Geochemical Anomaly Maps Research Articles

Stream sediment geochemical anomaly mapping based on the dynamic enhanced weighted drainage catchment basin method for mineral exploration

Accurately determining the presence of subterranean deposits is challenging due to the intricate geological processes that give rise to these deposits. The use of stream sediment geochemical data to identify and map geochemical anomalies is a widely employed technique in mineral exploration and offers a robust indicator of the possible presence of mineral deposits. This research presents a comparison between the weighted drainage catchment basin (WDCB) technique and its improved version, the dynamic enhanced weighted drainage catchment basin (DE-WDCB) approach, in the context of mineral exploration. We constructed a model of the catchment basin for the study area, clarified the upstream and downstream relationships between the basins, determined the background value in each basin via the trend surface method, and carried out geochemical anomaly mapping based on the DE-WDCB method. Then we conducted two comparative experiments using the WDCB method, employing both dynamic and nondynamic classification methods for anomaly classification. Finally, we compared the mineralization of the three methods and find that the DE-WDCB method exhibited superior performance in identifying sedimentary manganese ore anomalies, followed by the dynamic grading WDCB method and the nondynamic grading WDCB method. These results indicated that the DE-WDCB method revealed excellent performance when applied to stream sediment geochemistry in prospecting, hence enhancing its utility in mineral resource exploration.

Compositional balance analysis for Cu–Zn(±Co) geochemical anomaly mapping in Trøndelag county, Norway

Trøndelag county has been recently the focus of a new mineral exploration wave given its well-known base metal mineralization potential associated with the occurrence of volcanogenic Cu–Zn (±Co ± Ag ± Au) massive sulfide deposits (VMS). This study evaluates a regional low-density soil sampling survey by implementing principal component analysis (PCA), and compositional balance analysis (CoBA) to target prospective areas for base metal mineralization. Principal component analysis indicates a dominant base metal mineralization signature characterized by a correlation between As and Cu–Cr–Co–Ni, which is consistent with the occurrences of mafic metavolcanic and associated low K–Ca metasedimentary rocks. However, several other Cu–Zn occurrences hosted in K- and/or Ca-rich (meta-)sedimentary (e.g., Røros districts) and felsic (meta-)igneous lithologies have been overlooked by this approach. Data- and knowledge-driven balances (i.e., isometric log-ratios) were then constructed to enhance these “weaker” Cu–Zn (±Co) mineralization fingerprints. Most of the data-driven balances (dcobals) are shown to be very noisy, and less useful for base metal geochemical anomaly mapping compared to their more coherent knowledge-driven counterparts (kcobals). However, the kcobals (e.g., for Cu, Zn and Co) highlight relatively large anomalous areas, which makes them less practical if used as individual exploration vectors. To further define zones of economic interest, common anomalous areas (defined as kcobal >75th percentile) have been filtered out from selected kcobal pairs. This has resulted in the identification of several prospective areas that correspond with 15%–20% of the Trøndelag county.This study not only explores the combined use of CoBA and PCA for detecting base metal anomalies, but also elaborates on some factors that can affect the interpretation and performance of multivariate approaches (e.g., mineralization/alteration extension versus survey resolution, geological framework, and deposit type). The visualization and implementation of CoBA presented in this paper aim to improve geoscientists’ understanding of the reach and limitations of using isometric log-ratios for mineral exploration studies.

Regional Geochemical Characteristics of Lithium in the Mufushan Area, South China

With the explosive growth in demand for lithium (Li) resources, the Mufushan area has been a hotspot for Li deposit exploration in China in recent years. Geochemical maps and geochemical anomaly maps are basic maps in the geochemical exploration of mineral resources. A fixed-value method to contour a Li geochemical map is presented here, in which Li concentrations are divided into 19 levels on 18 fixed values, ranging from 5 μg/g (corresponding to the detection limit) to 1858 μg/g (corresponding to the cut-off grade of Li deposit in hard-rock type) and illustrated in six color tones corresponding to Li areas of low background, high background, low anomaly, high anomaly, mineralization in clay-type, and mineralization in hard-rock type. The geochemical map of Li in the Mufushan area using the new fixed-value method indicates that the study area belongs to the high background area, and the known Li deposits are located in the high anomaly areas. In addition, the geochemical anomaly map of the Mufushan area is drawn using the method of seven levels of classification, and indicates that the known Li deposits are all in the anomaly areas, with anomaly levels not lower than the second level. Furthermore, four other areas are recognized for Li resource potential based on the geochemical map and geochemical anomaly map in the Mufushan area.

