Staged Factor Analysis Research Articles

A hybrid framework for Detection of Multivariate porphyry Cu Signatures and Anomaly Enhancement: Incorporation of SFA, GMPI, and Grey Wolf Optimization

Geochemical data from stream sediment are commonly employed for regional scale mineral exploration, as they can be utilized to detect geochemical anomalies associated with mineralization processes. However, recognizing efficient multielement geochemical signatures related to target mineralization using stream sediment geochemical data can be challenging due to the intricate nature of mineralization events. To achieve this goal, this article proposes a combination of statistics, logistic functions and machine learning techniques. Initially, the staged factor analysis (SFA) approach was applied to the data to determine the geochemical footprints associated with porphyry copper mineralization in the Varzaghan area. Subsequently, the geochemical mineralization probability index (GMPI) was utilized to facilitate appropriate separation, more precise identification, and the transfer of data into a fuzzy space. By using a combination of SFA and GMPI, it was feasible to enhance the detection of geochemical halos and concealed anomalies. Ultimately, the output of these two approaches was subjected to an improved clustering technique known as GWO-K-means to detect geochemical anomalies. The findings indicated that there is a strong relationship between known mineral occurrences (KMOs) and the identified anomalous areas. The success rate curve demonstrated that the optimized model outperformed the K-means model, proposing that optimization can boost the accuracy in identifying geochemical targets. Overall, the outcomes of this study can be beneficial for proper planning of future exploration activities.

Geochemical mapping by stream sediments of the NW portion of Quadrilátero Ferrífero, Brazil: Application of the exploratory data analysis (EDA) and a proposal for generation of new gold targets in Pitangui gold district

Quadrilátero Ferrífero (QF) is a worldwide known metallogenetic province and has in its NW portion the Pitangui greenstone belt, one of the new frontiers for gold exploration in that region, presenting important gold deposits, such as Turmalina and São Sebastião. Among several actions of the geological and mineral resources projects carried out by Geological Survey of Brazil (SGB/CPRM) in the QF region, a high-density geochemical survey (1 sample/4 km2) by stream sediments was accomplished in its NW portion to better understand the behavior and distribution of chemical elements along the main geological units, especially those related to gold mineralization (Au, As, Sb, Bi, etc.). The exploratory data analysis (EDA), regarding uni-, bi-, and multivariate analyses, point to an important chemical control of the stream sediments by the geological units and help to describe some lithological features and to understand the behavior and distribution of hydrothermal processes and gold mineralization. This procedure defined the main geochemical associations, including the association As, Sb, Cu and Bi linked to Ni, Cr and Co, interpreted as gold mineralization hosted in the metavolcanosedimentary lithotypes. The staged factor analysis (SFA) considering compositional data (CoDa), logistic function, and fractal model approaches provided the generation of the geochemical mineralization probability index (GMPI), which produce a GMPI map for gold mineralization. The calculations did not take into account the gold concentrations but only the pathfinder elements and the model validation by the prediction-area plot, confirm the high performance of this approach. The gold mineralization map, in addiction to satisfactorily predicting the location of most known gold occurrences, was able to indicate new potential targets for prospecting new gold deposits.

Exploration targeting of copper deposits using staged factor analysis, geochemical mineralization prospectivity index, and fractal model (Western Anti-Atlas, Morocco)

The present work aims to identify the geochemical signatures of copper deposits in the Igherm area (Anti-Atlas, Morocco) using stream sediment data. We applied factor analysis on the data normalized by log transformation. This approach identified three geochemical data associations (F1, F2, and F3) which were then used to calculate the values of the geochemical prospectivity index (GPMI). The results indicated that the large GMPI Cu values correlate strongly with the surface indices. A concentration-surface (C-A) fractal classification was applied to separate the geochemical anomalies from the regional background. To recognize and geo-position the significant geochemical signatures of the deposit, we applied the prediction zone (P-A) plot. A geographic information system (GIS) was used to define the spatial distribution of the anomalies at the sub-watershed level. The results represent the first approach to evaluating potential mineral resources at the scale of the Anti-Atlas: 90% of the anomalous sub-catchments mapped are located along the Precambrian-Cambrian transition (Base Series), 67% are traversed by major tectonic faults, and 40% contain copper showings and/or deposits. The remaining 60% are devoid of any visible surface copper occurrences. They constitute promising areas for future exploration of Cu deposits hidden under the Cambrian cover.

