Logratio Matrix Research Articles

Application of an approximate geostatistical simulation algorithm to delineate the gold mineralized zones characterized by fractal methodology

The Zarshuran Carlin-type gold deposit is located in the NW part of the Sanandaj-Sirjan metamorphic zone which hosts Neoproterozoic carbonate and black shale rock units. Concentration-Volume (C–V) fractal model and Turning Band Simulation method have been applied to effectively separate gold mineralized stages in the Zarshuran deposit. Three mineralized stages determined by the means of C–V fractal model and Turning Bands Simulation method were correlated with the mineralized stages considering geological studies. The results revealed the presence of enriched (>37 ppm) and Au high grade (5.5–37 ppm) mineralized zones within the deposit. A log ratio matrix was used to correlate the results obtained by the C–V fractal model and turning band simulation for Au and lithological units which showed that the main mineralized zones occur in the black gouges and Jasperoids.

Fractal modeling and relationship between thrust faults and carbonate-hosted Pb-Zn mineralization in Alborz Mountains, Northern Iran

Central Alborz Metallogenic Belt is a major mineral province of northern Iran placed at the northern margin of the Arabia-Eurasia collision zone. This study aimed to at recognition the affiliation between reverse/thrust faults and Pb-Zn carbonate-hosted mineral deposition using fractal modeling. Thrusting is the major mechanism of faulting in this region in terms of length, frequency and density. The carbonate-hosted Pb and Zn occurrence/deposit were classified using the concentration-number fractal method. All faults then are categorized based on fault density, and concentration-area fractal methods are utilized for the thrust faults. According to our analysis, main fault density zones are situated at the central and western parts of the Alborz Mountains. Relationship among the major fault density zones and the Pb-Zn carbonate-hosted occurrences/deposits using logratio matrix reveals that there is a proper connection between reverse/thrust faults and the Zn-Pb carbonate-hosted occurrences/deposits. Moreover, the results were controlled by four ore deposits/prospects in this region which shows high-grade ores directly relate to faults especially reverse/thrust faults based on their distribution. This research indicates that this method can be used for other various ore deposit types that have been distributed by faulting.

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.

Application of multivariate geostatistical simulation and fractal analysis for detection of rare-earth element geochemical anomalies in the Esfordi phosphate mine, Central Iran

The present study was conducted to determine different geochemical anomalies of rare-earth elements (REEs) using a combined approach of stepwise factor analysis (SFA), sequential Gaussian simulation (SGS), and concentration–area (C-A) fractal modeling based on surface lithogeochemical samples obtained from the Esfordi phosphate mine (Central Iran). The Esfordi mine is one of the important mines in the Bafq metallogenic zone due to average and maximum grades of 0.5 and 1.7%, respectively for REEs. With SFA operating in two steps, the REEs were placed in the first factor of the second stage (F1-2). Then, SGS and C-A fractal modeling were performed on F1-2 factor scores for classification of anomalies. A log-ratio matrix was used to evaluate the correlation of these results with anomalous lithogeochemical samples, as well as to determine the relationship of anomalies with rock types and mineralized units and, finally, to validate the results of the SGS–fractal modeling. The results confirmed an appropriate correlation between F1-2 anomalies and high concentration in further rock samples. The main anomalies were found to have good correlation with an apatite–iron unit and in general with other apatite-bearing units based on overall accuracy values. The apatite-bearing units with high values of REEs were located in the northern and central parts of the mine. The results of the SFA, SGS and C-A fractal modeling show that this hybrid approach can be useful in determining anomalies with high accuracy.

Delineation of Alteration Zones Based on Wavelet Neural Network (WNN) and Concentration–Volume (C-V) Fractal Methods in the Hypogene Zone of Porphyry Copper Deposit, Shahr-e-Babak District, SE Iran