Intelligent mapping of geochemical anomalies: Adaptation of DBSCAN and mean-shift clustering approaches

Cluster analysis can be used to organize samples and generate ideas regarding the multivariate geochemistry of given dataset. Traditional clustering techniques have the drawbacks of high computational complexity and poor adaptability to big data. Hence, there has recently been much focus on creating better clustering algorithms. Although many clustering algorithms have been applied, and some produce notable clustering results, the performance efficiency of algorithms is often highly dependent on the values the user chooses for the parameters. Currently, density-based spatial clustering of applications with noise clustering (DBSCAN) is widely utilized in image processing, bioinformatics, and social network analysis owing to its ability to detect clusters of various shapes. Even though partitional clustering techniques may be effective when the number of clusters K is known in advance, they cannot implement non-convex clustering and rapidly return to a local optimum. This study proposes the concept of DBSCAN clustering for stream sediment geochemical data. In this respect, the geochemical data collected from Varcheh district, SW Iran, were processed using the clr transformation before applying DBSCAN. Then, PCA was used to minimize the dimension of variables and specify the mineralization-related elements. In the following, one of the PCs connected with mineralization (PC2) was chosen for further analysis. DBSCAB, Mean-shift and Fuzzy K-means algorithms were used to monitor the multi-element geochemical anomalies linked to MVT PbZn deposits in the study area. According to Davies-Bouldin and Silhouette as two validation metrics, it can be deduced that the three SCB models are advantageous, however, the model generated by DBSCAN is preferable to the model generated by Mean-shift and Fuzzy K-means.

Delineation of Integrated Anomaly with Generative Adversarial Networks and Deep Neural Networks in the Zhaojikou Pb‐Zn Ore District, Southeast China

AbstractGeochemical maps are of great value in mineral exploration. Integrated geochemical anomaly maps provide comprehensive information about mapping assemblages of element concentrations to possible types of mineralization/ore, but vary depending on expert's knowledge and experience. This paper aims to test the capability of deep neural networks to delineate integrated anomaly based on a case study of the Zhaojikou Pb‐Zn deposit, Southeast China. Three hundred fifty two samples were collected, and each sample consisted of 26 variables covering elemental composition, geological, and tectonic information. At first, generative adversarial networks were adopted for data augmentation. Then, DNN was trained on sets of synthetic and real data to identify an integrated anomaly. Finally, the results of DNN analyses were visualized in probability maps and compared with traditional anomaly maps to check its performance. Results showed that the average accuracy of the validation set was 94.76%. The probability maps showed that newly‐identified integrated anomalous areas had a probability of above 75% in the northeast zones. It also showed that DNN models that used big data not only successfully recognized the anomalous areas identified on traditional geochemical element maps, but also discovered new anomalous areas, not picked up by the elemental anomaly maps previously.

The Relationship Between Geological and Environmental Aspects with the Anomalies of Track Elements and Heavy Metal Elements in the Volcanic Area of the Muria Peninsula

The Muria Peninsula is a complex of volcanic activity located in the back arc of Sunda, which includes Kudus, Jepara, and surrounding districts. Volcanic activity on Mount Muria, can be used to study geological conditions, which were closely related to chemical elements. The presence of chemical elements in the river sediments of the Muria Peninsula was also influenced by anthropogenic heavy metal pollutants present in river deposits along the Muria Peninsula. The purpose of this study was to determine the relationship between the geological conditions and the environmental conditions of the Muria Peninsula with the anomaly of track elements and heavy metal elements. The methods used in this research were the pick test method, XRF geochemistry and the results were correlated into a geochemical anomaly map. The results showed that there were trace elements anomalies of Ce and Nd in Jepara area which were influenced by Muria shoshonitic rocks. Anomaly of Cu element was found in Jepara area, anomaly of Mn element was found in Jepara and Demak, and anomaly of Zn element was found in Jepara and Demak. Anomaly of Cu, M,n and Zn elements were high in upstream area and caused by land use of dry agriculture, paddy fields and settlement areas.