Identification of Multi-Element Geochemical Anomalies for Cu–Polymetallic Deposits Through Staged Factor Analysis, Improved Fractal Density and Expected Value Function

Identification of Multi-Element Geochemical Anomalies for Cu–Polymetallic Deposits Through Staged Factor Analysis, Improved Fractal Density and Expected Value Function

Geochemical exploration for Li in regional scale utilizing Staged Factor Analysis (SFA) and Spectrum-Area (S-A) fractal model in north central Iran

The main aim of this study was to outline the lithium anomalies by a regional exploration, at an area of 7800 km2, in Semnan province (north central Iran) using the Staged Factor Analysis (SFA) and Spectrum-Area (S-A) fractal model based on stream sediments and rock samples. Results derived via the SFA denote that Li was located in a factor as F2-4 with B, Cs, U and Rb which was utilized for calculation of the threshold values by the S-A method. The F2-4 data were classified by the fractal model for determination of the Li anomalies. Main anomaly for F2-4≥ 1.5 was situated in the SW and northern parts of this region. Furthermore, Li high grades of rock samples were correlated with main F2-4 anomalies. The main anomalies were correlated with geological particulars of Li mineralization types which represent that the main F2-4 anomalies associate with volcanic and tuff units in the SW part, and overlapped with clay minerals in the northern sector of this region. On the other hand, there are proper potential for Li mineralization which is demonstrated by this method.

Application of modified wavelet and fractal modeling for detection of geochemical anomaly

Aim of this research is to propose a wavelet-fractal model for detection of geochemical anomaly based on a modified Morlet Wavelet Kernel (MMWK). This study was carried out for detection of lithium anomalies regarding stream sediment samples. Major Li anomalies commence from 51.1 ppm obtained by the wavelet-fractal modeling based on Decomposed Wavelet Transformation (DWT) using a conventional Morlet wavelet. A modified kernel for the Morlet wavelet was utilized for separation of the Li anomalies. The MMWK benefits from the use of L1 norm (instead of L2) which consequently provides more robustness and resistance to outliers for stream sediment data. Results derived via the modified Morlet kernel-fractal modeling show that there are principal lithium anomalies with Li ≥ 55.2 ppm. In addition, staged factor analysis was used and Li was placed with Rb, Cs and F in F2-2. Then, the F2-2 main anomalies commence from 2.7 and 2.9 using the conventional Morlet and MMWK, respectively. Subsequently, both methods were compared by a log-ratio matrix with an Overall Accuracy (OA) due to rock samples retaken from these anomalies. These rock samples were classified using a Concentration-Number (C-N) fractal model and high intensive anomalous samples with Li ≥ 126 ppm. This correlation indicated that the OA between the main obtained anomalies by the MMWK and the rock samples are higher than conventional Morlet-fractal modeling.

Discrimination Between Hypogene and Enriched Supergene Zones Using Fractal and Sequential Factor Analysis in Milloieh Porphyry Copper Deposit, SE Iran

The aim of this study was to separate the different mineralized zones consisting of supergene enrichment and hypogene zones in Milloieh porphyry Cu deposit (SE Iran) based on subsurface data and using the Staged Factor Analysis (SFA) and Concentration-Number (C-N) fractal modelling. Results obtained by SFA indicate that Cu and Mo were situated in a factor as F2-2 which was modelled by the C-N fractal method for separation of the mineralized zones. The supergene enrichment zone derived via the SFA and C-N fractal analysis contains 0.86% for Cu and 5.3 ppm for Mo. Moreover, the hypogene zone obtained by this modelling has Cu and Mo average values of 0.59% and 1.88 ppm, respectively. These mineralized zones were correlated with mineralographical data obtained by the polished section microscopic studies which indicate that the obtained zones based on the SFA and C-N fractal model are consistent with the geological and mineralization data.

