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Mass movements of land, such as landslides, pose significant threats to human safety and infrastructure. This study focuses on advancing the understanding of landslide dynamics through the application of geophysical surveys, specifically the Electrical Resistivity Tomography (ERT) and Seismic Refraction Tomography (SRT). Unstructured meshing, as a pivotal technique in geophysics simulation studies, provides flexibility in discretizing complex geological structures. This method allows for refined mesh elements where needed, optimizing computational resources. In the field of geophysics, unstructured meshing is particularly advantageous for accurately representing subsurface heterogeneities. This study employs pyGIMLi, a Geophysical Inversion and Modelling Python library. This Python programming library, although devoid of a GUI, offers a comprehensive suite of tools for geophysical data analysis and inversion. This library incorporates unstructured meshing capabilities. This feature enhances the accuracy of simulations, enabling researchers to model intricate geological formations with more precision. Using this library empowers users to seamlessly generate, manipulate, and analyze unstructured meshes, facilitating robust simulations and detailed investigations of subsurface properties in geophysics. In this study, we present a novel approach to simulate a threelayered landslide using the ERT and SRT, coupled with inverse modelling through utilizing the unstructured meshing of the inversion area. The synthetic model produced has a depth of study extending to 65 meters. The SRT model reveals a dense coverage in layer 2, providing crucial information about the subsurface characteristics. The utilization of the ERT and SRT in tandem allows for a comprehensive understanding of the landslide structure, offering insights into detecting the slip surface of the landslide. The study's innovative methodology provides a robust framework for the analysis of complex geological scenarios. The results obtained from this simulation contribute to the broader knowledge of landslide dynamics and offer a valuable tool for assessing and mitigating landslide risks in similar geological settings. [ABSTRACT FROM AUTHOR]

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]

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 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]

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]

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]

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]