In this paper, we aim to achieve two specific objectives. The first one is to examine the applicability of wavelet neural network (WNN) technique in ore grade estimation, which is based on integration between wavelet theory and Artificial Neural Network (ANN). Different wavelets are applied as activation functions to estimate Cu grade of borehole data in the hypogene zone of porphyry ore deposit, Shahr-e-Babak district, SE Iran. WNN parameters such as dilation and translation are fixed and only the weights of the network are optimized during its learning process. The efficacy of this type of network in function learning and estimation is compared with Ordinary Kriging (OK). Secondly, we aim to delineate the potassic and phyllic alteration regions in the hypogene zone of Cu porphyry deposit based on the estimation obtained of WNN and OK methods, and utilize Concentration–Volume (C–V) fractal model. In this regard, at first C–V log–log plots are generated based on the results of OK and WNN. The plots then are used to determine the Cu threshold values of the alteration zones. To investigate the correlation between geological model and C-V fractal results, the log ratio matrix is applied. The results showed that, Cu values less than 1.1% from WNN have more overlapped voxels with phyllic alteration zone by overall accuracy (OA) of 0.74. Spatial correlation between the potassic alteration zones resulted from 3D geological modeling and high concentration zones in C-V fractal model showed that the alteration zone has Cu values between 1.1% and 2.2% with OA of 0.72 and finally have an appropriate overlap with Cu values greater than 2.2% with OA of 0.7. Generally, the results showed that the WNN (Morlet activation function) with OA greater than OK can be can be a suitable and robust tool for quantitative modeling of alteration zones, instead of qualitative methods.

Application of an ordinary kriging–artificial neural network for elemental distribution in Kahang porphyry deposit, Central Iran

Estimation of elemental distribution with minimum estimation errors is crucial in mineral exploration. This study aims to estimate Cu, Mo, Ag, and Au distribution in a local scale. To do this, the application of an ordinary kriging–artificial neural network (OK-ANN) model is proposed for elemental distribution based on lithogeochemical data in Kahang porphyry deposit, Central Iran. In addition, ordinary kriging (OK) method is applied and the result will be compared with the suggested method. Following this, the proposed estimation model has less error in comparison with the OK. To ensure the validity of proposed OK-ANN, the results were correlated with lithological, faults, and alteration maps in the studied region using a logratio matrix. As a result, main parts of Ag and Au enrichment are associated with faults and argillic alteration with high values of overall accuracies (OAs) in the western part of the deposit. Furthermore, Cu values estimated by the OK-ANN are connected with potassic alteration zone in the central part of deposit. In addition, the Cu-, Mo-, Au-, and Ag-enriched anomalous parts are associated with the existing faults, major alterations, and Eocene sub-volcanic rocks as well as porphyritic quartz diorites, monzodiorite–monzogranite, and dacitic units. Correlation coefficient between raw data and the results obtained by the proposed estimator is more than 84%, meaning that the proposed technique can be utilized for estimation of geochemical data in porphyry deposits.

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.

Delineation of alteration zones based on artificial neural networks and concentration-volume fractal methods in the hypogene zone of porphyry copper-gold deposit, Masjed-Daghi, East Azerbaijan Province, Iran

In this paper, we aim to achieve two specific objectives. The first one is to examine the applicability of the Artificial Neural Networks (ANNs) technique in ore grade estimation. Different training algorithms and numbers of hidden neurons are applied to estimate Cu grade of borehole data in the hypogene zone of porphyry copper-gold deposit, Masjed-Daghi, East Azerbaijan Province (Iran). The efficacy of ANNs in function-learning and estimation is compared with ordinary kriging (OK). As the kriging algorithms smooth the data, their applicability in the pre-processing of data for fractal analysis is not conducive. ANNs can be introduced as an alternative for this kind of problem. Secondly, we aim to delineate the potassic and phyllic alteration regions in the hypogene zone of Cu-Au porphyry deposit based on the estimation obtained by the ANNs and OK methods, and utilize the Concentration-Volume (C-V) fractal model. In this regard, at first, C-V log-log is generated based on the ANN results. The plots are then used to determine the Cu threshold values for the alteration zones. To investigate the correlation between the geological model and C-V fractal results, the log ratio matrix is applied. The results obtained show that Cu values less than 0.38% from ANNs have more overlapped voxels with phyllic alteration zone by an overall accuracy of 0.72. Spatial correlation between the potassic alteration zones resulting from 3D geological modeling and high concentration zones in C-V fractal model show that Cu values greater than 0.38% have more voxels overlapped with the potassic alteration zone by an overall accuracy of 0.76. Generally, the results obtained show that a combination of the ANNs and C-V fractal methods can be a suitable and robust tool for quantitative modeling of alteration zones instead of the qualitative methods.

