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Journal articles on the topic 'Mineral Mapping'
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Contreras Acosta, Isabel Cecilia, Mahdi Khodadadzadeh, and Richard Gloaguen. "Resolution Enhancement for Drill-Core Hyperspectral Mineral Mapping." Remote Sensing 13, no. 12 (2021): 2296. http://dx.doi.org/10.3390/rs13122296.
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Drill-core samples are a key component in mineral exploration campaigns, and their rapid and objective analysis is becoming increasingly important. Hyperspectral imaging of drill-cores is a non-destructive technique that allows for non-invasive and fast mapping of mineral phases and alteration patterns. The use of adapted machine learning techniques such as supervised learning algorithms allows for a robust and accurate analysis of drill-core hyperspectral data. One of the remaining challenge is the spatial sampling of hyperspectral sensors in operational conditions, which does not allow us to render the textural and mineral diversity that is required to map minerals with low abundances and fine structures such as veins and faults. In this work, we propose a methodology in which we implement a resolution enhancement technique, a coupled non-negative matrix factorization, using hyperspectral, RGB images and high-resolution mineralogical data to produce mineral maps at higher spatial resolutions and to improve the mapping of minerals. The results demonstrate that the enhanced maps not only provide better details in the alteration patterns such as veins but also allow for mapping minerals that were previously hidden in the hyperspectral data due to its low spatial sampling.
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Pan, Ziwu, Junjie Liu, Liqun Ma, et al. "Research on Hyperspectral Identification of Altered Minerals in Yemaquan West Gold Field, Xinjiang." Sustainability 11, no. 2 (2019): 428. http://dx.doi.org/10.3390/su11020428.
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Predictions of prospectivity based on remote sensing were developed using alteration mineral indicative hyperspectral mapping and remote sensing anomaly filtering, combined with geological characteristics and anomalous mineral field verification. Based on the results of the hyperspectral mineral mapping and the actual geological ground conditions, the results of mapping of altered minerals, such as chlorite, muscovite, kaolinite, and iron oxide were validated, and gold, silver, copper, nickel, and other geochemical anomaly areas were identified for verification work. The results of hyperspectral mineral extraction show that the mineral assemblage closely related to gold deposits in shear zones is muscovite + chlorite + epidote + kaolinite. This alteration mineral assemblage can be used as regional search criteria for shear zone gold mineralisation and was the basis for the discovery of mineralised hydrothermal alteration centres and delineation of four prospective targets. Established on a spectral prospectivity model of the study area, prospective ore-bearing areas have been delineated, which indicate the direction for further geological and mineral resource surveys.
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Zhang, Chuan, Fa Wang Ye, Dong Hui Zhang, Ning Bo Zhao, and Ding Wu. "Method Study of Mineral Weight Information Extraction Based on Hyperion Hyperspectral Remote Sensing Data - The Region of Gannan as an Example." Advanced Materials Research 718-720 (July 2013): 2237–41. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.2237.
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In this paper, the methods of extracting minerals weight information are studied based on hyperion hyperspectral remote sensing data, taking the region of Gannan area in Jiangxi as an example. After studying spectral angle mapping and matched filtering, the method has been developed which combines them to extract the weight information of minerals. The results show that this method can successfully apply and made spectral angle mapping integrate with matched filtering, combined their advantages and made up their shortcomings, and extract weight information of clay minerals accurately from the background image. Meanwhile, the location of all kinds of mineral and results of mineral mapping are consistent very well, reflecting the application feasibility of the method.
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Fakhrurrozi, Afnindar, Izzul Qudsi, Mochamad Rifat Noor, and Anggun Mayang Sari. "Mineral Mapping on Hyperspectral Imageries Using Cohesion-based Self Merging Algorithm." Jurnal Elektronika dan Telekomunikasi 22, no. 2 (2022): 78. http://dx.doi.org/10.55981/jet.507.
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Recently, hybrid clustering algorithms gained much research attention due to better clustering results and are computationally efficient. Hyperspectral image classification studies should be no exception, including mineral mapping. This study aims to tackle the biggest challenge of mapping the mineralogy of drill core samples, which consumes a lot of time. In this paper, we present the investigation using a hybrid clustering algorithm, cohesion-based self-merging (CSM), for mineral mapping to determine the number and location of minerals that formed the rock. The CSM clustering performance was then compared to its classical counterpart, K-means plus-plus (K-means++). We conducted experiments using hyperspectral images from multiple rock samples to understand how well the clustering algorithm segmented minerals that exist in the rock. The samples in this study contain minerals with identical absorption features in certain locations that increase the complexity. The elbow method and silhouette analysis did not perform well in deciding the optimum cluster size due to slight variance and high dimensionality of the datasets. Thus, iterations to the various numbers of k-clusters and m-subclusters of each rock were performed to get the mineral cluster. Both algorithms were able to distinguish slight variations of absorption features of any mineral. The spectral variation within a single mineral found by our algorithm might be studied further to understand any possible unidentified group of clusters. The spatial consideration of the CSM algorithm induced several misclassified pixels. Hence, the mineral maps produced in this study are not expected to be precisely similar to ground truths.
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Cavur, Mahmut, Yu-Ting Yu, Ebubekir Demir, and Sebnem Duzgun. "Mapping Geothermal Indicator Minerals Using Fusion of Target Detection Algorithms." Remote Sensing 16, no. 7 (2024): 1223. http://dx.doi.org/10.3390/rs16071223.
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Mineral mapping from satellite images provides valuable insights into subsurface mineral alteration for geothermal exploration. In previous studies, eight fundamental algorithms were used for mineral mapping utilizing USGS spectra, a collection of reflectance spectra containing samples of minerals, rocks, and soils created by the USGS. We used an ASD FieldSpec 4 Hi-RES NG portable spectrometer to collect spectra for analyzing ASTER images of the Coso Geothermal Field. Then, we established the ground-truth information and the spectral library by analyzing 97 samples. Samples collected from the field were analyzed using the CSIRO TSG (The Spectral Geologist of the Commonwealth Scientific and Industrial Research Organization). Based on the mineralogy study, multiple high-purity spectra of geothermal alteration minerals were selected from collected data, including alunite, chalcedony, hematite, kaolinite, and opal. Eight mineral spectral target detection algorithms were applied to the preprocessed satellite data with a proposed local spectral library. We measured the highest overall accuracy of 87% for alunite, 95% for opal, 83% for chalcedony, 60% for hematite, and 96% for kaolinite out of these eight algorithms. Three, four, five, and eight algorithms were fused to extract mineral alteration with the obtained target detection results. The results prove that the fusion of algorithms gives better results than using individual ones. In conclusion, this paper discusses the significance of evaluating different mapping algorithms. It proposes a robust fusion approach to extract mineral maps as an indicator for geothermal exploration.
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MacRae, Colin, Nick Wilson, and Mark Pownceby. "Electron Microprobe Mapping as a Tool in Ilmenite Characterisation." Microscopy and Microanalysis 7, S2 (2001): 710–11. http://dx.doi.org/10.1017/s1431927600029627.
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The demand for accurate mineralogical data is increasing rapidly as exploration methods, prospect evaluation procedures and metallurgical optimisation studies become more sophisticated. in response to these needs, semi-automated and automated image processing systems which detect minerals using optical microscopy, scanning electron microscopy or electron microprobe microanalysis (EPMA) are becoming increasingly important tools in the exploration, mining and mineral processing industries. CSIRO Minerals has developed an EPMA based imaging (or mapping) method for characterising ilmenite concentrates. The method uses a JEOL 8900R EPMA to collect elemental x-ray maps which are then processed using in-house developed software, Chimage. The mapping procedure differs from traditional automated identification systems in that no detailed a priori knowledge of the mineral phases is required. in addition, Chimage software enables complete processing and interpretation of the data set off-line. Elemental data can be displayed in either scatter or ternary diagrams showing clusters which allow mineral phases to be identified.