Delineation of geochemical haloes using the developed zonality index model by multivariate and fractal analysis in the Cu–Mo porphyry deposits

Main aim of this study is to propose a developed zonality index for Cu–Mo porphyry deposits based on staged factor analysis with Number-Size (N–S) fractal model. This method was used in an Iranian porphyry system as named Serenu (SE Iran). The developed zonality index was a ratio between factors related to major and minor ore elements based on in-situ soil samples. In this study, two factors were extracted after three stages of factor analysis including F1-3 (Pb, Zn, Au, Ag, Mn, Cd, As) and F2-3 (Cu–Mo). Based on these factors, F1−3F2−3is determined as a developed zonality index. Furthermore, conventional zonality index was calculated on the basis of(Pb×Zn)(Cu×Mo). Then, these zonality indexes were classified by the N–S fractal modeling. Results obtained by the fractal modeling indicates that the main anomalies for the developed zonality index is correlated with plutonic units, mineralized rock samples and exploratory boreholes especially in the central and SE parts of the studied area. This methodology can be utilized for other types of ore mineralization.

A New Discovery of Ag-Pb-Zn Mineralization via Modern Portable Analytical Technology and Stream Sediment Data Processing Methods in Dajiacuo Area, Western Tibet (China)

Tibet, which is rich in mineral resources, is a treasure trove for geological explorers. However, prospecting work has been slow, especially in the western part, due to the precipitous terrain, changeable climate and low access. Hence, modern advanced field analytical technology and effective data processing methods play significant roles in rapid and efficient exploration in Tibet. In this paper, spectrum-area fractal modeling and portable X-ray fluorescence analysis (pXRFA) were used to identify and verify geochemical anomalies associated with Ag-Pb-Zn mineralization based on a stream-sediment dataset of 39 elements in the Dajiacuo-Xurucuo region of western Tibet. First, staged factor analysis (SFA) was used to obtain the Ag-Pb-Zn-Cd geochemical assemblage. Second, the first-factor pattern obtained using SFA was dissociated by a spectrum-area (S-A) fractal model and a digital elevation model (DEM)-based geochemical model (DGM) was constructed. Finally, the sections of Ag, Cd, Pb, and Zn were obtained using pXRFA. The results show that Ag-Pb-Zn-Cd enrichment zones were mostly located around the contact belt of volcanic rocks and intrusions, or along SE-NW trending faults. Considering the variable terrain and catchment basin, the extension of long axes of Ag-Pb-Zn-Cd anomalies into higher elevation areas that are favorable for Ag-Pb-Zn mineralization should be investigated. Anomaly maps created with the aid of a DGM show promising potential for mineralization in the Dajiacuo-Xurucuo region, and abundant Ag-Pb-Zn mineralization was identified with the assistance of pXRFA in the source areas for the geochemical anomalies in the Dajiacuo. We conclude that SFA and the S-A fractal model constitute a valid tool to identify or verify geochemical anomalies in areas of low-density stream-sediment sampling. The pXRFA can accurately determine the source of geochemical anomalies and improve anomaly verification efficiency.

Geochemical exploration for lithium in NE Iran using the geochemical mapping prospectivity index, staged factor analysis, and a fractal model

Geochemical exploration for rare metals, specifically lithium, is essential on a regional scale based on their demand and consumption in recent years. The main objective of this study was to delineate lithium anomalies in regional exploration utilizing the geochemical mapping prospectivity index (GMPI), staged factor analysis (SFA), and a concentration-number (C-N) fractal model based on stream sediments. The case study area is 26000 km 2 and is located in the Khorasan Razavi province (NE Iran). In addition, rock samples were used to validate the Li anomalies identified. Results derived via the SFA show that Li was located on a factor denoted as F1-3 with Be, Cs, F, Nb, Sn, Th, U and W, which was used for calculation of the GMPI values. The GMPI data were classified by the C-N fractal method for determination of the Li anomalies. The main anomalies with GMPI ≥ 0.7 and Li ≥ 48 ppm were situated in the SE, SW, north and south parts of the study region. Li grades of rock samples were categorized by the C-N fractal technique for validation of F2-2 anomalies using a log-ratio matrix. The main anomalies were correlated with related lithological units of Li mineralization types. This correlation indicates that the main GMPI–Li anomalies are associated with granitic–pegmatitic units in the central and SE parts, and overlap with clay minerals in the northern and southern sectors of this region. There is good potential for Li mineralization as demonstrated by this hybrid method.

