Geochemical Anomaly Maps Research Articles

Determining thresholds of arsenic and mercury in stream sediment for mapping natural toxic element anomaly using data-driven models: a comparative study on probability plots and fractal methods

Natural toxic elements such as arsenic and mercury are widespread distributed in nature and the over accumulation of them usually has adverse effects on both environment and human beings. Therefore, to assess potential toxicity of these elements in natural environment such as water, soil, and stream sediment, it is significant to determine thresholds between anomalous and background geochemical data for identifying and mapping target areas with toxic element geochemical anomaly. In terms of data-driven, this study aims to demonstration of using the four statistical models that are box plot (BP), cumulative distribution function (CDF) diagram, concentration-number (C-N) fractal, and concentration-area (C-A) fractal methods to determine the threshold values of As and Hg in stream sediment for mapping their potential toxic anomaly in Pangxidong district, southwestern Guangdong, China, and compare the results critically. Firstly, a wide range of threshold values have been estimated by the four investigated data-driven methods. Then, the three threshold values of As and Hg that separate background data from low, moderate, and high geochemical anomalies determined by BP, CDF diagram, C-N fractal, and C-A fractal models were compared with the three level baselines defined in the Environmental Quality Standard for Soil in China (EQSSC). Furtherly, the geochemical anomaly maps of As and Hg were outlined by using the three threshold values to investigate the performance of the four data-driven methods in practical application of mapping natural toxic element anomaly. Finally, the contaminations of As and Hg in the study area were evaluated based on the suitable geochemical anomaly maps derived by the applicable data-driven method (i.e. CDF diagram). The main conclusions are that (1) of the four investigated data-driven methods, CDF diagram is the most suited for assist in the estimation of proper threshold values of As and Hg dividing geochemical background data from anomalies in the Pangxidong district; and (2) the contaminations of As mainly come from natural sources (i.e., bedrocks), while the contaminations of Hg are most likely to originate from anthropogenic factors (i.e., mining activities) in the Pangxidong district. The target areas, particular to the southern Wuling Reservoir, which have been identified with high As and Hg anomaly, probably should be taken effective measures to prevent the occurrences of pollution and toxicity from these elements.

Identification of multi-element geochemical anomalies using unsupervised machine learning algorithms: A case study from Ag–Pb–Zn deposits in north-western Zhejiang, China

Identifying significant geochemical anomalies is a fundamental task in mineral exploration. In terms of stochastic process, a specific ore-forming process can be regarded as a small probability event since the deposit type sought rarely occurs in the crust of the earth. Therefore, machine learning algorithms for anomaly detection are suitable for identification of mineralization-related geochemical anomalies. However, identification of significant geochemical anomaly signatures is often hindered by irrelevant and/or redundant variables in a geochemical dataset. It is then necessary to extract significant features by means of dimensional reduction before applying unsupervised machine learning algorithms for anomaly detection. In this study, six unsupervised machine learning methods including isolation forest (IF) and five hybrid models that combined IF with dimensional reduction techniques were implemented and evaluated in identifying Ag–Pb–Zn polymetallic mineralization-related geochemical anomalies using stream sediment geochemical data from north-western Zhejiang Province, China. The five hybrid models involved combining IF with hierarchical clustering (HC-IF), principal component analysis (PCA-IF), stacked denoising autoencoder (SDAE-IF), both HC and PCA (HC-PCA-IF) and both HC and SDAE (HC-SDAE-IF). Anomalies identified by these six methods were evaluated by the receiver operating characteristic (ROC) curve method. The spatial overlay of known mineral deposits and the identified geochemical anomaly maps showed that a strong spatial association existed between the locations of discovered mineral deposits and high geochemical anomaly areas. Furthermore, the area under the curve (AUC) values suggested that the HC-SDAE-IF hybrid model performed best and the HC-IF and HC-PCA-IF models performed better than the IF, PCA-IF and SDAE-IF models. This study demonstrated that: (i) IF is a useful and efficient unsupervised machine learning algorithm for detection of Ag-Pb-Zn polymetallic mineralization-related geochemical anomalies in the study area; and, (ii) the proper manipulation of dimensional reduction and extraction of mineralization-related geochemical variables can not only significantly improve the performance of unsupervised machine learning algorithms including deep learning methods in geochemical anomaly detection, but also be instrumental in clarifying the geological meaning of the identified geochemical anomaly. In this regard, reducing dimensionality and extracting mineralization-related variables (i.e. elements) from raw geochemical data is strongly recommended before applying unsupervised machine learning methods, particularly the sophisticated deep learning algorithms, in geochemical anomaly detection.