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Khan, Ihtisham, Kashif Khan, Muhammad Fahad Bial, and Raja Muhammad Usama. "Geospatial Mapping and Detection of Ferrous, Iron Oxides, and Clay Minerals in District Mohmand, Pakistan." International Journal of Economic and Environmental Geology 14, no. 04 (2024): 40–45. http://dx.doi.org/10.46660/ijeeg.v14i04.224.
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The study focuses on the utilization of remote sensing techniques to effectively identify and detect ferrous,iron oxides, and clay minerals in Mohmand district, Pakistan, using LANDSAT 8 multispectral images. This studyfocuses on the increasing demand and importance of these minerals in mining and commercial activities. Mineralexploration in Mohmand district, utilizing remote sensing technology to identify mineral compositions using the bandratio is done. This technique allows for a more focused and precise approach to exploration and extraction techniques.Cloud-free LANDSAT 8 images with minimal vegetation cover were utilized for the analysis. The band ratio approachwas utilized to identify areas exhibiting diverse mineral compositions. The study highlights the effectiveness of thesuggested methodology in mapping and detecting ferrous, iron oxides, and clay minerals, indicating the significantpotential of remote sensing for mineral exploration. The results highlight the importance of developing distributionmaps to support more efficient methods for mining and mineral exploration. The study contributes to a more focusedand efficient assessment of mineral resources and extraction techniques in the Mohmand district and similar geologicalterrains, offering stakeholders a valuable tool for informed decision-making in mineral exploration and exploitationefforts.
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Rifai, Kheireddine, Marc Constantin, Adnan Yilmaz, Lütfü Ç. Özcan, François R. Doucet, and Nawfel Azami. "Quantification of Lithium and Mineralogical Mapping in Crushed Ore Samples Using Laser Induced Breakdown Spectroscopy." Minerals 12, no. 2 (2022): 253. http://dx.doi.org/10.3390/min12020253.
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This article reports on the quantification of lithium and mineralogical mapping in crushed lithium ore by laser-induced breakdown spectroscopy (LIBS) using two different calibration methods. Thirty crushed ore samples from a pegmatite lithium deposit were used in this study. Representative samples containing the abundant minerals were taken from these crushed ores and mixed with resin to make polished disks. These disks were first analyzed by TIMA (TESCAN Integrated Mineral Analyzer) and then by a LIBS ECORE analyzer to determine the minerals. Afterwards, each of the thirty crushed ore samples (<10 mm) were poured into rectangular containers and analyzed by the ECORE analyzer, then mineral mapping was produced on the scanned surfaces using the mineral library established on the polished sections. For the first method the lithium concentrations were inferred from the empirical mineral chemistry formula, whereas the second one consisted of building a conventional calibration curve with the crushed material to predict the lithium concentration in unknown crushed materials.
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Kumar, C., A. Shetty, S. Raval, P. K. Champatiray, and R. Sharma. "Sub-pixel mineral mapping using EO-1 Hyperion hyperspectral data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 455–61. http://dx.doi.org/10.5194/isprsarchives-xl-8-455-2014.
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This study describes the utility of Earth Observation (EO)-1 Hyperion data for sub-pixel mineral investigation using Mixture Tuned Target Constrained Interference Minimized Filter (MTTCIMF) algorithm in hostile mountainous terrain of Rajsamand district of Rajasthan, which hosts economic mineralization such as lead, zinc, and copper etc. The study encompasses pre-processing, data reduction, Pixel Purity Index (PPI) and endmember extraction from reflectance image of surface minerals such as illite, montmorillonite, phlogopite, dolomite and chlorite. These endmembers were then assessed with USGS mineral spectral library and lab spectra of rock samples collected from field for spectral inspection. Subsequently, MTTCIMF algorithm was implemented on processed image to obtain mineral distribution map of each detected mineral. A virtual verification method has been adopted to evaluate the classified image, which uses directly image information to evaluate the result and confirm the overall accuracy and kappa coefficient of 68 % and 0.6 respectively. The sub-pixel level mineral information with reasonable accuracy could be a valuable guide to geological and exploration community for expensive ground and/or lab experiments to discover economic deposits. Thus, the study demonstrates the feasibility of Hyperion data for sub-pixel mineral mapping using MTTCIMF algorithm with cost and time effective approach.
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Molua, Collins O., and John C. Morka. "Application of Multispectral Remote Sensing in Mapping of Mineral Deposits." Journal of Image Processing and Intelligent Remote Sensing, no. 11 (September 30, 2021): 21–33. http://dx.doi.org/10.55529/jipirs.11.21.33.
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This research examined Multispectral Remote Sensing in mineral mapping in the Ogoni area of Port Harcourt, Rivers State, Niger Delta. That is why the objective of the research was to improve the efficiency of mineral exploration with the help of non-destructive methods. Envi and ArcGIS software were used to analyze Landsat 8 OLI and Sentinel -2 MSI datasets. The applied preprocessing procedures involved radiometric and geometric corrections, and the values of these procedures ranged from 0. 006 to 0. 987 and 0. 064 to 0. 887, respectively. While mapping the minerals, we used spectral signature, band rasterizing, and principal component analysis. Here, the classification results exhibit a wide range in terms of the total percentage of accuracy, which was between 0. 097 and 0. 908. Consequently, the band ratio analysis showed the areas with high mineral potential; for example, Region 5 has ratios of 0. 972, 0. 986, and 0. 591 for three of the most important combinations of bands. Application of hyperspectral data calculated the degree of minerals present in the area; also, areas of high mineral dominance were observed and found to be Region_9 at the degree of 0. 711 concentration for Mineral_3. The results-oriented work and the study suggest that multispectral remote sensing could be a preliminary way of exploring mineral-rich environments to locate areas of interest and higher potential for ground-based exploration. Solutions include further tweaking the algorithms, including other geospatial data sources and detailed surveys in the subject areas.
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Pownceby, M. I., C. M. MacRae, and N. C. Wilson. "Mineral characterisation by EPMA mapping." Minerals Engineering 20, no. 5 (2007): 444–51. http://dx.doi.org/10.1016/j.mineng.2006.10.014.
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THOMPSON, ANNE J. B., PHOEBE L. HAUFF, and AUDREY J. ROBITAILLE. "Alteration Mapping in Exploration: Application of Short-Wave Infrared (SWIR) Spectroscopy." SEG Discovery, no. 39 (October 1, 1999): 1–27. http://dx.doi.org/10.5382/segnews.1999-39.fea.
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ABSTRACT Alteration mineral assemblages are important to the understanding of and exploration for hydrothermal ore deposits. Conventional mapping tools may not identify fine-grained minerals or define important compositional variations. Field portable short-wave infrared (SWIR) spectrometers solve some of these problems and provide a valuable tool for evaluating the distribution of alteration assemblages. Spectrometers such as the PIMA-II allow rapid identification of minerals and mineral-specific variations at a field base. Mineral assemblages, integrated with other exploration data, are then used to target drill holes and guide regional exploration programs. Data collection must be systematically organized and carried out by a trained operator. Analysis of data sets requires the use of spectral reference libraries from different geological environments and may be aided in some cases by computer data processing packages. Integration of results with field observations, petrography, and X-ray diffraction analysis is necessary for complete evaluation. The PIMA (portable infrared mineral analyzer) has been used successfully in the high-sulfidation epithermal, low-sulfidation epithermal, volcanogenic massive sulfide (VMS) and intrusion-related environments. Case studies from these systems demonstrate the ability to rapidly acquire and process SWIR data and produce drill logs and maps. The resulting information is critical for targeting.
