information and noisy data. Conversely, the Sparse regularization, utilizing sparsity-promoting penalties, excels in capturing sharp geological features and identifying anomalous regions, such as mineralized zones. Applying these methodologies to real gravity data from the Safu manganese deposit in northwest Iran, we assess their efficacy in recovering the geometry of dense ore deposits. The Sparse regularization demonstrates superior performance, yielding lower misfit values and sharper boundaries during individual inversions. This underscores its capacity to provide a more accurate representation of the depth and edges of anomalous targets in this specific case. However, both methods represent the same top depth of the target in the real case study, but the lower depth and density distribution were not the same in the XZ cross-sections. The inversion results imply the presence of a near-surface deposit characterized by a high-density contrast and linear distribution, attributed to the high grade of manganese mineralization. [ABSTRACT FROM AUTHOR]

The Euler deconvolution system is a well-known approach to estimate the depth of underground sources in potential field geophysics. Overdetermined Euler linear equations are usually solved independently and separately for the gravity and magnetic data, and each result is an estimate for the depth of the potential sources. This technique is widely utilized to analyze the depth variations of magnetic and gravity sources individually. However, the depth estimation of each of the mentioned potential fields may return specific and exclusive results regarding the complex nature of the subsurface structures, and the gravity and magnetic separate depth estimation solutions may be discordant in many aspects. In the cases of low-resolution for the gravity and magnetic data sets, this study indicates that the independently solved Euler depth estimation systems cannot yield reliable and accurate solutions for potential field sources. By combining the gravity and magnetic data and simultaneously solving the Euler equations for the gravity and magnetic potential fields, this research presents a novel approach called the joint Euler method with a proper capability to return more accurate and improved depth estimations for the boundary and body of potential field sources. The presented method was solved and examined over homogeneous and non-homogeneous synthetic scenarios with reduced resolution, and the depth solutions were also compared with the separate approach. After obtaining the desired results from the synthetic models, the joint Euler technique was applied to the gravity and magnetic data of the Kifl oil trap located in Iraq. The results were quite promising compared to the separate depth estimations, proving the sufficiency and applicability of the proposed potential field method in terms of interpretational aspects. [ABSTRACT FROM AUTHOR]

As the depth of mineral exploration has increased in recent years, multiple exploration methods have become necessary to obtain more accurate depth and surface data. Each type of exploratory data has different uncertainty, resolution, and efficiency levels. Using these data individually or preparing traditional models based on a single data type often fails to meet the desired accuracy level. Therefore, mineral prospectivity mapping (MPM) has become more common in integrating these data. MPM methods require determining the importance of the data used. This importance is expressed as the weight of the layers (evidence). Typically, data-driven methods cannot be used to determine the weight of evidence layers in green areas due to the need for sufficient deposits. In these areas, knowledge-based methods, using the opinions of expert geologists, are often used to determine the weight of the layers. However, the weights determined by different experts may vary depending on their perspectives. Therefore, one of the challenges of using MPM methods in green areas is determining a reliable weight for the layers. This paper uses different exploration data, such as airborne geophysical data, geochemistry, geology, and remote sensing data, to prepare suitable reference layers. Due to the limited mineral prospects available in this area, we used the prediction-area (P-A) method to calculate the layers' weights without experts' opinions. We then used these weights to produce the gold prospectivity map in this area using the multi-index overlay (MIO) and the (Adjusted, Conventional, and Modified) TOPSIS methods. Finally, the obtained results were used to evaluate the efficiency of these methods and the calculated weights for this area. [ABSTRACT FROM AUTHOR]

Birjand region is located in South Khorasan province in the structural-magmatic zone of eastern Iran. This part of the Iranian plateau is the result of subduction during the Cenozoic and subsequent continental collisions. This region is known as important in terms of copper and gold mineralization for various geological reasons. This research aims to develop a map of Au geochemical potential. 1966 geochemical samples were collected in the study area, and a 20-element analysis was performed. After data pre-processing including correction of outlier data and data normalization, and through a graph from the fractal concentration-number (C-N) model to isolate different geochemical populations of Au, As, Sb, Hg, Bi, Mo, Sn, and W with Au targeting, a Prediction-area (P-A) graph was plotted for each variable to determine the weight of each geochemical indicator. The results show that after gold, with an ore prediction rate of 74% and specifying 26% of the studied district as mineralization-prone areas, arsenic with a prediction rate of 72% has covered 28% of the Birjand region as potential mineralization areas while Bismuth and Mercury with a prediction of 64% covered 36% of the Birjand region. In addition, a hybrid indicator map was prepared utilizing a multi-class index overlay method, where the potential geochemical areas were located further south and southeast of Birjand. In addition, there are favourable areas in the middle. Notably, the mineral potential map (MPM) has higher efficiency than any geochemical indicator, with an ore prediction rate of 88% and occupying 12% of the whole prospect 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]