Identifying Geochemical Anomalies Associated with Gold Mineralization Using Factor Analysis and Spectrum–Area Multifractal Model in Laowan District, Qinling-Dabie Metallogenic Belt, Central China

The Laowan deposit is a typical gold deposit in the Qinling-Dabie metallogenic belt, which produces the most gold resources in Central China. After being explored for decades, follow-up exploration requires additional theoretical support. In this study, the factor analysis (FA) and spectrum–area (S–A) multifractal model were used to process multi-element geochemical data from 369 samples collected in the study area for identifying the geochemical anomalies associated with gold mineralization. The results showed that: (1) the mean Au content in this region is up to 1000 times higher than the Au background values of the upper crust of the South Qinling unit; (2) the factor analysis revealed that Au, Ag, Cu, As, Sb, and S can be used as direct ore-prospecting criteria; (3) the observed elemental zonation is consistent with the zonation of metallic elements in the magmatic–hydrothermal system. This supports the magmatic–hydrothermal origin of the Laowan deposit; (4) the spectrum–area fractal model can help to decompose the geochemical patterns in a complex geological setting. The decomposed geochemical anomaly map obtained by the S–A multifractal model indicated that highly anomalous areas have a great relationship with the Au occurrence and can be a guidance for further exploration in the study area.

Mineral potential mapping for Au and As using Gap statistic method in multivariate mode

Quantitative descriptions of geochemical patterns and providing geochemical anomaly map are important in applied geochemistry for mineral exploration and environmental assessment. To identify and separate geochemical anomalies, several statistical methodologies (nonstructural and structural) are presented by researchers. The Gap statistic method is a traditional approach which does not depend on the location of samples. This method is one of the univariate methods which assess the threshold based on one variable (one element). In this study, the Gap statistic applied on grade values of samples for separating anomaly from background in the case of Au and As resulted from surface sampling in Susanvar district, Iran. Then, this method is employed to delineation geochemical anomalies in multivariate mode using Euclidean distance. Finally, bivariate geochemical anomaly map is provided for Au and As using multivariate Gap statistic method. Results show that dispersion of the anomalous samples indicated by this method has decreased and they are located near each other in comparison with other univariate nonstructural ones. Also, it is seen that performance of Gap statistic method is increased for estimating threshold and separation in multivariate mode using Euclidean distance. So that, prepared mineral potential map for Au and As using multivariate Gap statistic shows that anomalous area is corresponded to Au occurrences in the region.

Geochemical Anomaly and Mineral Prospectivity Mapping for Vein-Type Copper Mineralization, Kuhsiah-e-Urmak Area, Iran: Application of Sequential Gaussian Simulation and Multivariate Regression Analysis

In this paper, sequential Gaussian simulation (SGS) and number–size (N–S) fractal modeling were used for copper geochemical anomaly mapping in the western part (training area) of Kuhsiah-e-Urmak area, Iran. Then, according to the generated anomaly model in the training area, mineral potential mapping (MPM) was performed for the entire study area based on a well-fitted regression model as a data-driven method. In order to select the best model, six multivariate regression models including two linear, two quadratic, and two cubic functions were examined. For developing a mineral potential map of the study area, the geochemical anomaly raster map of the training area was utilized to create six models based on the values of geo-data sets. According to the results of $$R^{2}$$,$$R_{\text{adj}}^{2}$$, and $${\text{EF}}$$, the fourth model, generated using a quadratic function, was found to be the superior model compared with the rest of regression models defined in this paper. Based on the mathematical formula derived for the superior model, the geo-exploration data sets were synthesized to generate a potential map showing favorable areas for prospecting copper. The potential map generated was verified by the results of lithogeochemical sampling conducted in the training area and field observations in the entire study area.