Modelling spatial uncertainty of geochemical anomalies using fractal and sequential indicator simulation methods

Mapping of geochemical anomalies is crucial to exploration and environmental geochemistry. The complex geochemical landscape, multiple geological sources and various secondary surficial processes impose a certain degree of spatial uncertainty in mapping of geochemical anomalies. Quantifying this uncertainty is significant for improving the efficiency of environmental monitoring or mineral prospecting. In this paper, sequential indicator simulation (SISIM) was used to assess local and spatial uncertainties of geochemical anomalies, and the concentration–area (C–A) fractal model was employed to determine the geochemical threshold prior to SISIM analysis. To illustrate the uncertainty of Ag geochemical anomalies, Ag concentration data of 1880 soil samples collected from NE of the Dong Ujimqin Banner district of Inner Mongolia, North China, was used in this study. Based on a set of simulation realizations of Ag concentrations, it was concluded that areas with low local (i.e. single-location) uncertainty of Ag concentrations entail low risk for mineral exploration. However, the spatial uncertainty for multi-locations showed that the joint probability statistics were stricter than local uncertainty. Therefore, combining local probability and spatial joint probability for delineating geochemical anomalies of Ag is more acceptable and reliable. The hybrid approach using the C–A fractal model and SISIM provides a new way to delineate anomalous areas by considering the uncertainty of spatial distributions of geochemical elements. Thematic collection: This article is part of the Applications of Innovations in Geochemical Data Analysis collection available at: https://www.lyellcollection.org/topic/collections/applications-of-innovations-in-geochemical-data-analysis

Improvement of geochemical prospectivity mapping using power spectrum–area fractal modelling of the multi-element mineralization factor (SAF-MF)

Geochemical anomaly mapping is a significant method in exploring for Au and associated elements using fine-fraction sediments. The fractal behaviour of geochemical elements has frequently been modelled for geochemical prospectivity mapping. In this study, concentration–area (C-A) and power spectrum–area (S-A) fractal modelling were utilized for Au concentration in stream sediments near the Saqqez mineralization area, NW Iran. A novel integrated approach based on the S-A fractal method and principal component analysis (PCA) was used to improve the geochemical anomaly map. In this approach, the multi-element mineralization factor returned from PCA was modelled by the S-A fractal method, and the obtained results were compared to the C-A and S-A fractal models of the element Au. Six known mineralization zones in the studied area were revisited to validate the fractal models. The elements Au, As, W, and Sb were identified as associated elements based on the log-ratio-transformed data in the shear zone gold mineralization. S-A fractal modelling of the multi-element mineralization factor (SAF-MF) of the Au-associated elements in the mineralization zones enhanced the geochemical halos and increased the prediction rate of mineralization zones using stream sediment geochemical data. The SAF-MF that models the behaviour of frequency signals of associated elements upgraded the geochemical anomaly mapping compared to conventional fractal models. Supplementary material: Supplementary information including figures S1–S5 is available at https://doi.org/10.6084/m9.figshare.c.6123537

Generation of Geochemical Exploration Targets from Regional Stream Sediment Data Using Principal Component and Factor Ana

A regional-scale stream sediment geochemical sampling was carried out with an averagesampling density of one sample per nine square-kilometre in Kiteto District, Manyara Region.A total of 358 stream sediment samples were collected and analysed for major and traceelements by X-ray fluorescence (XRF) and fire assay with atomic absorption spectrometry(AAS) finish methods. In this study, Factor and Principal Component Multivariate (FPCM)analyses have been used to the stream geochemical data to delineate potential mineralizationzones by plotting correlated factors as geochemical anomaly maps. Four factors that accountfor 73.7% of the total variance of the stream sediment geochemical data were established.Factor 1: Ni–Ba–Co–Cu–Sr which possibly defines the underlying metamorphosed graniticunits with some contribution from mafic and ultramafic rocks. Factor 2: Cr–Zn–Mn whichdefines crustal forming elements reflecting the mafic rocks. Factor 3 which entails Rb and Pbprobably attributed to metamorphosed granitic lithology. Factor 4 is arsenic, a chalcophileelement with affinity to sulfide phases. The FPCM analyses have been successfully indelineating potential target areas for gold, nickel and copper exploration in the study area.
 Keywords: Stream sediment; principal component; factor analysis; exploration targets;Kibaya-Kiteto, Manyara.