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]

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]

Geoelectrical methods are considered common subsurface geophysical imaging tools that provide significant insight into the electrical properties of targets. Considering the three-dimensional nature of subsurface structures, geoelectrical survey data and their 3D inverted models can yield reliable and accurate results. In this research, using an unstructured tetrahedral meshing, three-dimensional forward and inverse models of electrical resistivity and chargeability data were performed for geological structures with travertine layers. The Application of the unstructured mesh for the discretization of subsurface geological units increases the speed and accuracy of the modelling procedure, as well as the flexibility in designing and implementing meshing on tough topographies and the complex-shaped geometry of the target mass. Using an open-source and full-item Python software named ResIPy, the forward and inverse models were calculated and interpreted precisely. According to the geological background of the studied area, to investigate the applicability and efficiency of the 3D geoelectrical modelling method in imaging the subsurface travertine deposits, three synthetic scenarios were modeled according to the geological setting of the studied area. The results of the 3D inversion of the synthetic models indicated the accuracy and validity of this procedure in the exploration of underground travertine deposits. As a real case study, the electrical resistivity and chargeability survey datasets in the Atashkohe travertine mine were inverted in 3D, aimed to inferring schematic geological sections along the three surveyed profiles. The survey was conducted with electrode spacing of 10 and 15 meters, using a combination of dipole-dipole and pole-dipole arrays. Considering the two-dimensional nature of these data and the relatively large distance between the two main profiles, the three-dimensional inversion results may increase the error rate. Therefore, the 2D batch inversion was preferably utilized in order to obtain a more realistic and sensible geological model. According to the geological studies and instrumental analysis of the rock samples, three types of geological structures were identified throughout the study area. Based on the subsurface electrical characteristics inferred along each profile, three geological layers were designed to illustrate the underground structures. The comparison of the inferred geological models and the drilling results along one of the survey profiles demonstrated acceptable compatibility and concordance, indicating the efficiency of the research utilized approach. [ABSTRACT FROM AUTHOR]

This work presents an algorithm to construct a 3D magnetic susceptibility property from magnetic geophysical data. Physical model discretization has substantial impact on accurate inverse modeling of the sought sources in potential field geophysics, where structural meshing suffers from edge preserving of complex-shaped geological sources. In potential field geophysics, a finite-element (FE) methodology is usually employed to discretize the desired physical model domain through an unstructured mesh. The forward operator is calculated through a Gauss-Legendre quadrature technique rather than an analytic equation. To stabilize mathematical procedure of inverse modeling and cope with the intrinsic non-uniqueness arising from magnetometry data modeling, regularization is often implemented by utilizing a norm-based Tikhonov cost function. A so-called fast technique, "Lanczos Bidiagonalization (LB) algorithm", can be utilized to solve the central system of equations derived from optimizing the function, where it decreases the execution time of the inverse problem by replacing the forward matrix with a lower dimension one. In addition, to obtain best regularization parameter, a weighted generalized cross-validation (WGCV) curve is plotted, that makes a balance between misfit norm and model norm introduced in the cost function. In order to tackle the normal propensity of physical structures to focus at the shallow depth, an expression of depth weighting is used. This procedure is applied to a synthetic scenario presenting a complex-shaped geometry along with a real set of magnetic data in central part of Iran. So the capability of the proposed algorithm for inversion indicates the accuracy of the inversion algorithm. Additionally, the modeling results pertaining to a field case study are in good agreement with the drilling data. [ABSTRACT FROM AUTHOR]