Identification of geochemical anomaly and gold potential mapping in the Sonakhan Greenstone belt, Central India: An integrated concentration-area fractal and fuzzy AHP approach

The present work combines Concentration-Area (C-A) fractal model with the Fuzzy Analytical Hierarchy Process (FAHP) on a GIS platform for mapping gold potential in the Sonakhan Greenstone Belt, India. A set of stream sediment geochemical data obtained over a part of the study area was used to delineate potential regions of gold mineralisation. In order to develop a suitable predictive model, the gold exploration target was taken as target (dependent variable) and concentration of pathfinder elements of gold such as Au, As, Ag, Hg, Sb and Se in the stream sediments were used as predictors (independent variables). The C-A fractal model was applied to the geochemical data of each element to decompose anomaly and background components of the spatial dispersion of element concentration in the study area. AHP combined with fuzzy set theory (FAHP) was used to determine the priority or, the weight of each evidential geochemical anomaly map. The Gold exploration targets are delineated by multiplying the weights with the respective fuzzy normalised geochemical anomaly map of the gold pathfinder elements and integrating the weighted geochemical evidential layers using fuzzy Gamma operator. The reliability of the outcome was assured by the coincidence of known gold mineralisation with the potential regions delineated in the final gold potential map. The areas with high potential are good targets for detailed exploration of gold in the region. The results highlight that (a) application C-A fractal model can effectively separate geochemical anomalies as it considers the spatial variation of the data unlike the conventional statistical methods which account only the frequency of the data, (b) the FAHP allows more flexibility in judgements of multiple decision makers in a group and reduces the inconsistency of the result which is essentially required in the knowledge-driven predictive mapping of mineral prospectivity.

Mapping Geochemical Anomalies Through Integrating Random Forest and Metric Learning Methods

Extracting geochemical anomalies from geochemical exploration data is one of the most important activities in mineral exploration. Geochemical anomaly detection can be regarded as a binary classification problem. The similarity between geochemical samples can be measured by their distance. The key issue of this classification is to find the intrinsic relationship and distance between geochemical samples to separate geochemical anomalies from background. In this paper, a hybrid method that integrates random forest and metric learning (RFML) is used to identify geochemical anomalies related to Fe-polymetallic mineralization in Southwest Fujian Province of China. RFML does not require any specific statistical assumption on geochemical data, nor does it depend on sufficient known mineral occurrences as the prior knowledge. The geochemical anomaly map obtained by the RFML method showed that the known Fe deposits and the generated geochemical anomaly area have strong spatial association. Meanwhile, the receiver operating characteristic curves for the results of RFML and another method, namely maximum margin metric learning, indicated that the RFML method exhibited better performance, suggesting that RFML can be effectively applied to recognize geochemical anomalies.

Singularity mapping of bulk leach extractable gold and −80# stream sediment geochemical data in recognition of gold and base metal mineralization footprints in Biga Peninsula South, Turkey

Stream sediments remain the preferred geochemical sampling media for regional mineral exploration programs in most parts of the world where there is a well-defined drainage system. In Au exploration the analysis of either Bulk Leach Extractable Gold (BLEG) or acid-extractable Au in the −80# (<180 μm) stream sediment fraction are the two most common approaches. Western Turkey contains a variety of mineral deposit types and geological settings. Using a geochemical database derived mainly from company exploration programs, a study is made on the efficiency of singularity mapping technique in detecting the known mineral deposits or prospects.The composite media of samples are representative of sampling locations but also are extremely diluted. Therefore, recognition and mapping of significant geochemical anomalies of composite media still remains a challenge. In this study, geochemical assay results were subjected to multifractal analyses to detect gold geochemical anomalies, which are related to gold mineralization. In this regard, a singularity mapping technique was applied to enhance the single element geochemical signatures of BLEG sand −80# stream sediment data. Since the singularity mapping technique does not define threshold values for delineation of anomalies, number-size (N-S) and concentration-area (C-A) fractal models were applied to delimit the geochemical anomalies as target areas for further exploration. A hundred percent of the known gold deposits and prospects discovered in the study area are delineated by BLEG Au stream sediment anomalies by N-S and C-A multifractal modeling. Moreover, 89% of known gold prospects of the study area are delineated within the recognized anomalous zones of Au in −80# stream sediment data. The comparison of the delineated geochemical anomalies of BLEG and −80# stream sediment with those of rock chip samples demonstrated the efficiency of the applied methodology.

