جستجوی مقالات مرتبط با کلیدواژه « zafarghand » در نشریات گروه « زمین شناسی »

The purpose of this study is to highlight the alterations in the region using methods such as; False Color Composite (FCC), Band Ratio (BR), Least Square Fitting (LS-Fit), Matched Filtering (MF), Principal Component Analysis (PCA) and Spectral Angle Mapper (SAM) are also used to identify pure pixels. The region uses the PPI method, which takes advantage of the result of the Minimum Noise Fraction (MNF) method (7 methods in total). To validate the results, the alteration map of the region has been employed, based on which the results have a very high correspondence with the phyllic and propylitic variations in the study area. The processing methods applied to the area have highlighted the phyllic, argillic and propylitic alteration, but due to the limited potassic alteration in the Zafarghand area, the correct identification of this alteration in the area is not guaranteed. In terms of identifying and highlighting the alterations in the study area, SAM, PPI, BR, and PCA methods have been very effective, each of which helps to analyze spectral data or images and identify differences and different patterns in the study areas. In fact, with a more detailed look, it could be admitted that in highlighting the alterations in the Zafarghand region with the help of the aforementioned 7 processing methods, argillic changes with 71.43% accuracy (5 methods), phyllic changes with 85.71% accuracy (6 methods) and propylitic changes with 71.43% accuracy (5 methods) have been identified and approved.

In order to reduce errors and save money and energy, this research has dealt anomaly separation. The importance of detecting anomaly values from the background is not hidden from anyone. For this purpose, several methods have been invented, among which we can mention Mahalanobis distance separation method, which is an effective and multi-variable method for separating anomalous values from the background. In the present study, the performance of the combination of the above separation method with three data mining methods; K-Nearest Neighbor, Simple Bayes Classifier and Convolutional Neural Network is investigated. In this way, after separating the anomalous values of copper and molybdenum in the case of 177 samples obtained from the surface sampling operation in the area of Zafarghand with the Mahalanobis distance method, in order to predict these values for each random sample, The above three data mining methods were used. The results show that K-Nearest Neighbor method is much stronger, because in the network designed by this method, no sample has been wrongly identified, which shows the high accuracy of the designed network. It should be noted that the number of wrongly predicted samples for convolutional neural network and Bayes methods are reported as 2 and 3, respectively. Considering the far more acceptable error rate for the network designed by the combination of K-Nearest Neighbor method and Mahalanobis intervals, the said combination has been introduced to the decision makers of this industry as a reliable and useful method to reach the most accurate predictions.

Today, remote sensing in geology efficiently identifies alteration zones and suitable locations for hydrothermal deposits. In the present study, satellite image processing techniques are employed to highlight the alteration zones in the Zafarghand exploration area. Zafarghand, located in the northeast of Isfahan, falls within the Central Iran zone according to geological structural zoning (the middle part of the Urumieh-Dokhtar magmatic arc). The Zafarghand porphyry system includes phyllic, potassic, propylitic, argillic, and silicic alteration halos. In this study, ASTER sensor images are used to identify the associated alterations. Due to the raster and digital nature of satellite images, the digital number (DN) values of each pixel in the image matrix are considered as samples in a systematic grid. Ultimately, the singularity method algorithm, as a structural approach for effectively separating geochemical anomalies, is applied to the DN values of ASTER satellite images. Additionally, the fuzzy gamma operator was used to integrate available information layers from the study area, including the lithological layer, fault density, false color composite with ASTER bands 468 (RGB) based on new DN values (calculated α values), and the Cu geochemistry layer. The results from this technique demonstrate that the singularity method, due to its structural nature, is successful in decision-making and effectively identifies alteration zones in the Zafarghand area, particularly propylitic and phyllic alterations. Furthermore, it was observed that the fuzzy gamma operator, using the proposed technique in this study, successfully highlights prospective Cu mineralization areas in the Zafarghand exploration area.