Identifying the source of gold geochemical anomalies in Jiaodong, eastern China: Tracking the occurrence of gold nanoparticles in a metallogenic system

The gold particles induced geochemical anomaly shows good potential for the prospecting of gold deposits all around world. Most of the discovered gold resources are located at geochemical related anomaly area in Jiaodong, which are associated with Micro-to nanoscale particle matter. However, it has been known little about the relationship between the occurrence of gold nanoparticles and their geochemical anomaly in multimedia accommodating to the detailed process of gold mineralization system and geochemical exploration. Micro-to nanoscale gold as nanoparticles are related to the hydrothermal fluid flows and precipitation among elemental migration. This paper presents gold nanoparticles in ore, constructed the fundamental link to geochemical anomaly distribution maps of gold in drainage sediments, wall rocks, and ore, aimed to identify the source of primary and secondary geochemical anomalies according to careful observation at nanoscale gold and revealed the genesis of gold mineralization and their potential in Jiaodong. A potential model of migration pathway of gold nanoparticle was built to understand the process of massive gold accumulation and the further prospecting in the Jiaodong Peninsula.

Mapping of geochemical anomalies in the Zagros ophiolites stream sediments by fractal and classical analyses: implication for mineral exploration in Iraqi Kurdistan

Mapping of geochemical anomalies in the Zagros ophiolites stream sediments by fractal and classical analyses: implication for mineral exploration in Iraqi Kurdistan

Geochemical anomaly definition using stream sediments landscape modeling

Geochemical anomaly mapping is a crucial part of environmental and mineral exploration projects. Stream sediments are the most frequently sampling medium for geochemical mapping in regional scales, resulting from various geological processes. To enhance geochemical anomaly in stream sediments, the geochemical landscape, including topographic factors and drainage catchment basin extracted from a digital elevation model (DEM), and average precipitations, were applied. These factors were taken into consideration in this research to investigate the influence of the geochemical landscape constraints on the Mo polymetallic mineralization anomaly of stream sediments in a typical hilly area of Jining, Inner Mongolia, China, using Bayesian Maximum Entropy (BME) approach. Geographically weighted regression was applied to construct probability density function of soft data based on slope, aspect, drainage catchment basin and precipitation. Stream sediment data, at a scale of 1:200,000 (hard data), was integrated with the soft data to map stream sediment anomalies associated with molybdenum-polymetallic mineralization. Considering the influences of geochemical landscape during stream sediment anomaly identification can make up the shortage of missing and incomplete observations in regional geochemical data. By integrating the geochemical landscape factors during the stream sediment anomaly identification process, the impacts of the geochemical landscape on sediment dispersion halos were addressed, and the anomaly identification accuracy was improved. The anomaly areas defined by the BME model demonstrated that 31.41% of the study area contained 75% of the known mineral deposits and consequently the uncertainty of geochemical anomalies was reduced.

Introducing a new approach of geochemical anomaly intensity index (GAII) for increasing the probability of exploration of shear zone gold mineralization

Increasing the prediction rate in the identification of mineralization zones using the stream sediment geochemical data is an essential issue in the regional exploration stage. The various univariate (such as fractal and probability plot (PP) methods) and multivariate methods (such as principal component analysis (PCA)) have been performed for interpreting the geochemical data and detecting the mineralization areas. In this study, a new geochemical criterion named geochemical anomaly intensity index (GAII) was proposed for geochemical anomaly mapping. This approach was developed based on the PCA method and the catchment basin coefficient (CBC). The GAII as a weighted geochemical index is calculated using the mineralization principal component (MPC) scores and CBC. GAII can be mapped and utilized for geochemical anomaly mapping and detecting the mineralization areas. Besides, GAII can identify paragenesis elements better than the current methods. In this research, GAII was successfully used to generate geochemical anomaly maps on shear zone gold mineralization in the southwest of Saqqez, NW Iran. The geochemical data have been divided into three groups based on catchment basins and the host rock type. Then the MPCs and paragenesis elements of Au mineralization have been obtained individually using PCA. Three mineralization paragenesis groups consisting of (Au, Sn), (Au, W), and (Au, As, Sb and Ba) have been recognized for different catchment basins of the southwest of Saqqez district using PCA. GAII was calculated and mapped based on the CBC(Au, Sn), CBC(Au, W), CBC(Au, As, Sb, Ba), and their MPC scores. GAII accurately detected the Au mineralization zones and improved the geochemical anomaly map in this area compared to the PP method, concentration-area fractal model, and U-spatial statistics method. The results demonstrated that GAII was successfully used for (a) identifying the mineralization paragenesis elements, (b) intensifying the geochemical anomaly, and (c) increasing the prediction rate of mineralization zones. The shear zone gold mineralization areas in the southwest of Saqqez district were effectively detected using this new data analysis approach. GAII has provided better results than the current PP method, concentration-area fractal model, and U-spatial statistics method.