Application of fractal models to delineate mineralized zones in the Pulang porphyry copper deposit, Yunnan, southwestern China

Abstract. The aim of this study is to delineate and identify various mineralized zones and barren host rocks based on surface and subsurface lithogeochemical data from the Pulang porphyry copper deposit, southwestern China, utilizing the number–size (N-S), concentration–volume (C-V) and power-spectrum–volume (S-V) fractal models. The N-S model reveals three mineralized zones characterized by Cu thresholds of 0.28 % and 1.45 %: <0.28 % Cu represents weakly mineralized zones and barren host rocks, 0.28 %–1.45 % Cu represents moderately mineralized zones, and > 1.45 % Cu represents highly mineralized zones. The results obtained by the C-V model depict four geochemical zones defined by Cu thresholds of 0.25 %, 1.48 % and 1.88 %, representing nonmineralized wall rocks (Cu<0.25 %), weakly mineralized zones (0.25 %–1.48 %), moderately mineralized zones (1.48 %–1.88 %) and highly mineralized zones (Cu>1.88 %). The S-V model is used by performing a 3-D fast Fourier transformation of assay data in the frequency domain. The S-V model reveals three mineralized zones characterized by Cu thresholds of 0.23 % and 1.33 %: <0.23 % Cu represents leached zones and barren host rocks, 0.23 %–1.33 % Cu represents hypogene zones, and >1.33 % Cu represents supergene enrichment zones. All the multifractal models indicate that high-grade mineralization occurs in the central and southern parts of the ore deposit. Their results are compared with the alteration and mineralogical models resulting from the 3-D geological model using a log-ratio matrix. The results show that the S-V model is best at identifying highly mineralized zones in the deposit. However, the results of the C-V model for moderately and weakly mineralized zones are also more accurate than those obtained from the N-S and S-V models.

Application of fractal modeling to delineate alteration zones and lithological units in Masjed-Daghi Cu-Au porphyry deposit, NW Iran

In this paper, we aim to present a quantitative modeling for delineating the alteration zones and lithological units in the hypogene zone of Masjed-Daghi Cu-Au porphyry deposit (NW Iran) based on the drill core data. The main goal of this work is to apply Ordinary Kriging (OK) and concentration-volume (C-V) fractal model based on Cu grades in order to separate the different alteration zones and lithological units. Initially, anisotropy was investigated and modeled based on calculating the experimental semi-variograms of the Cu values, and the main variography directions were identified and evaluated. Then a block model of the Cu grades was generated using the kriging, and the estimation obtained for OK was applied to the C-V fractal model. The C–V log–log plot based on the estimation method represents the various alteration and lithological zones via threshold values. The comparison and interpretation of the alteration zones and lithological units based on the C–V fractal modeling proved that the method was acceptable and capable of correctly delineating the alteration and lithological units. Regarding the correlation derived from log ratio matrix (used to compare the geological model with the C-V fractal results), it was observed that Cu values less than 0.4% were obtained for OK overlapped voxels with the phyllic alteration zone by an overall accuracy (OA) of 0.737. The spatial correlation between the potassic alteration zones resulting from a 3D geological modeling and the high concentration zones in the C-V fractal model based on OK indicated that the alteration zone contained Cu values greater than 0.4% with OA of 0.791. Also using this method, trustworthy results were obtained for the rock units.

Prospecting for polymetallic mineralization using step-wise weight assessment ratio analysis (SWARA) and fractal modeling in Aghkand Area, NW Iran

The processing and analysis of airborne geophysical data for polymetallic mineralization in Aghkand area at East Azerbaijan Province (NW Iran) were carried out with a view to establishing the prospective mineralized zones. This was done in two stages: (i) The magnetic and radiometric data were used to produce different maps including total magnetic intensity (TMI), reduction to the pole (RTP), magnetic lineation, intrusive, ternary, alteration, and other maps as well as the updated geological map of the area which when correlated with the geological map shows the concentration of potential areas at the western part; and (ii) the fractal model of concentration-area (C-A) was applied to find the main anomalous areas of RTP map, and the results were checked with geological units. The main anomalous areas in the RTP map were found from the western to the southwestern part of the area, consisting of andesite, tuffs, and dacite rocks. For validation purposes, the log-ratio matrix was used to correlate the main anomalous areas derived from the RTP map with mineral potential map. The prospective map of potential polymetallic mineralization was developed through integration of different exploration datasets using step-wise weight assessment ratio analysis (SWARA). The overall accuracy rate of RTP map when correlated with mineral potential map is 99%. Results are in good compliance with airborne geophysics data and indicate the presence of intrusive masses which is probably located in the western part of the study area.