In integrating geospatial datasets for mineral potential mapping (MPM), the uncertainty model of MPM can be inferred from the Dempster - Shafer rules of combination. In addition to generating the uncertainty model, evidential belief functions (EBFs) present the belief, plausibility, and disbelief of MPM, whereby four models can be simultaneously utilized to facilitate the interpretation of mineral favourability output. To investigate the functionality and applicability of the EBFs, we selected the Naysian porphyry copper district located on the Urmia - Dokhtar magmatic belt in the northeast of Isfahan city, central Iran. Multidisciplinary datasets-that are geochemical and geophysical data, ASTER satellite images, Quickbird, and ground survey-were designed in a geospatial database to run MPM. Implementing the Dempster law through the intersection (And) and union (OR) operators led to different MPM performances. To amplify the accuracy of the generated favourability maps, a combinatory EBFs technique was applied in three ways: (1) just OR operator, (2) just And operator, and (3) combination of And and OR operators. The plausibility map (as mineral favourability map) was compared to Cu productivity values derived from drilled boreholes, where the MPM accuracy of the hybrid method was higher than each operator. Of note, the success rate of the hybrid method validated by 21 boreholes was about 84%, and it demarcates high favourability zones occupying 0.67 km² of the studied area. [ABSTRACT FROM AUTHOR]

Edge detection is an important part of interpreting potential field geophysics data. It helps better recognition of magnetic anomaly features such as superficial position and shape. Many edge detection methods have been presented; however, due to the natural complexity of subsurface features, only a few of the processing techniques might be accurate. Because, in practical cases, multi-dimensional precision is essential, using other methodologies along with edge detection can be useful. As a new research, this study utilized the 3D inverse modeling procedure as a confirmation and completion tool for combining both subsurface magnetic susceptibility distribution and surface anomaly projection. This research focused on investigating simultaneous application of 3D inversion and edge-detecting methods that provide an accurate perspective view of sought sources with high magnetic susceptible properties. To perform the mentioned task, along with the definition of two new edge detection filters, some beneficial edge detection filters were implemented on synthetic data. Then, these filters were applied to the Charmaleh Iron deposit magnetic data to obtain a valid anomaly edge perimeter. Subsequently, a 3D inverse modeling scheme was run on the data to investigate the magnetic susceptibility properties of surface and underground structures. In order to validate our edge detection results and to achieve a proper interpretation, the utilized edge detectors and the inverted model results were overlaid, compared, and depicted concurrently. Drilling operations were also conducted within the jointly achieved favorable zones, which were in line with the research findings. Conclusively, it can be stated that joint consideration of the mentioned approaches can be advantageous attitude toward accurate interpretation of environments with highly magnetic anomalies. [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]

Potential field geophysical measurements were conducted in the west of Kifl region in central Iraq to image a plausible oil-trapping reservoir. Ground-based magnetometry and gravimetry surveys were conducted to investigate this region by covering an area of 16 × 24 km by designing a regular grid spacing of 250 m. After preprocessing potential field data, different filters were utilized to separate the residuals from the regional anomalies. The complicated tectonic setting of the studied area was imaged by recognition of the fault system through simulation of the magnetic and gravity anomalies, which facilitates the configuration display of the oil-trapping mechanism. The geometry of a fault system was derived from parametric inversion of gravity data. The magnetic anomalies were extended with the trends of NS, NW, and NE and reached a maximum value of 55 nT. However, the gravity anomalies appeared with the same extensions and values ranging from -3.3 to 1.5 mGal. The intense magnetic susceptibility amount of the reservoir rocks is arising from chemical processes and iron-oxide ion replacements, accompanied by the migration and accumulation of hydrocarbon. Incorporating the results from the Euler's depth estimation, parametric data modeling along with logging data assisted simultaneous modeling of the magnetic and gravity data. The 2D geological model of the subsurface layers at the Kifl area presents a graben-horst fault system within a thick sequence of sediment. Geological characteristics extracted from geophysical data modeling provided insightful information on the nature and essence of the hydrocarbon reservoirs in the Kifl area. It has formed through tectonic deformation and tension over the Arabian plate during the Permian - Paleocene cycle. Hence, it can be concluded that the aforementioned fault system has divided the hydrocarbon reservoirs. [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]