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Kumar, H., A. K. Sharma, and A. S. Rajawat. "APPLICATIONS OF IMAGING SPECTROSCOPY FOR NON-METALLIC MINERAL EXPLORATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5 (November 27, 2018): 835–38. http://dx.doi.org/10.5194/isprs-archives-xlii-5-835-2018.
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<p><strong>Abstract.</strong> Imaging spectroscopy/hyperspectral remote sensing technique acquires images in a very narrow and contiguous spectral bands. High spectral resolution data provided by imaging spectrometers enables remote compositional mapping of earth surface. In the present study, we demonstrate the potentials of airborne AVIRIS-NG datasets for identification and mapping of non-metallic minerals. Several minerals such as carbonates, sulphates and phyllosilicate exhibit diagnostic absorption feature in Short Wave Infrared Region (SWIR) (2.0&amp;ndash;2.5<span class="thinspace"></span>&amp;mu;m). Therefore, mapping of wavelength of deepest absorption in SWIR is very useful for exploratory earth surface composition/mineral mapping. To map the mineralogical diversity in the parts of Banswara region, Rajasthan, wavelength of deepest absorption feature and absorption band depth in SWIR region was calculated at each pixel. It was found that majority of pixels showed absorption near &amp;sim;2.31, 2.33 and 2.20<span class="thinspace"></span>&amp;mu;m. Detailed analysis of spectra of image revealed dolomite as dominant mineral at pixels showing deepest absorption at 2.31<span class="thinspace"></span>&amp;mu;m. Calcite and clays were found to be present at pixels showing deepest absorption feature near 2.33 and 2.20<span class="thinspace"></span>&amp;mu;m respectively. It is noted that mapping wavelength position of deepest feature is a very fast and reliable indicator of mineralogy. The mineral map of calcite and dolomite shall be useful for locating new mining prospect in the region.</p>
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Zhang, Chuan, Min Yi, Fawang Ye, Qingjun Xu, Xinchun Li, and Qingqing Gan. "Application and Evaluation of Deep Neural Networks for Airborne Hyperspectral Remote Sensing Mineral Mapping: A Case Study of the Baiyanghe Uranium Deposit in Northwestern Xinjiang, China." Remote Sensing 14, no. 20 (2022): 5122. http://dx.doi.org/10.3390/rs14205122.
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Deep learning is a popular topic in machine learning and artificial intelligence research and has achieved remarkable results in various fields. In geological remote sensing, mineral mapping is an appealing application of hyperspectral remote sensing for geological surveyors. Whether deep learning can improve the mineral identification ability in hyperspectral remote sensing images, especially for the discrimination of spectrally similar and intimately mixed minerals, needs to be evaluated. In this study, shortwave airborne spectrographic imager (SASI) hyperspectral images of the Baiyanghe uranium deposit in Northwestern Xinjiang, China, were used as experimental data. Three deep neural network (DNN) models were designed: a fully connected neural network (FCNN), a one-dimensional convolutional neural network (1D CNN), and a one-dimensional and two-dimensional convolutional neural network (1D and 2D CNN). A sample dataset containing five minerals was constructed for model training and validation, which was divided into training, validation and test sets at a ratio of 6:2:2. The final test accuracies of the FCNN, 1D CNN, and 1D and 2D CNN were 91.24%, 93.67% and 94.77%, respectively. The three DNNs were used for mineral identification and mapping of SASI hyperspectral images of the Baiyanghe uranium mining area. The mapping results were compared with the mapping results of the support vector machine (SVM) and the mixture-tuned matched filtering (MTMF) method. Combined with the ground spectral data obtained by the spectrometer, spectral verification and interpretation were carried out on sections that the two kinds of methods identified differently. The verification results show that the mapping results of the 1D and 2D CNN were more accurate than those of the other methods. More importantly, for minerals with similar spectral characteristics, such as short-wavelength white mica and medium-wavelength white mica, the 1D and 2D CNN model had a more accurate discrimination effect than the other DNN models, indicating that the introduction of spatial information can improve the mineral identification ability in hyperspectral remote sensing images. In general, CNNs have good application prospects in geological mapping of hyperspectral remote sensing images and are worthy of further development in future work.
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Sengar, V. K., A. S. Venkatesh, P. K. Champaty Ray, S. L. Chattoraj, and R. U. Sharma. "MINERALOGICAL MAPPING IN THE PART OF A GOLD PROSPECT USING EO-1 HYPERION DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (October 14, 2016): 991–93. http://dx.doi.org/10.5194/isprs-archives-xli-b7-991-2016.
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The satellite data obtained from various airborne as well as space-borne Hyperspectral sensors, often termed as imaging spectrometers, have great potential to map the mineral abundant regions. Narrow contiguous bands with high spectral resolution of imaging spectrometers provide continuous reflectance spectra for different Earth surface materials. Detailed analysis of resultant reflectance spectra, derived through processing of hyperspectral data, helps in identification of minerals on the basis of their reflectance characteristics. EO-1 Hyperion sensor contains 196 unique channels out of 242 bands (L1R product) covering 0.4&amp;ndash;2.5&amp;thinsp;μm range has also been proved significant in the field of spaceborne mineral potential mapping. <br><br> Present study involves the processing of EO-1 Hyperion image to extract the mineral end members for a part of a gold prospect region. Mineral map has been generated using spectral angle mapper (SAM) method of image classification while spectral matching has been done using spectral analyst tool in ENVI. Resultant end members found in this study belong to the group of minerals constituting the rocks serving as host for the gold mineralisation in the study area.
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Zhang, Han. "Mineral Information Extraction Using Hyper Spectral Remote Sensing Technology." Applied Mechanics and Materials 340 (July 2013): 480–83. http://dx.doi.org/10.4028/www.scientific.net/amm.340.480.
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Based on the hyper spectral data provided by small satellites for environmental hazard monitoring and forecasting (HJ-A), we convert the data format and reflectance to remove possible interference. Spectral angle mapping method is employed afterwards for mineral mapping of the study area. The small-scaled mineral distribution map is then generated for the whole study area. Our study suggests that mineral mapping using HJ-A satellite hyper spectral data is fairly effective in terms of mapping quality and cost.
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Sayyad, S. B., Z. R. Mohammed, and R. R. Deshmukh. "MINERAL MAPPING USING CHANDRAYAAN-1 HYPERSPECTRAL (HYSI) DATA FROM MARE VAPORUM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5 (November 19, 2018): 339–44. http://dx.doi.org/10.5194/isprs-archives-xlii-5-339-2018.