Application of sequential Gaussian simulation and concentration-volume fractal model to delineate alterations in hypogene zone of miduk porphyry copper deposit, SE Iran

The paper presents a quantitative modeling for delineating alteration zones in hypogene zone of Miduk porphyry copper deposit (SE Iran) based on drill core data. The main goal of this study is applying Sequential Gaussian Simulation (SGS) and Concentration-Volume (C-V) fractal model based on Cu grades to separate different alteration zones. In this study, 10 realizations were generated using the SGS modeling and C-V log-log plots were created for them to determine Cu threshold values which delineate alteration zones. Correlation derived from log ratio matrix, which was used to compare between geological and C-V fractal results, shows that Cu values less than 0.575% from simulation number 9 (sim 9) has more overlapped voxels with phyllic alteration zone by overall accuracy (OA) of 0.75. Furthermore, the comparison revealed that sim 9 with Cu values between 0.575% and 0.933% and OA of 0.88 can determine propylitic alteration. Spatial correlation between the potassic alteration zone resulted from 3D geological modeling and high concentration zones for the C-V fractal model shows that the alteration zone has Cu values between 0.955% and 1.380% (sims 2, 3 and 4) with OA of 0.71. Finally, based on correlation between classes in the geological and the fractal models of hypogene zone, this research resulted that potassic alteration, which is the richest zone, has a strong overlap with sims 2, 3 and 4 while phyllic and propylitic alterations, with lower grades, have an appropriate overlap with sim 9.

Application of spectrum-volume fractal modeling for detection of mineralized zones

The main goal of this research work was to detect the different Cu mineralized zones in the Sungun porphyry deposit in NW Iran using the Spectrum-Volume (S-V) fractal modeling based on the sub-surface data for this deposit. This operation was carried out on an estimated Cu block model based on a Fast Fourier Transformation (FFT) using the C++ and MATLAB programing. The S-V log-log plot was generated and six Cu populations were distinguished. Based on the S-V log-log plot obtained, different mineralized zones were detected in the Sungun deposit. Copper mineralized zones in the porphyry and skarn types commenced from 0.12% and 1.3%, respectively. A supergene enrichment zone began form 0.82%; it was located in the eastern part of this deposit. The enriched skarn zones were situated in the eastern and SE parts of the Sungun deposit that overlapped the intersection of cretaceous limestones and porphyry stock. Overlapping between the resulting zones derived via the S-V fractal model and geological zones and evidences were calculated using the logratio matrix, which indicated that the S-V fractal model had proper results for detection of the mineralized zones.

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.

Correlation between Mo mineralization and faults using geostatistical and fractal modeling in porphyry deposits of Kerman Magmatic Belt, SE Iran

Kerman Magmatic Belt (KMB) is located in the southern part of Urumieh-Dokhtar Magmatic Belt (UDMB), SE Iran. There are many Cu-Mo porphyry deposits which may have correlation with existing faults. The aim of this study is determination of correlation between Mo mineralization in the porphyry deposits of the KMB and faults based on fractal and geostatistical modeling. Fault density (FD) was calculated and classified by Concentration-Area (C-A) fractal model. Moreover, the Mo values in the porphyry deposits were classified by Concentration-Number (C-N) fractal model. However, the variograms and anisotropic ellipsoids for Mo, Cu and FD show that there is a proper relationship between Mo mineralization and FD trends. Consequently, the correlation between Mo mineralization and faults was calculated by logratio matrix. Based on the results derived via the fractal and geostatistical modeling, the Mo mineralization in Cu-Mo porphyry deposit have good correlation with faults in the KMB especially in the central part of the region including Sarcheshmeh mine and Nowchun deposit. Areas with extremely FD may be prospects for exploration of new porphyry deposits.

Application of fractal-wavelet analysis for separation of geochemical anomalies

The purpose of this paper is separation and detection of different geochemical populations and anomalies from background utilizing fractal-wavelet analysis. Daubechies2 and Morlet wavelets were used for transformation of the Cu estimated data to spatial frequency based on lithogeochemical data in Bardaskan area (SE Iran) by a MATLAB code. Wavelet is a significant tool for transformation of exploratory data because the noise data are removed from results and also, accuracy for determination of thresholds can be higher than other conventional methods. The Cu threshold values for extremely, highly and moderately anomalies are 1.4%, 0.66% and 0.4%, respectively, according to the fractal-wavelet analysis based on the Daubichies2 transformation. Moreover, the fractal-wavelet analysis by the Morlet wavelet shows that the Cu threshold values are 2%, 0.75% and 0.46% for extremely, highly and moderately anomalies and populations, respectively. The results obtained by the both WT methods indicate that the main Cu enriched anomalies and populations were situated in the central parts of the Bardaskan district which are associated with surface mineralization and ancient mining digs. Furthermore, results derived via the Morlet WT is better than Daubichies2 WT according to the correlation with geological characteristics by logratio matrix. The results obtained by the fractal-wavelet method have a good correlation with geological particulars including alteration zones and surface Cu mineralization which reveals the proposed technique is an applicable approach for identification of various geochemical anomalies and zones from background. However, the main targets for detailed exploration is located in the central part of the studied area.