Stream sediment geochemical exploration for gold in Central Eastern Desert, Egypt: Application of the concentration-number fractal model, factor analysis, and geochemical mineralization probability index

Geochemical exploration by stream sediment sampling using bulk leach extractable gold (BLEG) technique and applying concentration-number (C-N) fractal model, factor analysis (FA), and geochemical mineralization probability index (GMPI) resulted in the recognition of new Au occurrences around the Sukari gold mine in the central Eastern Desert of Egypt. The geochemical data of 128 stream sediment samples collected from the study area was used for delineating the geochemical anomalies and characterizing the dispersion trains of ore and associated elements (Au, Ag, As, Sb, Cu, Pb, Zn, Mo). Statistical analysis of the geochemical data applying the C-N fractal modeling enabled us to identify significant anomaly and background populations of the investigated elements and to construct reliable geochemical anomaly maps. Factor analysis using centered log-ratios (CLR), to address the problem of closed compositional data, revealed significant element associations for mineralization (Au, As, Mo, Zn, Ba), country rock compositions (Rb, Li, Be, Sn, Bi for granite, and Co, Cr, Ni for mafic rocks), and element mobility (e.g. Sb, Zr, and Ag). Weak and moderate Au anomalies that cannot be detected by factor score maps can be delineated clearly by using the C-N fractal method and GMPI distribution map. Our study revealed that Ag, As, and Sb are the main pathfinder elements for gold mineralization in arid to semiarid regions exemplified by the Sukari gold district. Silver can be used as a “direct” pathfinder, whereas As and Sb are “indirect” pathfinders for Au in such regions. The spatial distribution of Au and Ag anomalies indicate that gold mineralization in the Sukari district is structurally controlled. However, the spatial distribution of Cu, Pb, Zn, and Mo is controlled by mineralogical and lithological factors and is not related to any significant base metal deposits.

Application of the C-N fractal model, factor analysis and geochemical mineralization probability index (GMPI) for delineating geochemical anomalies related to a Mn-Fe deposit and associated Cu mineralization in west-central Sinai, Egypt

The west-central Sinai area includes one of the most productive manganese deposits in Egypt (Um Bogma Mn-Fe ore). The explored area is covered by a Carboniferous and Cambro-Ordovician sedimentary succession overlying Precambrian basement rocks. The purpose of this study is to delineate geochemical anomalies of ore and related elements and track their dispersion trains, which may lead to the discovery of unknown ore deposits. For this purpose, 143 stream sediment samples were analysed and the data were interpreted using the concentration–number (C-N) fractal model, factor analysis and the geochemical mineralization probability index (GMPI). Geochemical thresholds obtained from the C-N fractal model, factor scores and GMPI were used for constructing geochemical anomaly maps and delineating probable anomalous sites. The spatial distribution of Mn, Cu, Co, Pb and Zn anomalies was correlated to Mn and Cu mineralization sites whereas those of Fe and Cr were consistent with mafic rock distributions. Factor analysis revealed significant element associations for mineralization (Cu, Co, Mn, Zn, Pb), country rock composition (Fe, Cr) and element mobility (Cd, Zn). The spatial distribution of ore elements (Mn, Cu) delineated by the factor score and GMPI distribution maps was confirmed and more accurately interpreted using geochemical anomaly maps constructed based on the fractal-derived thresholds. Based on the current study, recent Cu and Mn mineralization sites may be suggested. Cobalt, Zn, Pb and Cd are suggested as efficient pathfinder elements for marine Mn deposits. A dispersion sequence of Cd>Mn>Co>Pb>Zn>Cu>Cr>Fe was proposed based on the threshold distribution patterns of these elements. Supplementary material: Tables S1–S3 and Figures S1 and S2 are available at https://doi.org/10.6084/m9.figshare.c.5463511