Detection of metallic prospects using staged factor and fractal analysis in Zouzan region, NE Iran

The Zouzan area is located in the SE of Torbat-e- Heydarieh city (NE Iran). This area is located on north part of the Lut block which consists of basaltic andesite, tuff and porphyritic andesite. The purpose of this research is to investigate the enrichment of metal elements in the Zouzan area and provide a metallic geochemical model. For this purpose, 488 stream samples were analyzed using by ICP-MS. The results were interpreted using staged factor analysis to determine the components that are rich in the study area. However, Concentration-Number (C-N) fractal modeling was used for interpretation of these data and presenting a final model. The results of this study indicate the concentration of anomalies of the Au, Ag, Cu, Pb and Zn as main elements in the central, NE and SW of the Zouzan area. Moreover, results obtained by the staged factor analysis shows the elements of the specified components by this method that enriched together in the study area. The place of enrichment is in the central NE and SW parts of the study area.

Delineation of podiform-type chromite mineralization using geochemical mineralization prospectivity index and staged factor analysis in Balvard area (SE Iran)

The aim of this work was to delineate the prospects of podiform-type chromite by staged factor analysis and geochemical mineralization prospectivity index in Balvard area, SE Iran. The stream sediment data and fault density were used as the exploration features for prospectivity modeling in the studied area. In this regard, two continuous fuzzified evidence layers were generated and integrated using fuzzy operator. Then fractal modeling was used for defuzzification of the prospectivity model obtained. Furthermore, the prediction-area plot was used for evaluation of the predictive ability of the generated target areas. The results obtained showed that using the prospectivity model, 82% of mineral occurrences was predicted in 18% of the studied area. In addition, the target areas were correlated with the geological particulars including ultrabasic and serpentinization rocks, the host rocks of the podiform-type chromite deposit type.

Determination of lithium prospects using fractal modeling and staged factor analysis in Torud region, NE Iran

The main purpose of this study was to outline the different lithium prospects in Torud district (NE Iran) using the staged factor analysis (SFA) and power spectrum-area (S-A) fractal modeling based on stream sediments and lithogeochemical data. Results obtained by the SFA represent that Li was situated in a factor 2 from stage 2 (F2-2) with Cs, Rb and F which was modelled and classified by the S-A fractal modeling for determination of the Li anomalies. There are three classes for geochemical anomalies. Major anomaly for Li was located in the NE part of this region which equal to F2-2≥2.8. Moreover, Li values of lithogeochemical data were classified by the concentration-number (C-N) fractal model for validation of the F2-2 anomalies using logratio matrix. These anomalies were correlated with lithogeochemical data utilizing logratio matrix which show that the main Li prospect based on the SFA and S-A fractal model is located in the NE part of the Torud region within magmatic rocks.

A comparison study on detection of key geochemical variables and factors through three different types of factor analysis

Large numbers of variables have been measured to explain different phenomena. Factor analysis has widely been used in order to reduce the dimension of datasets. Additionally, the technique has been employed to highlight underlying factors hidden in a complex system. As geochemical studies benefit from multivariate assays, application of this method is widespread in geochemistry. However, the conventional protocols in implementing factor analysis have some drawbacks in spite of their advantages. In the present study, a geochemical dataset including 804 soil samples collected from a mining area in central Iran in order to search for MVT type Pb-Zn deposits was considered to outline geochemical analysis through various fractal methods. Routine factor analysis, sequential factor analysis, and staged factor analysis were applied to the dataset after opening the data with (additive logratio) alr-transformation to extract mineralization factor in the dataset. A comparison between these methods indicated that sequential factor analysis has more clearly revealed MVT paragenesis elements in surface samples with nearly 50% variation in F1. In addition, staged factor analysis has given acceptable results while it is easy to practice. It could detect mineralization related elements while larger factor loadings are given to these elements resulting in better pronunciation of mineralization.