Geochemical modelling and mapping of Cu and Fe anomalies in soil using combining sequential Gaussian co-simulation and local singularity analysis: a case study from Dedeyazı (Malatya) region, SE Turkey

Mapping and modelling of the spatial distribution of geochemical anomalies is a key task for geochemical exploration. This case study explores mapping and modelling of Cu and Fe geochemical anomalies combining the Sequential Gaussian Co-simulation (SGCS) method and Local Singularity Analysis (LSA) in the Malatya region, SE Turkey. A total of 652 topsoil samples were collected on a regular grid design from the study area. A cell declustering technique was applied to the raw data. Both geometric and zonal anisotropy exist in the directional variogram of Cu and Fe. SGCS was applied to create maps representing an equally probable spatial distribution of Cu and Fe geochemical anomalies. SGCS results have been validated by a variety of tests including the reproduction of the variograms, histograms, descriptive statistics and contour plots. LSA is based on a sliding window estimation approach applied to the grid data created for SGCS realization. LSA was carried out and mapped via the results of the realization on the same grid layout. Critical anomaly threshold values of the variables were identified using the singularity and quantile plot. Integration of SGCS and LSA results provides quantification of the uncertainty of spatial distribution and determination of Cu and Fe critical thresholds.

A combined approach using spatially-weighted principal components analysis and wavelet transformation for geochemical anomaly mapping in the Dashui ore-concentration district, Central China

Sometimes geochemical anomalies linked with buried mineral deposits are too weak to be recognized by conventional methods. In this study, element concentration data from a stream sediment survey were subjected to a combined method of SWPCA (spatially-weighted principal components analysis) and WT (wavelet transformation) to derive a geochemical anomaly model for epithermal Au deposits in the Dashui ore-concentration district. The SWPCA was applied as a data integration method to extract information related to mineralization in the geochemical data. The WT was applied as a powerful tool for recognizing mineralization related anomalies in a complex geochemical field and for enhancing weak anomalies from background. The SWPCA–WT geochemical anomaly model indicated high favorability values for the known mineral occurrences and out-performed PCA–WT and SWPCA models. The SWPCA–WT geochemical model generated in this study provides a robust guide for further gold exploration in the Dashui ore-concentration district.

A comparative study of fractal models and U-statistic method to identify geochemical anomalies; case study of Avanj porphyry system, Central Iran

The most significant aspect of a geochemical exploration program is to define and separate the anomalous values from the background. In the past decades, geochemical anomalies have been identified by means of various methods. Most of the conventional statistical methods aiming at defining the geochemical concentration thresholds for separating anomalies from the background have limited the efficiency in the areas with complex geological settings. In this work, three methods including the Concentration-Area (C-A) and Spectrum-Area (S-A) fractal models, and the U-statistic method are applied to identify the geochemical anomalies in Avanj porphyry system due to a complex geological and tectonic setting. The results obtained show that the S-A and U-statistic methods present more acceptable outputs than the C-A method. The C-A model acts well to identify the geochemical anomalies within a region including a simple geochemical background; however, the model has limitations within a region including a complex geological setting, where each sub-area is characterized by different geochemical fields. The U-statistic method, by considering the location of sampling points, their spatial relation, and radius of influence for each point in the estimation of anomaly location, overcomes the limitations of the C-A model. The S-A model is a powerful tool to decompose mixed geochemical patterns into a geochemical anomaly map and a varied geochemical background map. The output of this method shows the analysis of geochemical data in the frequency domain, which can provide new exploratory information that may not be revealed in the spatial domain. Eventually, it can be pointed out that the accuracy of the S-A fractal model for determining the thresholds is higher than the other two methods mentioned.