This work illustrates the application of a fuzzy-guided focused technique for cooperative 2D modeling of magnetic and gravity data on common geological sources responsible for anomalous observation, whereby a well-known fuzzy c-means clustering tool is inserted in the center of the inversion mechanism to search different clusters of geophysical properties which are magnetic susceptibility and density contrast within several zones. An unstructured meshing is performed with triangular cells which captures the accurate borders of a rugged topography area and any complex-shaped sought sources. The efficiency of the proposed cooperative inversion algorithm is examined along 2D profiles with simulation of several synthetic sources by which the retrieved geophysical properties indicate sharp edges, correct depth and shape pattern of the sought sources. Imposing a model stabilizer, a depth weighting function and petrophysics constraints of physical models within the clustering inversion, greatly promotes the performance of the constructed models over the outputs of running individually each data set. Of note is that a conjugate gradient solver is utilized here with a preconditioner to estimate approximately sought physical properties from an objective function with two constituents that are a model and a misfit norm. Ground-based gravity and magnetic observations over a plausible oil-trapping structure are investigated at the Kifl region, situated in Iraq. Cooperative inversion output can pave the way for imaging of a fault feature which has been filled by a thick sequence of sediments, presenting a configuration of a graben-horst structure. The main notable output of this work proves the existence of an oil-trapping structure responsible for a distinct potential field geophysics anomaly. [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]

Producing an accurate and valid mineral prospectivity map is one of the most significant parts of mineral exploration studies. For this purpose, it is needed to obtain valid evidential layers and integrate them with an accurate methodology. Knowledge and data‐driven methods are two primary techniques applied to combine various evidential layers for mineral prospectivity mapping, of which each of them includes a variety of analytical techniques. In this study, in the first step, satellite data, aeromagnetic and airborne radiometric data, stream sediment geochemical data and geological data were applied to create valid remote sensing, geophysical, geochemical, lineaments and lithological evidential layers of the study area that are an essential factor in recognition porphyry copper mineralization, then in the second step, based on the known mineralization occurrences data, the evidential layers were weighted. Finally, these layers were integrated using fuzzy logic and index overlay methods in a combination of knowledge and data‐driven way. Validation of each layer was done using available data in the second step. The final mineral prospectivity map was evaluated, and the confirmation of this layer detected that the final mineral prospectivity map obtained from data‐driven multi‐index overlay method has a higher ore prediction rate of 76%, which identifies 24% of the area as potential zones for further exploration. [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 serves the purpose of generating a geochemical Fe-bearing potential map. Stream sediment geochemical survey was employed by collecting 843 samples for analyzing 19 elements and oxides. Taking preprocessing of data (e.g. outlier correction and data normalization) into consideration, a Concentration-Number (C-N) fractal model was used to separate different geochemical populations of Fe2O3, TiO2, V, and the main multi-element factor in close spatial association with the Fe targeting. A prediction-area(P-A) plot was drawn for each variable to determine the weight of each geochemical indicator. Results indicate that the main geochemical factor with an ore prediction rate of 73%, has occupied 27% of the Esfordi area as favorable zones for further mining prospectivity. The Esfordias a favorable Fe-bearing zone is of special interest in the NE of the Bafq mining district that hosts important "Kiruna-type" Magnetite-Apatite deposits. In addition, a synthesized indicators map was prepared by implementing a data-driven multi-class index overlay in a similar fashion to the previous version of the method, upon which geochemical potential zones were mostly in the NE part of the Esfordi, intimately linked with intense fault density map. The significance of this study lies in localizing the most geochemical favorable zones through simultaneous consideration of the C-N and PA plots accompanied by the incorporation of known active mines and prospects to determine indicator weight. Of note is that the Mineral Potential Mapping(MPM) has higher efficiency over each geochemical indicator with an ore prediction rate of 78% and area occupation of 22%. [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 this work, a quantifier-guided ordered weighted averaging (OWA) method was employed for mineral potential mapping (MPM) in Nowchun Cu–Mo prospect, SE Iran. The proposed knowledge-driven method has the capability of incorporating the geologists' preference (weight) and attitude toward risk analysis during MPM. Since quantitative determination of OWA parameters is a tough task, employing linguistic quantifiers aid geologists to simply define their desired strategy for MPM. To implement the method, eight weighted criteria spatially associated with mineralization were derived from geological, geochemical and geophysical datasets. The evidential layer integration was implemented using various OWA operators, which is generated by employing seven linguistic quantifiers. As a result, seven mineral potential maps, which report favorability index from 0 to 1, were produced in a spectrum range of risk from extremely optimistic to extremely pessimistic. According to results, the western and southeastern part of the study area were detected as regions with the lowest and highest mineral favorability. For validation, the results of subsequent geological field works and 106 drilled boreholes were taken into account. The evaluation indicated that the mineral potential map based on the "Some" quantifier has the highest correspondence with underground 3D mineralization zones of Cu and Mo. The mineral potential map based on the "Some" quantifier delineated two main prospective zones in the eastern and central-north parts of the study area. The former zone was recently investigated by drilling, but the latter zone was proposed for new drilling operation. Applying the proposed method in each scale of target delineation (a) generates various continuum favorability maps; (b) reveals mineralization patterns in the study area; and (c) provides an opportunity in exploration to select the optimal mineral potential map for detailed exploration tasks regarding the geologists' attitudes toward risk and project budget. [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]