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<p><strong>Abstract.</strong> The imaging spectroscopy offers an opportunity to map and discriminate different minerals on the lunar surface which further helps to understand the origin, evolution process, and the crustal composition on the surface of the moon. Compositional mapping of the lunar surface is considered as a standard approach for mineral mapping. This paper reports surface mineralogy of the lunar surface from Mare Vaporum using Chandrayaan-1 Hyperspectral remotely sensed data from HySi sensor. False color composite is created using different band shaping algorithms like band strength; band curve and band tilt parameters at crucial wavelength for spatial analysis. The Spectral analysis has been done by deriving reflectance spectra at varying locations from the area under study. The Study shows the mineral map with different categories of minerals which are high-Ca pyroxene and/or olivine and low Ca-pyroxene. However because of the limited spectral coverage of HySi, data at the longer wavelengths required to discriminate among different group of minerals.</p>
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Peyghambari, Sima, Yun Zhang, Hassan Heidarian, and Milad Sekandari. "One-Dimensional-Mixed Convolution Neural Network and Covariance Pooling Model for Mineral Mapping of Porphyry Copper Deposit Using PRISMA Hyperspectral Data." Photogrammetric Engineering & Remote Sensing 90, no. 8 (2024): 511–22. http://dx.doi.org/10.14358/pers.24-00006r2.
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Mapping distribution of alterations around porphyry copper deposits (PCDs) greatly affects mineral exploration. Diverse geological processes generate irregular alteration patterns with diverse spectral characteristics in mineral deposits. Applying remotely sensed hyperspectral images (HSIs) is an appealing technology for geologic surveyors to generate alteration maps. Conventional methods mainly use shallow spectral absorption features to discriminate minerals and cannot extract their important spectral information. Deep neural networks with nonlinear layers can evoke the deep spectral and spatial information of HSIs. Deep learning???based methods include fully connected neural networks, convolutional neural networks, and hybrid convolutional networks like mixed convolution neural network and covariance pooling (MCNN‐CP) algorithms. However, each has its advantages and limitations. To significantly avoid losing important spectral features, we proposed a new method by fusing a one‐dimensional convolutional neural network (1D‐CNN) with MCNN‐CP (1D‐MCNN‐CP), achieving an overall accuracy (97.44%) of mineral mapping from PRISMA HSIs. This research deduced that 1D‐MCNN‐CP improved performance and reduced misclassification errors among minerals sharing similar spectral features.
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Gorafi, Yasir S. A., Takayoshi Ishii, June-Sik Kim, Awad Ahmed Elawad Elbashir, and Hisashi Tsujimoto. "Genetic variation and association mapping of grain iron and zinc contents in synthetic hexaploid wheat germplasm." Plant Genetic Resources: Characterization and Utilization 16, no. 1 (2016): 9–17. http://dx.doi.org/10.1017/s1479262116000265.
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AbstractFe and Zn deficiency are widespread worldwide. As wheat is the primary food for the majority of the world people, producing wheat grains with high mineral content can ameliorate the problem of mineral hunger. However, the genetic variation available for breeders is limited. The aim of this study was to assess the genetic variation in grain Fe and Zn contents in 47 synthetic hexaploid wheats and to identify marker loci associated with Fe and Zn contents. We measured the grain Fe and Zn contents using inductively coupled plasma atomic emission spectroscopy and performed genotyping using SSR markers. The results showed considerable genetic variation for these minerals. We identified three lines with high Fe and Zn contents and six quantitative trait loci of which three were associated with Fe content and the other three with Zn content. The minerals showed positive phenotypic and genotypic correlation and high heritability (>60%). The ratio of the σ2g to the σ2g×e was ≥1 for the two mineral contents indicating that breeding for increasing mineral content within the synthetic lines is possible. The synthetic wheat lines identified in this study are valuable genetic resources, and can be utilized for breeding wheat cultivars with high mineral content.
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İşcan, Talan. "Mapping project could unearth mineral wealth." Nature 575, no. 7783 (2019): 443. http://dx.doi.org/10.1038/d41586-019-03569-2.
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Thakker, R. V. "Gene mapping of mineral metabolic disorders." Journal of Inherited Metabolic Disease 12, no. 3 (1989): 378. http://dx.doi.org/10.1007/bf01799246.
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Thakker, R. V., K. E. Davies, and J. L. H. O'Riordan. "Gene mapping of mineral metabolic disorders." Journal of Inherited Metabolic Disease 12, S1 (1989): 231–46. http://dx.doi.org/10.1007/bf01799298.
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Jones, Lewis, and Rosa Urbano Gutiérrez. "Circular ceramics: Mapping UK mineral waste." Resources, Conservation and Recycling 190 (March 2023): 106830. http://dx.doi.org/10.1016/j.resconrec.2022.106830.
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Son, Young-Sun, Byoung-Woon You, Eun-Seok Bang, et al. "Mapping Alteration Mineralogy in Eastern Tsogttsetsii, Mongolia, Based on the WorldView-3 and Field Shortwave-Infrared Spectroscopy Analyses." Remote Sensing 13, no. 5 (2021): 914. http://dx.doi.org/10.3390/rs13050914.
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This study produces alteration mineral maps based on WorldView-3 (WV-3) data and field shortwave-infrared (SWIR) spectroscopy. It is supported by conventional analytical methods such as X-ray diffraction, X-ray fluorescence, and electron probe X-ray micro analyzer as an initial step for mineral exploration in eastern Tsogttsetsii, Mongolia, where access is limited. Distributions of advanced argillic minerals (alunite, dickite, and kaolinite), illite/smectite (illite, smectite, and mixed-layered illite-smectite), and ammonium minerals (buddingtonite and NH4-illite) were mapped using the decorrelation stretch, band math, and mixture-tuned-matched filter (MTMF) techniques. The accuracy assessment of the WV-3 MTMF map using field SWIR data showed good WV-3 SWIR data accuracy for spectrally predominant alteration minerals such as alunite, kaolinite, buddingtonite, and NH4-illite. The combination of WV-3 SWIR mineral mapping and a drone photogrammetric-derived digital elevation model contributed to an understanding of the structural development of the hydrothermal system through visualization of the topographic and spatial distribution of surface alteration minerals. Field SWIR spectroscopy provided further detailed information regarding alteration minerals such as chemical variations of alunite, crystallinity of kaolinite, and aluminum abundance of illite that was unavailable in WV-3 SWIR data. Combining WV-3 SWIR data and field SWIR spectroscopy with conventional exploration methods can narrow the selection between deposit models and facilitate mineral exploration.
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Wu, Baokun, Xiaohui Ji, Mingyue He, et al. "Mineral Identification Based on Multi-Label Image Classification." Minerals 12, no. 11 (2022): 1338. http://dx.doi.org/10.3390/min12111338.
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The identification of minerals is indispensable in geological analysis. Traditional mineral identification methods are highly dependent on professional knowledge and specialized equipment which often consume a lot of labor. To solve this problem, some researchers use machine learning algorithms to quickly identify a single mineral in images. However, in the natural environment, minerals often exist in an associated form, which makes the identification impossible with traditional machine learning algorithms. For the identification of associated minerals, this paper proposes a deep learning model based on the transformer and multi-label image classification. The model uses transformer architecture to model mineral images and outputs the probability of the existence of various minerals in an image. The experiments on 36 common minerals show that the model can achieve a mean average precision of 85.26%. The visualization of the class activation mapping indicates that our model can roughly locate the identified minerals.
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Teboul, Naama, Yaron Gadri, Zipi Berkovich, Ram Reifen, and Zvi Peleg. "Genetic Architecture Underpinning Yield Components and Seed Mineral–Nutrients in Sesame." Genes 11, no. 10 (2020): 1221. http://dx.doi.org/10.3390/genes11101221.