Applying a Structural Multivariate Method Using the Combination of Statistical Methods for the Delineation of Geochemical Anomalies

Quantitative descriptions of geochemical patterns and providing geochemical anomaly map are important in applied geochemistry. Several statistical methodologies (nonstructural and structural) are presented in order to identify and separate geochemical anomalies. Structural methods take the sampling locations and their spatial relation into account for estimating the anomalous areas. In the present study, a nonstructural method (Mahalanobis distance method as a multivariate method) is used and U-statistic is considered as a structural method to assess prospective areas of Susanvar district as a gold mineralization index in the Torud-Chah Shirin mountain range of Semnan Province, northern Iran. Results show that the U-statistic method is an efficient method according to spatial distribution of the anomalous samples. In the present study, according to the ability of U-statistic method in combining with other methods, the goal is to use and develop Mahalanobis distance method in structural mode. For this purpose, Mahalanobis distance should be combined with a structural method which devotes a new value to each sample based on its surrounding samples. For this reason, the combination of efficient U-statistic method and multivariate Mahalanobis distance method has been used to separate geochemical anomalies from background. Combination results show that the performance of these two methods is more accurate than using just one of them. Because samples indicated by the combination of these methods as anomalous are less dispersed and closer to each other than in the case of using just the U-statistic and other nonstructural methods such as Mahalanobis distance. Also it was observed that the combination results are closely associated with the defined Au ore indication within the study area. Finally, univariate and bivariate geochemical anomaly maps are provided for Au and As, which have been, respectively, prepared using U-statistic and its combination with Mahalanobis distance method.

A method for extracting anomaly map of Au and As using combination of U-statistic and Euclidean distance methods in Susanvar district, Iran

Quantitative descriptions of geochemical patterns and providing geochemical anomaly map are important in applied geochemistry. Several statistical methodologies are presented in order to identify and separate geochemical anomalies. The U-statistic method is one of the most important structural methods and is a kind of weighted mean that surrounding points of samples are considered in U value determination. However, it is able to separate the different anomalies based on only one variable. The main aim of the presented study is development of this method in a multivariate mode. For this purpose, U-statistic method should be combined with a multivariate method which devotes a new value to each sample based on several variables. Therefore, at the first step, the optimum p is calculated in p-norm distance and then U-statistic method is applied on p-norm distance values of the samples because p-norm distance is calculated based on several variables. This method is a combination of efficient U-statistic method and p-norm distance and is used for the first time in this research. Results show that p-norm distance of p=2 (Euclidean distance) in the case of a fact that Au and As can be considered optimized p-norm distance with the lowest error. The samples indicated by the combination of these methods as anomalous are more regular, less dispersed and more accurate than using just the U-statistic or other nonstructural methods such as Mahalanobis distance. Also it was observed that the combination results are closely associated with the defined Au ore indication within the studied area. Finally, univariate and bivariate geochemical anomaly maps are provided for Au and As, which have been respectively prepared using U-statistic and its combination with Euclidean distance method.

Natural Resources Research Publications on Geochemical Anomaly and Mineral Potential Mapping, and Introduction to the Special Issue of Papers in These Fields

In its 26 years of existence, the journal of Natural Resources Research (NRR) has published and continues to publish papers on geochemical anomaly and mineral potential mapping. This is consistent with its aims and scope to publish quantitative studies of natural (mainly but not limited to mineral) resources exploration, evaluation and exploitation, including environmental and risk-related aspects. Over the years, NRR has contributed significantly more to the publication of developments in mineral potential mapping and notably less to the publication of developments in geochemical anomaly mapping. In more detail, NRR has contributed significantly more to the publication of research on development of robust quantitative methods for analysis and synthesis of spatial evidence of mineral potential but notably less to the publication of research on development of geologically focused models of mineral potential. The editorship of NRR recognizes the latter as a challenge to promote further research on development of numerically robust as well as geologically focused mineral potential models, and this special issue is a major initiative in response to that challenge. The recent inclusion of Natural Resources Research for coverage by the Clarivate Analytics (formerly the Institute for Scientific Information) in the Science Citation Index Expanded™ and Journal Citation Reports® (JCR) Science Edition will help make Natural Resources Research meet that challenge.

