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

Modeling mineral potential based on the precise collection and processing of geological, geophysical, and satellite data enables us to predict the potential presence of mineral substances in a specific area. This process involves constructing complex mathematical models that, utilizing machine learning algorithms and thorough data analysis, assist authorities and decision-makers in the mining sector. It helps identify mineral-rich zones for optimal extraction and manage land resources in the best possible way. Given the diverse geological units present in the Saqez sheet, this sheet is considered one of the most promising areas for the formation of metallic deposits. The host rock for most Pb-Zn deposits in Iran is sedimentary, known as Sedimentary-Hosted Pb-Zn deposits. According to conducted surveys, it has been determined that the Pb-Zn mineralization occurring in the Saqez sheet is also of this type. Typically, calcite, dolomite, shale, sandstone, and igneous rocks serve as hosts for these deposits.

In this research, the exploratory layers of lithology, dolomite alteration, and geochemical of Pb-Zn were used to prepare a map of the prediction of Pb-Zn mineralization in the turpentine sheet. Utilizing the singularity technique on sediments stream, using various exploratory layers performing laboratory spectroscopy, and applying the spectral behavior curves obtained on Sentinel-2A images in this innovative and creative research. It shows its existence compared to another similar research. After fuzzification of exploration layers in GIS software, the prediction map of Pb-Zn mineralization was obtained by Fuzzy-Gamma function and gamma value of 0.85. The results of XRF and ICP-MS analysis on the discovered Pb-Zn samples showed a grade between 3 and 7%, which indicates the correct selection of the studied area and the exploratory layers and their correct integration. Further, by conducting laboratory spectroscopy in a dark room with a halogen lamp and obtaining the spectral behavior curve of the sphalerite mineral related to the samples of the study area, the SAM matching algorithm was applied to the Sentinel-2A satellite images.

According to the Pb-Zn mineralization prediction map of the Saqez sheet, this map is classified into four categories of low, moderate, high, and extreme mineralization potential. It is evident from this map that the northern, central, and southeastern regions have the highest potential for Pb-Zn mineralization. Upon examining the topography and road identification of the Saqez sheet, six areas were selected for exploratory drilling of Pb-Zn. Samples were collected for analysis and validation of identified points. XRF and ICP-MS analysis results indicated that the total Pb-Zn content ranged from 2 to 7% and 70,000 to 20,000 ppm. Finally, high-grade Pb-Zn samples were selected for petrographic examination. Petrographic studies revealed that minerals such as sphalerite, galena, and pyrite were predominant in the collected samples, with their texture filling the pore spaces. Specifically, sphalerite replaced galena, and galena replaced pyrite.The results obtained from laboratory spectral analysis and the application of spectral behavior curves for sphalerite minerals on Sentinel-2A satellite images were utilized to assess the accuracy of the work and identify promising new mineralized areas. By comparing the corrected spectra from the laboratory experiments with the USGS spectral library, it was determined that the obtained spectral behavior is similar to the USGS spectral library. Therefore, the predictive map of Pb-Zn mineralization resulting from the application of spectral behavior curves for sphalerite on Sentinel-2A satellite images indicates the correct selection of imagery, appropriate spectral analysis, and all processing steps.

Due to the fact that the host rocks of most Pb-Zn deposits in Iran are of sedimentary origin, the first step in modeling the mineral potential of these deposits is to accurately recognize the ore deposit type. Based on the evidence and samples observed in the study area, the ore deposit type in the study area can be considered as the Irish type. Therefore, based on this, modeling and prediction of Pb-Zn mineralization on the one hundred thousand scale map of Saqez were carried out according to the Irish type Pb-Zn mineralization.After evaluating the Pb-Zn mineral potential map, seven final areas were selected for exploration. Based on the exploration conducted in six areas (S1, S2, P, Q-Pb-Zn, Polygon12, and Polygon13), samples containing Pb-Zn mineralization were discovered, while Polygon5 was identified as lacking Pb-Zn mineralization, indicating the reliability of the exploration layers and integration method. XRF and Aqua Regia analysis results showed that the discovered Pb-Zn samples had grades ranging from two to seven percent, indicating economic-grade content for these metals.

The Mayamey area (northeast of Shahrud), which forms a part of the Joghatay-Sabzevar zone, hosted Padiform chromite deposits with Harzburgite (ophiolitic) host rock, in which serpentine alteration is strongly spread. In this study, in order to identify the promising areas of chromite mineralization, host rock evidence maps (lithological), serpentine alteration resulting from remote sensing data processing, fault density, geophysics-magnetism, and geochemical of Cr have been used. Then, using the Fuzzy-Gamma function and the gamma value of 0.85, the evidence maps were weighted and fuzzified between 0 and 1 and integrated with each other in the GIS environment. According to the final map of chromite potential, the access roads and geological conditions of 11 areas were determined as exploration targets. Based on the searches, it was found that out of the 11 designated areas, there were three active mines, six promising areas, and two areas without chromite mineralization. The results of XRF analysis of two control samples S4 and S5 showed that the grade of chromite in these areas is 40.02% and 52.44%, respectively, which shows the economic grade of chromite, high accuracy, and the correct method of combining the selected observer maps in this is study. Also, the geochemical study showed that the amounts of MgO, Al2O3, TiO2, Fe2O3, and Cr2O3 are consistent with the accumulation type chromites and the chromites of the study area are rich in Cr and to some extent Fe and poor in Al.