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Genetic dissection of yield components and seed mineral-nutrient is crucial for understanding plant physiological and biochemical processes and alleviate nutrient malnutrition. Sesame (Sesamum indicum L.) is an orphan crop that harbors rich allelic repertoire for seed mineral–nutrients. Here, we harness this wide diversity to study the genetic architecture of yield components and seed mineral–nutrients using a core-collection of worldwide genotypes and segregating mapping population. We also tested the association between these traits and the effect of seed nutrients concentration on their bio-accessibility. Wide genetic diversity for yield components and seed mineral–nutrients was found among the core-collection. A high-density linkage map consisting of 19,309 markers was constructed and used for genetic mapping of 84 QTL associated with yield components and 50 QTL for seed minerals. To the best of our knowledge, this is the first report on mineral–nutrients QTL in sesame. Genomic regions with a cluster of overlapping QTL for several morphological and nutritional traits were identified and considered as genomic hotspots. Candidate gene analysis revealed potential functional associations between QTL and corresponding genes, which offers unique opportunities for synchronous improvement of mineral–nutrients. Our findings shed-light on the genetic architecture of yield components, seed mineral–nutrients and their inter- and intra- relationships, which may facilitate future breeding efforts to develop bio-fortified sesame cultivars.
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Ren, Zhongliang, Qiuping Zhai, and Lin Sun. "A Novel Method for Hyperspectral Mineral Mapping Based on Clustering-Matching and Nonnegative Matrix Factorization." Remote Sensing 14, no. 4 (2022): 1042. http://dx.doi.org/10.3390/rs14041042.
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The emergence of hyperspectral imagery paved a new way for rapid mineral mapping. As a classical hyperspectral classification method, spectral matching (SM) can automatically map the spatial distribution of minerals without the need for selecting training samples. However, due to the influence of noise, the mapping accuracy of SM is usually poor, and its per-pixel matching method is inefficient to some extent. To solve these problems, we propose an unsupervised clustering-matching mapping method, using a combination of k-means and SM (KSM). First, nonnegative matrix factorization (NMF) is used and combined with a simple and effective NMF initialization method (SMNMF) for feature extraction. Then, k-means is implemented to get the cluster centers of the extracted features and band depth, which are used for clustering and matching, respectively. Finally, dimensionless matching methods, including spectral angle mapper (SAM), spectral correlation angle (SCA), spectral gradient angle (SGA), and a combined matching method (SCGA) are used to match the cluster centers of band depth with a spectral library to obtain the mineral mapping results. A case study on the airborne hyperspectral image of Cuprite, Nevada, USA, demonstrated that the average overall accuracies of KSM based on SAM, SCA, SGA, and SCGA are approximately 22%, 22%, 35%, and 33% higher than those of SM, respectively, and KSM can save more than 95% of the mapping time. Moreover, the mapping accuracy and efficiency of SMNMF are about 15% and 38% higher than those of the widely used NMF initialization method. In addition, the proposed SCGA could achieve promising mapping results at both high and low signal-to-noise ratios compared with other matching methods. The mapping method proposed in this study provides a new solution for the rapid and autonomous identification of minerals and other fine objects.
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JELLOULI, Amine, Mohcine CHAKOURI, Zakaria ADIRI, Jaouad EL HACHIMI, and Abdessamad JARI. "Lithological and Hydrothermal Alteration Mapping Using Terra ASTER and Landsat-8 OLI Multispectral Data in the North-Eastern Border of Kerdous Inlier, Western Anti-Atlasic Belt, Morocco." Artificial Satellites 60, no. 1 (2025): 14–36. https://doi.org/10.2478/arsa-2025-0002.
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ABSTRACT The copper belt of Anti-Atlas is recognized with several mineral occurrences of Cu, Zn, Mn, Ag, Au, and iron. We used ASTER and OLI in lithological and mineral detection and mapping. The lithological mapping was performed using principal components analysis (PCA), minimum noise fraction (MNF), and two classifiers: maximum likelihood (ML) and support vector machine (SVM). The hydrothermally altered zones were detected based on ASTER VNIR/SWIR bands by the integration of Ninomiya indices and constrained energy minimization (CEM) algorithm. In our study area, the enhanced band combinations of ASTER MNF1, PC4, and PC2 and OLI MNF1, PC5, and PC3 were applied for lithological discrimination. The OLI and ML classification shows the best lithological mapping accuracy with an overall accuracy of 91.74% and a 0.90 Kappa coefficient, followed by SVM with an overall accuracy of 88.82% and a 0.86 Kappa coefficient using the same sensor. The hydrothermal alteration mapping reveals alunite, chlorite, calcite, epidote, illite, kaolinite, montmorillonite, muscovite, and pyrophyllite minerals, principally in phyllic and argillic altered areas. The adopted methodology for lithological and mineralogical mapping can be used in other regions with similar criteria to the study area.
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Habashi, Jabar, Hadi Jamshid Moghadam, Majid Mohammady Oskouei, Amin Beiranvand Pour, and Mazlan Hashim. "PRISMA Hyperspectral Remote Sensing Data for Mapping Alteration Minerals in Sar-e-Châh-e-Shur Region, Birjand, Iran." Remote Sensing 16, no. 7 (2024): 1277. http://dx.doi.org/10.3390/rs16071277.
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Remote sensing satellite imagery consistently provides valuable and frequent information, enabling the exploration of mineral resources across immense, remote and harsh domains. Recent developments in spaceborne hyperspectral remote sensing have opened avenues to support diverse remote sensing applications, particularly in the realm of mineral exploration. This study evaluates the capabilities of the PRecursore IperSpettrale della Missione Applicativa (PRISMA) hyperspectral satellite data for mapping alteration minerals using the Matched Filtering Unmixing (MFU) approach in the Sar-e-châh-e-shur, Birjand, Iran. Minerals such as richterite, augite, psilomelane, ilmenite, kaolinite, smectite, mirabilite, muscovite, and chlorite were identified using the vertex component analysis (VCA) technique. Subsequently, alteration mineral maps of the study area were generated using a matched filtering technique. Additionally, through the integration of X-ray diffraction (XRD) analysis, thin section examination, geochemical study of stream sediments, and interpretation of geological maps, potential alteration mineralization zones were delineated in the study area. Ultimately, the validation process, which included comparing the maps with the findings derived from the PRISMA remote sensing study, was conducted using the normal score equation. Thus, our results yielded a normalized score of 3.42 out of 4, signifying an 85.71% agreement with the regional geological characteristics of the study area. The results of this investigation highlight the substantial potential of the PRISMA dataset for systematic alteration mineral mapping and consequent exploration of ore minerals, specifically in challenging and inaccessible terrains.
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Tusa, Laura, Louis Andreani, Mahdi Khodadadzadeh, et al. "Mineral Mapping and Vein Detection in Hyperspectral Drill-Core Scans: Application to Porphyry-Type Mineralization." Minerals 9, no. 2 (2019): 122. http://dx.doi.org/10.3390/min9020122.
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The rapid mapping and characterization of specific porphyry vein types in geological samples represent a challenge for the mineral exploration and mining industry. In this paper, a methodology to integrate mineralogical and structural data extracted from hyperspectral drill-core scans is proposed. The workflow allows for the identification of vein types based on minerals having significant absorption features in the short-wave infrared. The method not only targets alteration halos of known compositions but also allows for the identification of any vein-like structure. The results consist of vein distribution maps, quantified vein abundances, and their azimuths. Three drill-cores from the Bolcana porphyry system hosting veins of variable density, composition, orientation, and thickness are analysed for this purpose. The results are validated using high-resolution scanning electron microscopy-based mineral mapping techniques. We demonstrate that the use of hyperspectral scanning allows for faster, non-invasive and more efficient drill-core mapping, providing a useful tool for complementing core-logging performed by on-site geologists.
