| چکیده انگلیسی مقاله |
Introduction 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. Materials and Methods 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. Results and Discussion 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. Conclusion 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. |