Enhancement and Mapping of Weak Multivariate Stream Sediment Geochemical Anomalies in Ahar Area, NW Iran

In this study, stream sediment geochemical data have been subjected to robust principal components analysis (RPCA) and singularity mapping (SM) to enhance and map significant multivariate geochemical anomalies (i.e., mineralization-related) in Ahar area, NW Iran. The RPCA was applied to (a) account for the compositional nature of stream sediment geochemical data using suitable log-ratio transformation, (b) modulate the effect of outliers in component estimation and (c) derive a multivariate geochemical footprint of mineralization. The SM was applied to extract anomalous patterns of the multivariate geochemical footprint of mineralization. The exploration targets were then delineated using Student’s t-statistics analysis. The correlations of mapped exploration targets with the known mineral occurrences and mineralization-related patterns were further evaluated using normalized density index and overall accuracy analyses.

Analysis of Zoning Pattern of Geochemical Indicators for Targeting of Porphyry-Cu Mineralization: A Pixel-Based Mapping Approach

In this paper, a pixel-based mapping of geochemical anomalies is proposed to avoid estimation errors resulting from using interpolation methods in the modeling of anomalies. The pixel-based method is a discrete field modeling of geochemical landscapes for mapping lithogeochemical anomalies. In this method, the influence area of each composite rock sample is the whole area covered by a pixel where the materials of the sample were taken from. In addition to the pixel-based method, because delineation of mineral exploration target areas using geochemical data is a challenging task, the application of metal zoning concept is demonstrated for vectoring into porphyry mineralization systems. In this regard, different geochemical signatures of the deposit-type sought were mapped in a model. Application of the proposed pixel-based method and the metal zoning concept is a powerful tool for targeting areas with potential for porphyry copper deposits.

Comparing U-statistic and nonstructural methods for separating anomaly and generating geochemical anomaly maps of Cu and Mo in Parkam district, Kerman, Iran

In applied geochemistry, obtaining quantitative descriptions of geochemical patterns and identifying geochemical anomalies are important. To identify and separate geochemical anomalies, several statistical methodologies (nonstructural and structural) are presented by researchers. In this study, four nonstructural methods including threshold assessment method based on median and standard deviation, median absolute deviation (MAD), P.N product and Sinclair’s method are selected first and then U-statistic is considered as a structural method to compare their performance. Subsequently, the best method is used to assess prospective areas of Parkam district. Results show that P.N and Sinclair’s methods are not always efficient. MAD method reduced the background well and roughly increased the correlation factor of points. However, U-statistic method includes both mentioned advantages meaning in addition to reducing outlier data effect, it regularizes anomalous values and also their dispersion is reduced significantly. It is possible to determine anomaly areas according to anomalous samples positioning so that denser areas are more important. Finally, lithogeochemical map of study area is generated for copper and molybdenum. In this map, the Cu mineralization which is delineated by this method is closely associated with the defined potassic alteration zone (according to alteration map of the study area), and also, the delineated Mo mineralization is exactly associated with the phyllic alteration and is spatially conformable with the zone defined for it.

