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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 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]

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]

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]