Identification and mapping of lithogeochemical signatures using staged factor analysis and fractal/multifractal models

To identify geochemical signatures related to deposit-type sought, staged factor analysis (SFA), fractal and multifractal methodologies were utilized to process the lithogeochemical data in the Laochang district, Yunnan province, SE China. In this study, 13 elements, including W, Mo, Pb, Zn, Ag, As, Bi, Hg, Sb, Sn, Cu, Cd and Mn, were analyzed in 825 rock samples. A centred logratio (clr) transformation was applied to address the closure problem of geochemical data. The results clearly demonstrate that SFA greatly assisted with not only interpretation of geochemical zonation patterns genetically related to intrusions, but also extraction of multi-element associations signature of the mineral deposit-type sought. The geochemical distribution patterns can be characterized by multifractal parameters such as singular exponent α , as well as τ ″(1) and Δ α , which are effective for depicting the spatial heterogeneity signatures of geochemical data. Through geochemical mineralization prospectivity indices (GPMI) and C-A model, the results illustrate that element distribution signatures are greatly influenced by fault density. The integrated methods are useful for identifying significant multi-element anomalous signature and generating reliable target areas for further prospecting.

Prospecting of Ni mineralization based on geochemical exploration in Iran

The aim of this study is to prospect Ni mineralization based on stream sediments and lithogeochemical data in Iran. Ophiolite belts of Iran specifically Nain-Baft, Khoy complex and East of Iran belts are studied in this research. To do this, stream sediment and lithogeochemical data are interpreted by Concentration-Area (C-A), Concentration-Perimeter (C-P), Concentration-Number (C-N) fractal models and staged factor analysis in the ophiolitic belts. Lithogeochemical data only used for general exploration in the Patang prospect which is a proper target for Ni exploration. Results obtained by this research indicate that Ni has a direct correlation with Cr and Co and also, the main Ni anomalies are associated with chromite occurrences/deposits. Moreover, the main Ni anomalies occur in ophiolite sequences and ultrabasic rocks especially serpentinized peridotite and harzburgite serpentinite. In addition, Ni mineralization is prospected in Patang (eastern Iran) and Anarak (Central Iran) prospects for detailed exploration.

Identification and mapping of geochemical patterns and their significance for regional metallogeny in the southern Sanjiang, China

The southern Sanjiang region, southwestern China, comprises various continental blocks, tectonic sutures and arcs. This complex structural area is a proper place for the recognition of geochemical patterns and understanding of regional metallogenesis. Considering each individual tectonic unit (i.e., western South China block, Ailaoshan suture, Simao block, Changning–Menglian suture, Baoshan block and Tengchong block) as a statistical unit, this study identifies the distribution patterns of geochemical elements, distinguishes elemental associations for different geological backgrounds (controlled by the regional lithology) and diverse mineralizations, and thereby delineates the mineralized anomalies. To achieve the goals, the kriging interpolation, staged factor analysis and local singularity technology were utilized after the centered logratio (clr) transformation of stream sediment geochemical data. The spatial distributions of metallogenic elements (Au, Ag, Cu, Pb, Zn and Sn) show that not all the areas with high concentrations of elements contain ore deposits. It means that the formation of ore deposits is an independent anomalous geological event, not necessarily related to the original regional abundance of geochemical elements. The different element associations obtained by staged factor analysis of 28 elements are able to reveal the geological backgrounds and metallogenic signatures for different tectonic units. For example, the element associations for the first, second and third factors (Fs) in the western South China block are groups of Ti–Co–V, Sb–Ag–As and Th–U–Be (see text for the detailed element associations), which represent the Emeishan flood basalts, Ag–Pb mineralization and felsic rocks, respectively. Similarly, the element associations Co–V–Cu, Be–U–Th and Sb–Ag–Au for F1, F2 and F3 in the Ailaoshan suture respectively represent the ophiolite complex and mafic rocks, felsic rocks and Au mineralization. The associations from F1 to F4 in the Simao block are Th–U–Sn, V–Co–Cr, Cd–Ag–Pb and Au–Sb, which represent the Linchang batholith, mafic rocks, Ag–Pb–Zn mineralization and Au mineralization, respectively. The distribution patterns of their factor scores can roughly distinguish the anomalies caused by regional backgrounds and mineralizations. The local singularity of factor scores for mineralization can further identify mineralized areas in most of the tectonic units. Nevertheless, an exception occurs in the western South China block where the background element association, Ti–Co–V–Cu–Ni–Cr–P–Mn–Nb–Zn (F1), obscures the Au signature. Therefore, we substituted the single element Au for mineralized element association (F1) to perform singularity mapping, and obtained better result. It was concluded that the combination of staged factor analysis, local singularity and tectonic setting is effective in regional metallogenic potential analysis.