Investigation of copper and gold prospects using index overlay integration method and multifractal modeling in Saveh 1:100,000 sheet, Central Iran

Abstract This study aims at prospecting copper and gold promising areas in Saveh 1:100,000 sheet, situated in Urumieh-Dokhtar magmatic belt (Central Iran). Geographic information system (GIS) is effective in recognition of probable mineral resources by collecting, processing, exploration layer weighting and integrating thematic maps. As there is no certainty in different geological phenomena, modeling and integrating information layers are used to obtain suitable results for determining potential areas. In this study, index overlay method, which is a combination of software processing and expertise knowledge, was used. The survey layers consist of the lithologic units, geophysical data, mineralization, faults and structures and alteration. For more exact survey, concentration-area (C-A) fractal modeling was applied to the map of results obtained from integration. Applying fractal model to this map, the results of the original map were prioritized and became more limited. In the end for assessing the validity of determined promising areas, the results were compared with geochemical anomaly maps of stream sediments and also field observation was performed. Areas with high exploration priority with limited extend exist in center, west and some parts of NW, conformed to granodiorite intrusives and also next priority exists in center, west, NE and NW conformed to granite, diorite, and also subvolcanic rocks respectively. These prioroties are related to fault systems. In order to perform X-ray Diffraction (XRD), Inductively Coupled Plasma – Mass Sepctrometry (ICP-MS) analyses and microscopic studies, lithogeochemical samplings were done. All investigations indicate the possibility of pyrite, chalcopyrite and galena epithermal mineralizations resulted from post magmatic fluids, and also secondary products like hematite, goethite, and malachite as a result of oxidation processes in these areas.

Assessment of prospective areas for providing the geochemical anomaly maps of lead and zinc in Parkam district, Kerman, Iran

There are several statistical methodologies presented for separating anomalous values from background leading to determination of anomalous areas. These methods range from simple approaches to complicated ones and include nonstructural and structural methods, subtraction separation method and so on. Structural methods take the sampling locations and their spatial relation into account for estimating the anomalous areas. The U-statistic method is one of the most important structural methods. It considers the location of samples and carries out the statistical analysis of the data without judging from a geochemical point of view and tries to separate subpopulations and also to determine anomalous areas. In the present study, several nonstructural methods including assessment of threshold based on median and standard deviation, median absolute deviation (MAD) and P.N product are used and U-statistic is considered as structural method to assess prospective areas of Parkam district. Results show that MAD method reduced background well and P.N method increased correlation of points. However, U-statistic method plays the role of both mentioned advantages meaning in addition to reducing outlier data effect, it regularizes anomalous values and also their dispersion is reduced significantly. It is possible to determine anomaly areas according to anomalous samples positioning so that denser areas are more important. Finally, lithogeochemical map of study area is provided for lead and zinc.

Analysis and modeling of geospatial datasets for porphyry copper prospectivity mapping in Chahargonbad area, Central Iran

In mineral exploration, proposing new prospects is a main issue and geospatial information system (GIS) offers different techniques for this procedure. Mineral prospectivity maps (MPMs) can reduce costs and errors for predicting mineral potentials. In this research, data layers have been analyzed and integrated for porphyry copper mineral prospectivity mapping in Chahargonbad sheet of Kerman Province, Central Iran. First of all, geological, geochemical, geophysical, and satellite imagery have been processed separately and assigned according to their favorability in porphyry copper mineralization using index overlay and fuzzy logic methods which was lead to introduce two priorities for further exploration. Eight hundred forty-six geochemical stream sediment samples were analyzed using multifractal concentration-number model to generate geochemical anomaly maps. Afterward, airborne magnetic data was used to extract magnetic edges which can represent structures (faults and fractures) and lithological contacts. Airborne radiometric data is also used to identify felsic units based on K, Th, and U (RGB) ternary image. Thereafter, satellite imagery (ASTER, ETM+ images) was processed based on relative absorption band depth (RBD), principle component analysis (PCA) and Crosta methods in order to separate alteration zones in the study area. Totally, 80 km2 has been detected as the first priority that is located in the northwest (Kuh Panj), center (south of Hamid Abad and east of Sorkhgino), southeast (Chenar Kaf), and southwest (north of Alimoradi) of this sheet. Also, the second priority (97 km2) is located in the center (southeast of Syloieh), southeast (Chenar Kaf), east (Cheheltan), and northwest (south of Kuh Panj).