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Aliyu, Ohinowi, and Kankara Aliyu. "Utilizing landsat-8 sensor operational land image data for hydrothermal alteration mapping within Anka Schist Belt, northwestern Nigeria." Zbornik radova Departmana za geografiju, turizam i hotelijerstvo, no. 49-2 (2020): 127–49. http://dx.doi.org/10.5937/zbdght2002127a.
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Exploring for mineral deposits within the Anka Schist Belt involves the use of traditional geological techniques such as geochemical and geophysical studies that are very expensive and time consuming. There is therefore need for a better alternative that will provide accurate and reliable information with cost effective and time efficient solution. This effort seeks to explore the potential of remotely sensed digital data in highlighting mineralized zones through hydrothermal alteration studies. Landsat 8 OLI data covering the investigated area was used to detect and map locations of hydrothermal alterations. Image processing methods used were spectral enhancement, false colour composites, band rationing and Principal Component Analysis. Results of false colour composites of band 5: 7: 3 highlighted generally locations of hydrothermal alterations. Band ratios of 4/2, 6/7 and 6/5 revealed the presence of ferric iron minerals, clay rich minerals and ferrous minerals respectively. Principal Components (PCs) of two sets of images (2, 4, 5, 7 H-image and 2,5,6,7 F-image) depicting iron-oxide and hydroxyl mineral deposits as bright pixels were generated. Colour composite of H, F and H+F images enhanced the location of the mineral deposits, by showing areas of mineralization in dark blue (Fe rich), bright yellow (clay rich) and white (Fe and clay rich areas) pixels. Field coordinates of mining locations were superimposed on the remote sensing generated mineral map. The results were found to be in tune. This study recommends the use of remote sensing and geospatial technology in mineral studies through hydrothermal alteration within the basement complex rocks of Nigeria.
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Velev, Stefan, Kamen Bogdanov, Ivan Krumov, Ognian Ognianov, and Yana Georgieva. "Remote sensing mapping and 3D modelling of Petelovo Au-epithermal system, Panagyurishte ore district, Bulgaria." Review of the Bulgarian Geological Society 83, no. 3 (2022): 203–6. http://dx.doi.org/10.52215/rev.bgs.2022.83.3.203.
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Remote sensing drone-based study combined with field mapping, XRD and XRF tests for mineral detection outlined advanced argillic, argillic and propylitic alteration domains within the Petelovo silica cap to demonstrate quick approach for mineral alteration mapping. Mineral alteration modelling by 3D Leapfrog Geo outlined zonal patterns around epithermal high-sulphidation Cu-Au mineralization could be employed as a pathfinder for gold enrichment.
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Yalçın, Cihan, and Ali Karan. "Application of geophysical methods in subsurface mapping and mineral exploration: Adiyaman-Besni region, Türkiye." Journal of Geography and Cartography 7, no. 2 (2024): 10193. https://doi.org/10.24294/jgc10193.
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The present study aimed to delineate subsurface features and identify prospective metallic mineral deposits in the Adıyaman-Besni area, situated within the Southeastern Anatolian Thrust Belt of Turkey. This region, characterized by ophiolitic mélanges and volcanic massive sulfide (VMS) deposits in its geological framework, possesses significant mineralization potential, encompassing copper, lead, and various other sulfide minerals. Utilizing the combined methodologies of Induced Polarization (IP) and Electrical Resistivity Tomography (ERT), a comprehensive electrical mapping of the subsurface structures was conducted, revealing that mineralized zones had low resistivity and high chargeability. The findings indicate that the combined use of IP and ERT techniques yields excellent precision in accurately delineating the features of sulfide mineralization and the peripheries of mineral deposits. This study offers fundamental data for the economic assessment of prospective mineral deposits in the Adıyaman-Besni region and underscores the benefits of IP and ERT techniques in subsurface mapping and mineralization delineation investigations. The mineralized zone has low resistivity (< 50 ohm-m) and strong chargeability (> 30 ms), according to geophysical tests. It also offers a methodological framework for subsequent mineral exploration research in analogous geological formations.
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Casey, K. A., A. Kääb, and D. I. Benn. "Geochemical characterization of supraglacial debris via in situ and optical remote sensing methods: a case study in Khumbu Himalaya, Nepal." Cryosphere 6, no. 1 (2012): 85–100. http://dx.doi.org/10.5194/tc-6-85-2012.
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Abstract. Surface glacier debris samples and field spectra were collected from the ablation zones of Nepal Himalaya Ngozumpa and Khumbu glaciers in November and December 2009. Geochemical and mineral compositions of supraglacial debris were determined by X-ray diffraction and X-ray fluorescence spectroscopy. This composition data was used as ground truth in evaluating field spectra and satellite supraglacial debris composition and mapping methods. Satellite remote sensing methods for characterizing glacial surface debris include visible to thermal infrared hyper- and multispectral reflectance and emission signature identification, semi-quantitative mineral abundance indicies and spectral image composites. Satellite derived supraglacial debris mineral maps displayed the predominance of layered silicates, hydroxyl-bearing and calcite minerals on Khumbu Himalayan glaciers. Supraglacial mineral maps compared with satellite thermal data revealed correlations between glacier surface composition and glacier surface temperature. Glacier velocity displacement fields and shortwave, thermal infrared false color composites indicated the magnitude of mass flux at glacier confluences. The supraglacial debris mapping methods presented in this study can be used on a broader scale to improve, supplement and potentially reduce errors associated with glacier debris radiative property, composition, areal extent and mass flux quantifications.
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Reyes Ayala, Karen Itzel, Primož Kajdič, Jaime Urrutia Fucugauchi, et al. "Mapping the mineralogy in the Oxia Planum and Mawrth Vallis ExoMars landing sites – implications for aqueous alteration and paleoenvironmental evolution." Revista Mexicana de Ciencias Geológicas 40, no. 2 (2023): 174–86. http://dx.doi.org/10.22201/cgeo.20072902e.2023.2.1744.
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Mineralogical studies constitute one of the main tools to investigate geological processes on Mars. Here, the mineralogy of the ExoMars landing sites Oxia Planum and Mawrth Vallis is analyzed based on multispectral datasets of the CRISM Compact Reconnaissance Imaging Spectrometer for Mars of Mars Reconnaissance Orbiter, TES Thermal Emission Spectrometer of Mars Global Surveyor and OMEGA Visible and Infrared Mineralogical Mapping Spectrometer of Mars Express are integrated and interpreted. Primary and secondary minerals of carbonates, oxides, silicates and sulphates mineral groups are characterized, with most minerals produced by aqueous alteration, with 35 mineral species identified, amongst which phyllosilicates are the dominant group. Coexisting mineral types point to distinct hydrothermal and alteration stages. Our investigation supports that water must have been present on early Mars on superficial and endogenous processes, causing direct precipitation and transformations of secondary minerals, with some not usually formed simultaneously. Hence, it is likely that species may have arisen at different times, due to changes of Martian climate. Some minerals may have formed in the subsurface. The Martian palaeoenvironments might include subaerial, underwater, and shallow subsurface hydrothermal systems, cold springs, alkaline lakes, sabkhas, and playas. Our results contribute to our understanding of the planned landing sites to prepare for what the ExoMars rover mission may encounter, highlighting the impact that water may have had on the mineral genesis, alteration processes and their astrobiological potential.