Delineation of geochemical anomalies based on stream sediment data utilizing fractal modeling and staged factor analysis

Recognition of significant geochemical signatures and separation of geochemical anomalies from background are critical issues in interpretation of stream sediment data to define exploration targets. In this paper, we used staged factor analysis in conjunction with the concentration-number (C–N) fractal model to generate exploration targets for prospecting Cr and Fe mineralization in Balvard area, SE Iran. The results show coexistence of derived multi-element geochemical signatures of the deposit-type sought and ultramafic-mafic rocks in the NE and northern parts of the study area indicating significant chromite and iron ore prospects. In this regard, application of staged factor analysis and fractal modeling resulted in recognition of significant multi-element signatures that have a high spatial association with host lithological units of the deposit-type sought, and therefore, the generated targets are reliable for further prospecting of the deposit in the study area.

Using robust staged R-mode factor analysis and logistic function to identify probable Cu-mineralization zones in Khusf 1:100,000 sheets, east of Iran

Factor analysis method is a multivariate analysis technique that is widely used for the interpretation of stream sediment geochemical data. The purpose of factor analysis is describing the changes in a set of multi-element geochemical data by reducing the dimension of the data and variables to a number of factors that can present the hidden association between elements. Differences in mobility, physical, and chemical properties of the elements and the nature of the factor analysis method in which the matrix of all data is used cause paragenes elements not to be found on the output of factor analysis. In this research, to improve the output of factor analysis for deriving the best reagent multi-element mineralization, robust staged factor analysis method was used according to the close nature of geochemical data in order to identify the Cu-mineralization potential in Khusf 1:100,000 sheets located at the east of Iran. The robust staged factor analysis enhances the recognition of anomalous geochemical signatures and increases geochemical anomaly intensity and the percentage of the total explained variability of data. As indicated by the results of the study, few anomalous zones have been found in the study area. The observation of chalcopyrite and malachite mineralization in andesite and dacite–andesite rocks in a region during the field study confirms the effectiveness of the robust SFA technique. Such studies can be used by mine engineers and geologists for designing an optimum grid exploration on the next exploration steps.

Delineation of supergene enrichment, hypogene and oxidation zones utilizing staged factor analysis and fractal modeling in Takht-e-Gonbad porphyry deposit, SE Iran

The aim of this study is to delineate the different mineralized zones consisting of supergene enrichment hypogene and oxidation zones in Takht-e-Gonbad porphyry Cu deposit (SE Iran), subsurface data and using the staged factor analysis (SFA) and concentration–volume (C–V) fractal modeling. Results obtained by SFA reveal that Cu and Mo were situated in a factor as F1–5 which was modeled by C–V fractal modeling for separation of the mineralized zones. The supergene enrichment zone obtained by the SFA and C–V fractal modeling contains 1.16% for Cu and 241ppm for Mo. Moreover, the hypogene zone derived via the SFA and C–V fractal modeling has Cu and Mo mean values of 0.65% and 109ppm. These mineralized zones were correlated with geological models utilizing logratio matrix which indicate that the obtained zones based on the SFA and C–V fractal model are consistent with the geological models. The results derived via logratio matrix reveal overlapping between geological and mathematical models. As a result, combination of the C–V fractal modeling and SFA can be used to delineate mineralized zones based on multivariate data.