Quantitative Remote Sensing of Metallic Elements for the Qishitan Gold Polymetallic Mining Area, NW China

The recent development in remote sensing imagery and the use of remote sensing detection feature spectrum information together with the geochemical data is very useful for the surface element quantitative remote sensing inversion study. This aim of this article is to select appropriate methods that would make it possible to have rapid economic prospecting. The Qishitan gold polymetallic deposit in the Xinjiang Uygur Autonomous Region, Northwest China has been selected for this study. This paper establishes inversion maps based on the contents of metallic elements by integrating geochemical exploration data with ASTER and WorldView-2 remote sensing data. Inversion modelling maps for As, Cu, Hg, Mo, Pb, and Zn are consistent with the corresponding geochemical anomaly maps, which provide a reference for metallic ore prospecting in the study area. ASTER spectrum covers short-wave infrared and has better accuracy than WorldView-2 data for the inversion of some elements (e.g., Au, Hg, Pb, and As). However, the high spatial resolution of WorldView-2 drives the final content inversion map to be more precise and to better localize the anomaly centers of the inversion results. After scale conversion by re-sampling and kriging interpolation, the modeled and predicted accuracy of the models with square interpolation is much closer compare with the ground resolution of the used remote sensing data. This means our results are much satisfactory as compared to other interpolation methods. This study proves that quantitative remote sensing has great potential in ore prospecting and can be applied to replace traditional geochemical exploration to some extent.

Efficiency of singularity and PCA mapping of mineralization-related geochemical anomalies: a comparative study using BLEG and <180μm stream sediment geochemical data

In gold (Au) exploration, the analysis of both bulk leach extractable gold (BLEG) and acid-extractable Au in the <180μm stream sediment fraction are the two most common approaches. The Eskişehir- Sivrihisar region in Western Türkiye hosts several orogenic type mineral deposits. The purpose of this study is to delineate geochemical anomalies of ore-related elements and track their dispersion, which may lead to discovery of unknown ore deposits. This research also compares the capability of conventional statistical and principal component analysis (PCA), with concentration area (CA) and number-size/concentration (N-S/C) fractal methods as well as singularity index method to differentiate anomalous and background Au distributions. Known Au mineralization in the region of interest is strongly reflected in stream sediment BLEG Au patterns, which have robust singularity indices with C-A and N-S multifractal modeling and PCA. A hundred % of the Au deposits were detected using either BLEG Au and Ag singularity index mapping with C-A fractal analysis whereas the factor analysis of which revealed 85% efficiency. Several strong Au-Ag anomalies defined by the singularity index and factor analysis in this study requires further follow up for the discovery of new deposits.

Identifying geochemical anomalies and spatial distribution of gold and associated elements in the Zuru Schist Belt, northwest Nigeria

The Zuru Schist Belt is one of the gold-bearing schist belts in northwestern Nigeria. The gold mineralisation is mainly associated with NE-trending shear zones and is hosted in veins of quartz, quartz-tourmaline and quartz-feldspar. In this study, the spatial pattern of gold and associated elements was identified and delineated through statistical analysis of trace element geochemical data. Statistical analyses including spearman correlation, principal component analysis and hierarchical cluster analysis were applied on the geochemical data to decipher and interpret multi-element association related with gold mineralisation. Spearman correlation revealed significant positive correlation amongst Co, Mn, Fe, V, Ni and Zn (>0.70), while Au has moderate correlation with Ag, Bi and Cu (>0.30). Based on principal component analysis and hierarchical cluster analysis, three element associations can be recognised: (a) Zn-Ni-Co-Mn-Fe-V, (b) Au-Ag-Bi-Pb and (c) Zn-U-Th-Sr-La-Ba. The first two multi-element associations are related to gold mineralisation. The multi-element association of Zn-Ni-Co-Mn-Fe-V signifies lithogenic factor and associated hydrothermal alteration, while Au-Ag-Bi-Pb is indicative of gold mineralisation. The combination of Au and associated elements (Ag, Bi and Pb), which was achieved through the use of uni-element thresholds (median+2 median absolute deviation) in multi-element halo technique, enabled the mapping of multi-element geochemical anomaly. The uni- and multi-element geochemical anomaly maps show geochemical anomalies in the northern, central and southern parts of the area. These anomalies, largely restricted to metasedimentary rocks notably foliated quartzite, schist, and phyllite, represent where further exploration should be focused with more attention particularly in the northern part where higher concentration than previously reported was observed.