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Ullah, S., and A. Iqbal. "APPLICATION OF HYPERSPECTRAL THERMAL EMISSION SPECTROMETER (HYTES) DATA FOR HYSPIRI OPTIMAL BAND POSITIONING TO CHARACTERIZE SURFACE MINERALS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1893–97. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1893-2019.
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<p><strong>Abstract.</strong> This study aimed to characterize surface minerals from high dimensional HyTES (Hyperspectral Thermal Emission Spectrometer) data comprised of 256 spectral bands between 7.5 and 12&amp;thinsp;&amp;mu;m (i.e., TIR domain of the electromagnetic spectrum). The HyTES is across-track imager and can image 512 pixels with spatial resolution varies between 5 to 50&amp;thinsp;m depending upon aircraft flying height. HyTES is developed to support the HyspIRI (Hyperspectral Infrared Imager) mission by acquiring TIR data at much higher spectral and spatial resolutions in-order to define the optimum band positions for the TIR instrument of HyspIRI. For earth compositional mapping, the HyTES images of Cuprite and Death Valley regions were acquired in summer 2014 and spectral emissivities of fifteen minerals classes were extracted from regions of known mineral compositions and were randomly divided into training and testing sets (each mineral class com-prised of 100 spectra). These extracted emissivity signatures were then used for categorizing minerals and for finding HyspIRI's optimal band positions for earth composition mapping using Genetic Algorithm (GA) coupled with Spectral angle mapper (SAM). The GA-SAM was trained for fifteen mineral classes and the algorithms were run iteratively 40 times. High calibration (&amp;gt;&amp;thinsp;95&amp;thinsp;%) and validation (&amp;gt;&amp;thinsp;90&amp;thinsp;%) accuracies were achieved with limited numbers (seven) of spectral bands selected by GA-SAM. Knowing the important band Positions will help scientist of HyspIRI group to place spectral bands at regions were accuracies of Earth compositional mapping can be enhanced.</p>
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Radusinović, Slobodan, Robert Šajn, Božica Jovanović, et al. "The primary and secondary mineral resources of Montenegro and their mapping into the European data model." Geologia Croatica 75, Special Issue (2022): 335–48. http://dx.doi.org/10.4154/gc.2022.20.
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Primary and secondary mineral resources are of strategic importance to the EU economy. Montenegro, as a country candidate for membership in the EU, is required to follow (and later to implement) European policies, strategies as well as initiatives, including those related to mineral resources and the mining sector. The importance of providing access to mineral raw materials in the future is recognized by the EU, as well as meeting the needs of European industry, maintaining employment and ensuring further development. Considering the overall economic situation in Montenegro, it is important to encourge the mining sector and other industries based on the use of mineral resources in making a greater contribution to the development and sustainability of society as a whole and also increase the share of national GDP. The potential for discovery and utilization of primary and secondary mineral resources in Montenegro is demonstrated. The most important metallic mineral resources are bauxite, lead and zinc, while conventional energy resources include coal (oil and gas potential has yet to be proven). In addition, there are abundant non-metallic mineral raw materials - industrial minerals and construction materials. Secondary mineral resources, especially aluminous red mud (bauxite residue), are also significant and have been the subject of research in recent years. Tailings from flotation processes at operating and abandoned lead and zinc mines might also be of interest for metal recovery. Bottom and flay ash from thermal power plants, slag from steel production, as well as marlstone and limestone from the hanging wall of coal deposits may also have potential. Waste rocks could be used particularly for secondary aggregate production. A database was developed and the most important deposits of primary and secondary mineral resources in Montenegro were mapped during the RESEERVE project. Mineral data were harmonised so as to be INSPIRE compliant. In addition, some novel geochemical exploration results of secondary mineral resources are presented.
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Singh, Akarsh, and Sudha Agrahari. "Synergistic Application of Geoelectrical Methods for Mapping Placers along the Coastal Periphery of Southern Odisha, India." Journal Of The Geological Society Of India 100, no. 12 (2024): 1755–64. https://doi.org/10.17491/jgsi/2024/174047.
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ABSTRACT This study examines the distribution and amount of placer deposits enriched with heavy minerals along the coastal region of Odisha, India utilizing a combination of geoelectrical techniques. Abundant heavy mineral placers have been identified along the southern coast of Odisha. However, while geological investigations have been conducted, there is a notable absence of geophysical analyses in this specific area. To ascertain both the horizontal and vertical extensions of heavy mineral placer deposits, a comprehensive investigation was undertaken, involving the execution of 23 ERT (Electrical Resistivity Tomography) and TDIP (Time-Domain Induced Polarization) profiles within the designated study zone. These profiles unveiled substantial heavy-mineral zones spanning from shallow layers to depths of approximately 10-15 m. Two primary patterns of mineralization were discerned: firstly, dispersed occurrences which manifested closer to the surface, characterized by irregular black patches of minerals; secondly, concentrated mineralization found at moderate to significant depths, hinting at concealed or buried deposits. Considering the presence of conductive minerals (ilmenite, magnetite) within the beach placers of the study locale, the application of ERT and IP methods proved to be viable. Additionally, the mineralization trends exhibited variance as one traversed from the southwest to the northeast within the study area. Notably, the resistivity of the heavy minerals ranged from 0.1 to 1.1 Ωm, coupled with chargeabilities surpassing 20 mV/V. Significantly, the arrangement of alternating heavy mineralization layers within the sand formations implied the potential presence of stratal anisotropy. Consequently, a specialized code was developed and implemented to perform 2D anisotropic inversion of the ERT data. The application of this anisotropic inversion rectified the depth estimations for concealed pockets of heavy mineralization. While conventional isotropic inversion suggested a concealed mineralization pocket at depths of 15-32 m, the anisotropic inversion revealed that the same mineral-bearing strata existed at depths of 16–28 m.
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Guha, S., H. Govil, M. Tripathi, and M. Besoya. "EVALUATING CROSTA TECHNIQUE FOR ALTERATION MINERAL MAPPING IN MALANJKHAND COPPER MINES, INDIA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5 (November 19, 2018): 251–54. http://dx.doi.org/10.5194/isprs-archives-xlii-5-251-2018.
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<p><strong>Abstract.</strong> Landsat-8 Operational Land Imager (OLI) data has been successfully employed in the field of mineral exploration to detect important minerals. In this study, Crosta technique was applied to identify the diagnostic features of hydroxyl minerals, carbonate minerals and iron oxides in Malanjkhand copper mines, India. The Crosta technique was applied to six [blue, green, red, near-infrared (NIR), shortwave infrared1 (SWIR1), shortwave infrared2 (SWIR2) bands and two sets of four (blue, red, NIR, SWIR1; and blue, near-infrared, SWIR1, SWIR2) bands of OLI data. Results show that the areas with alteration zones are enhanced much better by using six bands of OLI data. The alteration differences are examined with the Crosta technique using four band combinations. Crosta technique is very useful in generating the images of hydroxyl minerals, carbonate minerals, and iron oxides.</p>
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Hussain, Fiaz, Muhammad Afzal Rashid, Syed Ghulam Mohayuddin, and Muhammad Qasim. "Mapping of Macro Minerals in Selected Summer Forages of District Kasur." Journal of Agriculture and Veterinary Science 1, no. 1 (2023): 17–26. http://dx.doi.org/10.55627/agrivet.01.01.0241.