The central Iranian volcanic-sedimentary belt in Kerman province of Iran that is located within the Urumieh-Dokhtar magmatic arc zone is chosen to integrate diverse evidential layers for mineral potential mapping. The studied area has high potential of mineral occurrences especially porphyry copper, and the prepared potential maps aim to outline new prospect zones for further investigation. Two evidential layers including the downward continued map and the analytic signal of filtered magnetic data are generated to be used as geophysical plausible traces of porphyry copper occurrences. The low values of the resistivity layer acquired from airborne frequency domain electromagnetic data are also used as an electrical criterion in this study. 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. The Enhanced Thematic Mapper Plus (ETM+) images are used as well to prepare iron oxide layer. Since potassic alteration is generally the mainstay of copper ore mineralization, the airborne potassium radiometry data is used to explore both phyllic and potassic alteration. Finally, the geochemical layers of Cu/B/Pb/Zn elements and the main geochemical component responsible for ore mineralization extracted from principal component analysis are included in the integration process to prepare final potential maps. The conventional and the extended version of VIKOR method (as a well-known algorithm in multi-criteria decision making problems) produced two mineral potential maps, and the results were compared with the ones acquired from prevalent methods of the index overlay and fuzzy logic operators of sum and gamma. The final mineral potential maps based upon desired geo-data set indicate adequately matching of high potential zones with previous working and active mines of copper deposits. [ABSTRACT FROM AUTHOR]

This paper describes an application of diverse geophysical methods to detect the geological contacts in the Tomelilla area, Sweden. Airborne magnetic data and the tipper and peaker maps derived from tensor very-low-frequency electromagnetic (TVLF-EM) data are interpreted in order to indicate most geological boundaries. The inverted airborne TVLF data along adjacent profiles over each contact show the resistivity variations at depth and laterally localize the geological contacts while delineating resistive bedrocks in all cases. To control the resistivity images across these profiles, the ground-based measurements of the radio magnetotelluric data combined with controlled-source magnetotelluric data (RMT/CSMT) have been performed to plot resistivity variations with higher resolution. The obtained resistivity images are in good agreement with the ones acquired from airborne TVLF data. A relatively conductive layer with an average thickness of 20 to 30 m of the Quaternary deposits, increasing in thickness from east to the west, and a resistive basement with much higher resistivity on the granites than the shales are resolved from the geoelectric sections. This paper confirms that the combined geophysical surveys (i.e., airborne magnetometry/TVLF along with ground-based RMT/CSMT) reduce the uncertainty arising from geological mapping. It is shown that the airborne TVLF data have high capability for image resistivity variation along geological contacts in the study area while its resolution has been confirmed by the ground-based RMT/CSMT survey. [ABSTRACT FROM AUTHOR]

This paper describes a general framework of incorporating magnetic data as prior information in the modeling of an iron deposit based on sparse drilling boreholes. Since multivariate kriging of a sparse pattern of drilling yields lower uncertainty in mineral deposit modeling, a model of magnetic susceptibility as a second variable which has an acceptable level of correlation with the first variable, iron, is used to make a model of iron grade. Before applying an inversion technique to recover a 3D model of magnetic susceptibility, the normalized full gradient (NFG) approach is used to generate an approximate visualization of probable magnetic anomaly. Based on the NFG result, we have acquired appropriate dimensions of composites to apply the geophysical inversion method. The subsurface of the desired area of the magnetic anomaly is divided into a mesh with a large number of rectangular prisms (composites) with unknown susceptibilities. The recovered magnetic susceptibility model which has a correlation equal to 0.543 with the iron grade is used as a second variable in order to apply a multivariate set of kriging equations. The collocated cokriging method as a modified version of the cokriging system is used to interpolate the iron grade based on a model of magnetic susceptibility. This method has been applied to a real case study in Zanjan Province of Iran, showing its effectiveness when we encounter with such lack of drilled boreholes in the study area. [ABSTRACT FROM AUTHOR]