Mineral prospectivity modelling using singularity mapping and multifractal analysis of stream sediment geochemical data from the auriferous Hutti-Maski schist belt, S. India

The regional stream sediment geochemical survey has become an all the more important exploration technique for delineating potential mineral zones to target exploration. However, the effective appraisal of pathfinder elements has remained a fundamental challenge in exploration geochemistry. In this regard, the singularity mapping technique based on modern nonlinear theory and fractal-multifractal geometry is a new efficient method to distinguish thresholds of anomalies based on stream sediment geochemical data. The auriferous Hutti-Maski schist belt in Eastern Dharwar Craton (EDC), S. India has been taken up for delineation future targets using local singularity analysis (LSA) of stream sediment geochemical data. Ordinary Singularity Mapping (OSM) and Spatially Weighted Singularity Mapping (SWSM) are employed for mapping singularity of gold pathfinder elements (Au, As, Ag, Hg, Sb, Se, Cu, and Zn). In SWSM, a distance-based fuzzy weighting method is used to estimate the spatial weighting factor with respect to ore controlling shear zones. The local isotropic singularity index and the local anisotropic singularity index are estimated for each of the eight elements. The Student’s t-statistics based on Weight of Evidence (WOE) model is used to determine the threshold singularity index value to separate geochemical anomaly from background. Principal Component Analysis (PCA) is applied to singularity maps of multiple elements derived using OSM and SWSM techniques. The first principal component (PC-1) scores estimated by the OSM-PCA and SWSM-PCA lead to comprehensive geochemical anomaly maps. Results of two different singularity mapping techniques are compared using Geochemical Mineralization Probability Index (GMPI), Concentration-Area (C-A) fractal analysis and Prediction-Area (P-A) plots. It is observed that the SWSM method results in a smaller target with higher predictive ability compared to OSM method that delineates a larger target area.

Indicator element selection and geochemical anomaly mapping using recursive feature elimination and random forest methods in the Jingdezhen region of Jiangxi Province, South China

Determining indicator element association for mineralization can not only improve mineral exploration efficiency but also reduce the cost of unnecessary element analysis during geochemical exploration. This study provides a case study of Zhuxi tungsten-copper deposits and presents a workflow using recursive feature elimination and random forest methods to select the indicator element association for copper and tungsten mineralization in regional geochemical mapping. First, a training dataset containing positive and negative samples was built based on the known mineral deposits and mineral deposit model. Second, a 100-time simulation of recursive feature elimination with cross-validation based on random forest (RFECV-RF) was run to get a robust result of indicator elements by the ranking of variable importance. Third, the random forest (RF) method was used to integrate six indicator elements for mapping geochemical anomaly. The Youden index and prediction-area (P-A) plot were used to determine the threshold value for geochemical anomaly identification. The results demonstrated the hybrid workflow was useful to determine key indicator element for geochemical anomaly identification associated with copper and tungsten mineralization. Bi, Mo, Cu, Cd, W, and As were selected as the key indicator elements for geochemical exploration of Cu–W mineralization. Bi, Mo, W and Cu elements correspond to skarn and altered granite mineralization at depth while Cd and As elements correspond to the hydrothermal-vein mineralization at shallow levels. The result of receiver operating characteristic (ROC) curve showed that geochemical anomaly identified using the hybrid method proposed in this study had the best performance in producing comprehensive geochemical signatures. The six indicator elements also exhibited an excellent performance of identifying geochemical anomaly associated to Cu–W mineralization. This study provides a cost-benefit solution to reduce the cost of unnecessary elements concentration detection by determining a small number of key indicator elements using machine learning methods in the regional geochemical mapping for discovering mineral deposits.