Malayer-Aligoudarz-Isfahan metallogenic belt is located in the Sanandaj-Sirjan Zone, which hosts a large number of metal deposits. In this study, using Fuzzy-GAMMA method and analytic hierarchy process (AHP) based on weighting to exploratory layers were used Cu metal modeling in the studied area. First, all exploration layers and substrates, including lithology, geochemistry and fault density, they were weighted by Fuzzy-GAMMA method and were in the range of 0 to 1. Then fuzzy layers are then integration according to the weights assigned with 0.9 gamma (γ) value and Potential maps of Cu metal in the study area were obtained by Fuzzy-GAMMA method. Subsequently, using analytic hierarchical process Method (AHP), and forming appropriate matrix for the exploration layers, the layers were weighted and the potential map of Cu metal was obtained. Finally, the location of the Cu deposits and indices in the region was used to compare the accuracy of the applied methods. The results showed that (AHP) method compared with Fuzzy-GAMMA method determine more anomaly and the obtained anomalies by this method more consistent with existing Cu deposits in the region.

1-Introduction Separation of geochemical anomalies from background has always been a significant concern of exploration geochemistry. The search for methods that can make this analysis quantitative and objective aims not only at the reduction of subjectiveness but also at providing an automatic routine in exploration, assisting the interpretation and production of geochemical maps (Nazarpour et al., 2016). Arak 1:100000 geological sheet is located in the north of Malayer-Aligoudarz-Efsahan Pb-Zn metalogenic belt, so this sheet has been studied. In this study, we compared the methods of classical statistics (Mean+2SDEV), exploratory data analysis (MAD), concentration-number (C-N) and concentration-area (C-A) fractal models. Also, singularity index models were used to separate the Pb and Zn anomalies in Arak 1:100000 geochemical sheet. The results of mentioned methods, showed that the singularity index method has a higher accuracy. Also, indicates the higher concentration of Zn in area of study. 2-Methodology 2-1 Classical statistics Various statistical methods have been used to process geochemical data in order to determine threshold values. Statistical quantities, such as the mean, standard deviation (SDEV) and percentiles, have been used to define threshold for separating anomalies form background. For example, geochemical anomalies have been defined as values higher than a threshold defined as the 75th or 85th percentile, and Mean+SDEV or Mean+2SDEV (Nazarpour et al., 2015). 2-2 EDA (Exploratory Data Analysis) In exploratory data analysis (here after named EDA) of geochemical exploration data, the median+2MAD value was initially used to identify extreme values and act as threshold for further inspection of large data sets (Carranza, 2009). The EDA was first established by Tukey (1977), was developed further by, and then was used by many researchers in modeling of geochemical anomalies (Carranza, 2009). The MAD is the median of absolute deviations of individual dataset values (Xi) from the median of all dataset values (Tukey, 1976): (1) MAD = median 14 [│Xi-median Xi│]"> 2-3 Multifractal Fractal and multifractal models have also been applied to separate anomalies from background values. These methods are gradually being adopted as an effective and efficient means to analyze spatial structures in metallic geochemical systems (Afzal et al., 2017). The concentration-number (C-N), concentration-area (C-A) multi-fractal methods has been used for delineation and description of relations among mineralogical, geochemical and geological features based on surface and subsurface data (Nazarpour et al., 2015). Fractal/multi-fractal models consist of the frequency distribution and the spatial self-similar or self-affine characteristics of geochemical variables and have been demonstrated to be useful tools for decomposing geological complexes and mixed geochemical populations and to recognize weak geochemical anomalies hidden within strong geochemical background (Cheng et al., 1994). 2-4 Singularity index The singularity technique is another vital progress for fractal/multifractal modeling of geochemical data (Zuo et al., 2012). It is defined as the characterization of the anomalous behaviors of singular physical processes that often result in anomalous amounts of energy release or material accumulation within a narrow spatial–temporal interval. The singularity can be estimated from observed element concentration within small neighborhoods based on the following equation (Cheng, 2007): (2) 14X=c·εa-E"> The singularity index is a powerful tool to identify weak anomalies, but it is influenced by the selection of the window size. When applying this method, one should use different window sizes to process the geochemical data and find an appropriate window size which can highlight the interesting results (Zuo et al., 2012). 3- Results and discussion Threshold values obtained using mentioned methods were used to map the spatial distribution of element concentrations. These interpolated maps were produced by means of inverse distance weighted (IDW) method (Nazarpour et al., 2016). In classical statistics and MAD methods, anomalies are usually detected, regardless of the location of each instance, and only by formulating relationships (Hashemi marand et al., 2018). In these methods, it is possible that some of the proposed ranges are false anomalies (Tukey, 1977). The geochemical anomalies of the Pb and Zn elements were separated using fractal methods of concentration-number (C-N), concentration-area (C-A), and according to the fitting line of each element on the logarithmic graphs. The singularity index estimated through a small window mainly reflects the fluctuation of the element concentration (Afzal et al., 2017). The singularity index estimated through a large window mainly reflects regional changes but it does not focus on the local weak anomalies (Zuo et al., 2012). There is probably a significant effect of the contact between exposed bedrock and covered areas, or there could be other deterministic trends as well, which should be studied further (Cheng, 2007). The results of the named methods are shown in Fig. 1. 4-Conclusions Singularity index analysis, indicated that the hidden anomalies are better coincidence with indices and mineral deposit occurrence in the study area. In general, the comparison between these methods indicate that the concentration of Pb and Zn increased toward the and southwest and south-northeast parts, respectively. In these regions there is high potential for the occurrence of promising mining areas. Moreover, the obtained Pb and Zn anomalies have a reasonable correlation with the exposure of limestone in the study area, which is a suitable host rock for the formation of MVT type Pb and Zn deposits. a b c d e Figure 1. Maps of spatial distributions of Pb, Zn elements. a) Classic statistics, b) MAD, c) Multifractal (C-N), d) Multifractal (C-A), e) Singularity index References Afzal, P., Ahmadi, K., Rahbar, K., 2017. Application of fractal-wavelet analysis for separation of geochemical anomalies. 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