IntroductionUrumieh-Dokhtar Magmatic Arc (UDMA) is a good prospective area for Cu, Cu-Mo and Cu-Au deposits (Fig. 1A and B). The Zafarghand district is located in the central part of the UDMA and the northeastern Isfahan. The present study concerns geological observations, alteration investigations, geochemical data and fluid inclusion studies. The purpose of the research is to identify geochemical anomalies and source of metals in this area. Geochemical anomalies for mineralizing elements and element associations were identified by using statistical analysis methods. Additionally, these results together suggest a site for exploration drilling in this study area.
Materials and methodsWe collected 186 samples (rock) along multi-cross sections oriented perpendicular to the strike of the South -Ardestan fault (Fig. 2).Trace element concentrations were determined by the ICP-MS technique in Amdel laboratory (Australia). Thin sections and doubly polished sections (100–200 µm thick) from quartz veins were prepared from samples collected from the Zafarghand district in the University of Isfahan. Heating and freezing experiments on fluid inclusions were performed as defined (by Goldstein and Reynolds (1994) on a Linkam THM600 stage.
ResultsIgneous rocks in the Zafarghand area are dominated by the Eocene and post Eocene acidic-intermediate rocks that include dacite, rhyodacite and andesite associated with diorite, quartz diorite and microdiorite intrusions. The present investigations indicate that all rocks of the Zafarghand district exhibit a variety of alterations. Hydrothermal alterations include phyllic, potassic, silicification, and argillic with widespread propylitic. The mineralization consists of malachite, azurite, hematite, and goethite, rare amounts of magnetite, pyrite, and chalcopyrite. Numerical traditional statistical analysis techniques have been applied to interpret the geochemical data of the study area. These methods are aimed at producing maps resulting from the detecting of anomaly or threshold values from the background (Aitchison, 1986; Sun et al., 2009). Anomalies of Cu, Mo, Au, Ag, Pb, Zn and Sb were determined by Mean 2 standard deviation (Cheng, 2007; Zuo et al., 2009; Chen et al., 2016). Geochemical maps for these elements in rocks and soils (Fig. 4) show significant contrasts in haloes concentrations within the diorite and dacite rocks in the southeast of the study area. The addition of concentrations in rocks is suggested indicating that a district-scale geochemical present is confined to either base metals or precious metals. The obtained fluid inclusion results are compiled in Table 3. Primary fluid inclusions in quartz mostly consist of two-phases and rarely three phases. Homogenization temperatures (Th) in quartz samples represent wide variations from 123° to 550°C. They were classified according to the mode of homogenization into two immiscible types (Fig. 8): These are early inclusions stage with a high Th (between 328° and 550°C) and late stage inclusions with a low Th (between 123° and 390°C). The salinity measured using the equation of Bodnar (1993) for fluid inclusions varies from 1.15 to 43 eqv.wt% NaCl. It was divided into two groups including high salinity (32 to 43 eqv.wt% NaCl) and low salinity (1.15 to 5.16 eqv.wt% NaCl).
DiscussionThe predictive results obtained by field observations, geochemical and micro thermometric studies are in good agreement with the known deposits. Geochemical anomalies are associated with phyllic and rare silicified altered rocks. The host rocks of anomalies are mainly dacite and diorite, respectively with an Eocene and younger age. District-scale geochemical patterns of several elements (Cu, Mo, Au, Pb, Ag, As, and Sb) in the surface coincide with the southeastern area and can be used to explore for epithermal and/or porphyry-type deposits. Anomalies of Cu and Mo are suitable for targeting Cu-Mo mineralization. Weak anomalies associated with Au concentration should also be combined with other exploration methods to identify mineralization in the Zafarghand district.
Quartz veins are classified as V1 and V2 (Fig. 3G). Based on the properties of quartz hydrothermal fluids in the Zafarghand district, they are interpreted to have evolved in two-types of fluid fields (Fig. 7):- Stage 1 fluid inclusion: This inclusion included 32 to 43 wt percent NaCl eqv (high salinity) and homogenizing between 328° and 550°C. Primary quartz in stage 1 veins (V1) is poor inclusion and associated with sulfide minerals. It can be represented by fluids trapped during aporphyry- system episode.
- Stage 2 fluid inclusion. This inclusion typically contains

Zafarghand granitoid pluton with approximately 80 Km2 in the extent with early to middle Miocene age cropped out in 35 Km of SE Ardestan. The pluton composed mainly of gabbro to tonalite and intruded in the Eocene volcanic and volcanosedimentary rocks of Uroumieh-Dokhtar structural zone. For the first time، variations of anisotropy of magnetic susceptibility in Zafarghand granitoid pluton have been investigated by using anisotropy of magnetic susceptibility (AMS) method. Based on obtained data، the measured mean magnetic susceptibility values (Km in µSI) of the different rock groups of Zafarghand pluton are as follows: gabbros (32536)، diorites (23768)، granodiorites (18436)، granites (8068) and tonalites (68). Therefore، the Km values decrease from grabbros and diorites to granodiorites، granites and tonalites. Gabbros and tonalites have the highest and the lowest Km values، respectively. The measured Km values indicate that، the studied granitoid belongs to ferromagnetic type (according to magnetite series and I-type granitoids).