ABSTRACT The aim of this work is to introduce the application of the fuzzy ordered weighted averaging method as a straightforward knowledge-driven approach to explore porphyry copper deposits in an airborne prospect. In this paper, the proposed method is applied to airborne geophysical (potassium radiometry, magnetometry, and frequency-domain electromagnetic) data, geological layers (fault and host rock zones), and various extracted alteration layers from remote sensing images. The central Iranian volcanic-sedimentary belt in Kerman province of Iran that is located within the Urumieh-Dokhtar (Sahand-Bazman) magmatic arc is chosen for this study. This region has high potential of mineral occurrences, especially porphyry copper, containing some active world-class copper mines such as Sarcheshmeh. Two evidential layers, including the downward continued map and the analytic signal of such filtered magnetic data, are generated to be used as geophysical plausible traces of porphyry copper occurrences. The low values of the resistivity layer acquired from airborne frequency-domain electromagnetic data are also used as an electrical criterion in this study. Four remote sensing evidential layers, including argillic, phyllic, propylitic, and hydroxyl alterations, are extracted from Advanced Spaceborne Thermal Emission and Reflection Radiometer images in order to map the altered areas associated with porphyry copper deposits. The Enhanced Thematic Mapper plus images are used to map iron oxide layer. Since potassium alteration is the mainstay of copper alteration, the airborne potassium radiometry data are used. Here, the fuzzy ordered weighted averaging method uses a wide range of decision strategies in order to generate numerous mineral potential/prospectivity maps. The final mineral potential map based upon desired geo-data set indicates adequately matching of high-potential zones with previous working mines and copper deposits. [ABSTRACT FROM AUTHOR]

In this paper, a set of airborne magnetic data and helicopter-borne frequency domain electromagnetic data are used in order to retrieve a joint model of magnetic susceptibility and electrical conductivity. The study area called Kalat-e-Reshm located in Semnan province of Iran consists of an arc-shaped porphyry andesite covered by sedimentary units which has high potential of mineral occurrences especially porphyry copper. Based upon Occam approach assuming a multilayered earth model with fixed thicknesses, a conductivity-depth plot from airborne electromagnetic data is generated to image the desired porphyry type geological structure. The 3D visualization of the 1D conductivity models along all flight lines provided a resistive geological unit which corresponds to the desired porphyry andesite. A multi-constraint method is also applied on the airborne magnetic data to recover a 3D distribution of magnetic susceptibility model which reveals extension of an arc-shaped porphyry andesite at depth. The high susceptibility, low conductivity porphyry andesite shows a thicker structure than what is shown on the geological map and extends to depth. [ABSTRACT FROM AUTHOR]

This paper describes the usage of clustering methods including self-organizing map (SOM) and fuzzy c-means (FCM) which are applied to prepare mineral prospectivity map. Different evidential layers, including geological, geophysical, and geochemical, to evaluate Now Chun copper deposit located in the Kerman province of Iran are used. Clustering approaches are used to reduce the dimension of 13 feature vectors derived from different layers. At first, Geospatial Information Systems (GIS) is employed to analyze and integrate different layers, and the area under study is prioritized to five classes. Then, the SOM as an unsupervised classification method is carried out to classify this area into five clusters. Produced clusters are compared with GIS prospect map, while the SOM results are matched with the GIS output. The main reason to use the FCM is that a vector belongs simultaneously to more than one cluster so that membership values of each cluster can be mapped. As a consequence, clusters generated by the SOM and FCM are considerably matched with five-class-map of the GIS approach. The chosen cluster as a high potential location to additional drilling is matched to the main alteration and faults zone. To validate generated clusters for mineral potential mapping, geological matching of study area and selected proper cluster can be a satisfactory way. Finally, clustering methods can be a very fast approach to interpret the area under study. [ABSTRACT FROM AUTHOR]

This paper presents a neural network approach to determine 2D inverse modeling of a buried structure from gravity anomaly profile. The results of the applied neural network method are compared with the results of two other methods, least-squares minimization and the simple method. Sphere, horizontal cylinder and vertical cylinder and their gravity effects are considered as the synthetic models and the synthetic data, respectively. The synthetic data are also corrupted with noise to evaluate the capability of the methods. Then the Dehloran bitumen map in Iran is chosen as a real data application. Anomaly value of the cross-section, which is taken from the gravity anomaly map of Dehloran bitumen, is very close to those obtained from these methods. [ABSTRACT FROM AUTHOR]