Identification of mineralized zones in the Zardu area, Kushk SEDEX deposit (Central Iran), based on geological and multifractal modeling

AbstractThe aim of this paper is to delineate the different lead–zinc mineralized zones in the Zardu area of the Kushk zinc–lead stratabound SEDEX deposit, Central Iran, through concentration–volume (C–V) modeling of geological and lithogeochemical drillcore data. The geological model demonstrated that the massive sulfide and pyrite+dolomite ore types as main rock types hosting mineralization. The C–V fractal modeling used lead, zinc and iron geochemical data to outline four types of mineralized zones, which were then compared to the mineralized rock types identified in the geological model. ‘Enriched’ mineralized zones contain lead and zinc values higher than 6.93% and 19.95%, respectively, with iron values lower than 12.02%. Areas where lead and zinc values were higher than 1.58% and 5.88%, respectively, and iron grades lower than 22% are labelled “high-grade” mineralized zones, and these zones are linked to massive sulfide and pyrite+dolomite lithologies of the geological model. Weakly mineralized zones, labelled ‘low-grade’ in the C– V model have 0–0.63% lead, 0–3.16% zinc and > 30.19% iron, and are correlated to those lithological units labeled as gangue in the geological model, including shales and dolomites, pyritized dolomites. Finally, a log-ratio matrix was employed to validate the results obtained and check correlations between the geological and fractal modeling. Using this method, a high overall accuracy (OA) was confirmed for the correlation between the enriched and high-grade mineralized zones and two lithological units — the massive sulfide and pyrite+dolomite ore types.

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.

Application of Concentration-Number and Concentration-Volume Fractal Models to Recognize Mineralized Zones in North Anomaly Iron Ore Deposit, Central Iran / Zastosowanie Modeli Fraktalnych Typu K-L (Koncentracja-Liczba), Oraz K-O (Koncentracja Objętość) Do Rozpoznawania Stref Występowania Surowców Mineralnych W Regionie Złóż Rud Żelaza North Anomaly, W Środkowym Iranie

Abstract Identification of various mineralized zones in an ore deposit is essential for mine planning and design. This study aims to distinguish the different mineralized zones and the wall rock in the Central block of North Anomaly iron ore deposit situated in Bafq (Central Iran) utilizing the concentration-number (C-N) and concentration-volume (C-V) fractal models. The C-N model indicates four mineralized zones described by Fe thresholds of 8%, 21%, and 50%, with zones <8% and >50% Fe representing wall rocks and highly mineralized zone, respectively. The C-V model reveals geochemical zones defined by Fe thresholds of 12%, 21%, 43% and 57%, with zones <12% Fe demonstrating wall rocks. Both the C-N and C-V models show that highly mineralized zones are situated in the central and western parts of the ore deposit. The results of validation of the fractal models with the geological model show that the C-N fractal model of highly mineralized zones is better than the C-V fractal model of highly mineralized zones based on logratio matrix.

Classification of mineralized veins using concentration volume (C–V) fractal modeling: a case study from Chah-Mesi Cu–Au vein deposit, SE Iran

The aim of this study is to classify the different Cu–Au mineralized zones based on subsurface lithogeochemical data applying concentration–volume (C–V) model in the Chah-Mesi gold–copper vein deposit, SE Iran. C–V modeling identified four geochemical populations (barren host rocks, low, medium, and high mineralized zones) for Au and Cu based on log–log plot, which was correlated with the mineralized veins from geological data. High-mineralized veins are higher than 2.24 % in Cu grade, which contain a minor volume of Cu mineralization. Main mineralized population of Cu ranged from 0.16 to 2.24 % Cu and main mineralized population of Au ranged from 0.08 to 3.54 ppm Au. High-mineralized zone of Au has small extension and are considered to range higher than 3.54 ppm. The results were compared with a geological model, showing a strong positive correlation between mineralized zones and quartz veins and andesite–basalt rocks. To certify this, a log-ratio matrix was employed to validate the C–V fractal model for Cu and Au and two lithological units namely andesite––basalt rocks and quartz indicating high values of overall accuracies.