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Minerals are essential for production, reproduction and health of livestock. Among feed resources, forages are considered as a cheap source of minerals for livestock. Therefore, the aim of this study was to map the mineral contents of selected summer forages of district Kasur. Four seasonal forages including maize, sorghum and millet were selected for analysis of mineral contents. A total n=400 forage samples were collected from four tehsils of district Kasur including: Kasur, Chunian, Pattoki and Kot Radha Kishan (KRK). For Sample collection GPS essential android mobile application was used to determine latitude and longitude for sample collection site. After collection, samples were processed for wet digestion. Sodium (Na) and Potassium (K) content was analyzed using flame photometer; whereas, Calcium (Ca) and Phosphorus (P) contents were analyzed by atomic absorption and spectrophotometer. The data were analyzed using one-way Analysis of Variance (ANOVA). Results of the current experiment showed that Ca concentration was higher (P<0.05) in the Chunian compared with the KRK and Pattoki in maize forage. Additionally, sorghum Ca concentration was same (P>0.05) in the entire tehsils of the Kasur. Whereas, Ca contents of millet was higher (P<0.05) in the Kasur compared with the Chunian. Concentration of P in maize and sorghum forages were not different (P>0.05) among all the tehsils. Whereas, P contents of millet was higher (P<0.05) in the KRK compared with the Chunian. In maize forage, Na contents was higher (P<0.05) in the KRK compared with the Kasur and was lower in the Pattoki and the Chunian. Na concentration of sorghum forage was greater (P<0.05) in the Pattoki compared with the other tehsils. Whereas, Na contents of millet forage was higher (P<0.05) in the Chunian than the KRK. K contents of maize and millet forages were not different (P0.05) among the all tehsils of the district Kasur. Whereas, K concentration was higher (P<0.05) in the KRK compared with the Kasur in sorghum forage. It can be concluded that concentration of Ca, P, N and K in summer forages including maize, sorghum and millet of district Kasur varied. Data can help us to identify the mineral deficiency in district Kasur and formulate area specific mineral mixtures to manage mineral status of livestock.
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Hamedianfar, Alireza, Kati Laakso, Maarit Middleton, Tuomo Törmänen, Juha Köykkä, and Johanna Torppa. "Leveraging High-Resolution Long-Wave Infrared Hyperspectral Laboratory Imaging Data for Mineral Identification Using Machine Learning Methods." Remote Sensing 15, no. 19 (2023): 4806. http://dx.doi.org/10.3390/rs15194806.
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Laboratory-based hyperspectral imaging (HSI) is an optical non-destructive technology used to extract mineralogical information from bedrock drill cores. In the present study, drill core scanning in the long-wave infrared (LWIR; 8000–12,000 nm) wavelength region was used to map the dominant minerals in HSI pixels. Machine learning classification algorithms, including random forest (RF) and support vector machine, have previously been applied to the mineral characterization of drill core hyperspectral data. The objectives of this study are to expand semi-automated mineral mapping by investigating the mapping accuracy, generalization potential, and classification ability of cutting-edge methods, such as various ensemble machine learning algorithms and deep learning semantic segmentation. In the present study, the mapping of quartz, talc, chlorite, and mixtures thereof in HSI data was performed using the ENVINet5 algorithm, which is based on the U-net deep learning network and four decision tree ensemble algorithms, including RF, gradient-boosting decision tree (GBDT), light gradient-boosting machine (LightGBM), AdaBoost, and bagging. Prior to training the classification models, endmember selection was employed using the Sequential Maximum Angle Convex Cone endmember extraction method to prepare the samples used in the model training and evaluation of the classification results. The results show that the GBDT and LightGBM classifiers outperformed the other classification models with overall accuracies of 89.43% and 89.22%, respectively. The results of the other classifiers showed overall accuracies of 87.32%, 87.33%, 82.74%, and 78.32% for RF, bagging, ENVINet5, and AdaBoost, respectively. Therefore, the findings of this study confirm that the ensemble machine learning algorithms are efficient tools to analyze drill core HSI data and map dominant minerals. Moreover, the implementation of deep learning methods for mineral mapping from HSI drill core data should be further explored and adjusted.
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Xie, B. S., S. Y. Zhou, and L. X. Wu. "AN INTEGRATED MINERAL SPECTRAL LIBRARY USING SHARED DATA FOR HYPERSPECTRAL REMOTE SENSING AND GEOLOGICAL MAPPING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B5-2020 (August 24, 2020): 69–75. http://dx.doi.org/10.5194/isprs-archives-xliii-b5-2020-69-2020.
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Abstract. Mineral spectral library (MSL) is the foundation of hyperspectral remote sensing, and a significant tool of storing and managing massive mineral spectral data to facilitate the matching or identifying of unknown rocks and minerals conveniently and fast. However, mineral spectral data are scattered and stored in different spectral libraries worldwide, which behave different spectral resolutions, mineral categories and measurement parameters, and hinder its application in field investigation, mineral identification, landcover identification and geological mapping. An integrated MSL using shared data is developed currently in Central South University, China, to improve the properties of MSL. We collected the shared spectral data and related information (e.g., mineral attribute data, spectrometer information, etc.) worldwide, performed data cleaning measures to retain the qualified spectral data and consolidated all the data in a common framework so as to establish a reliable and comprehensive dataset, and developed an integrated MSL for data management and diversified applications. The user can analysis the target spectrum with the spectrum absorption characteristic parameters, and match the measured spectral curve with the reference spectrum in the integrated MSL to find the most similar spectrum curve. It’s crucial to note that a new spectrum classifier was designed to limit the scope of matching for improving the efficiency of identification when the experimental sample lacks the specific information. The integrated MSL is developed in B/S and C/S website environments. A demonstration of functions of the integrated MSL and its preliminary applications are introduced in the article.
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Williams, M. L., and M. J. Jercinovic. "Application of Electron Microprobe Age Mapping and Dating of Monazite." Microscopy and Microanalysis 6, S2 (2000): 406–7. http://dx.doi.org/10.1017/s1431927600034528.
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High resolution X-ray mapping and dating of monazite (Th, REE-phosphate) using the electron microprobe is an exceptionally powerful technique for structural, metamorphic, and tectonic analysis in geology. Age determination of geologic materials has been conventionally accomplished by mass spectrometry-based analysis of radioisotopic ratios in minerals from hand-picked mineral separates, careful sampling of individual grains out of petrographic thin sections, or by detailed ion probe analysis. Recently, use of the electron microprobe for dating purposes has been attempted. In principal, the concentrations of Th, U and Pb uniquely define the age if non-radiogenic Pb is either not initially present or can be subtracted from the total Pb. Monazite contains high concentrations of Th and U and does not appear to incorporate significant non-radiogenic Pb during mineral growth. Furthermore, monazite is a ubiquitous accessory phase in many metamorphic and igneous rocks, making it ideal for microprobe dating.
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Liu, Yue, Kefa Zhou, and Qinglin Xia. "A MaxEnt Model for Mineral Prospectivity Mapping." Natural Resources Research 27, no. 3 (2017): 299–313. http://dx.doi.org/10.1007/s11053-017-9355-2.
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Porwal, Alok, E. J. M. Carranza, and M. Hale. "Bayesian network classifiers for mineral potential mapping." Computers & Geosciences 32, no. 1 (2006): 1–16. http://dx.doi.org/10.1016/j.cageo.2005.03.018.
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Pan, Guocheng. "Indicator favorability theory for mineral potential mapping." Nonrenewable Resources 2, no. 4 (1993): 292–311. http://dx.doi.org/10.1007/bf02257540.
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-, Palkrisman, and Arif Budiman. "PEMETAAN PERSENTASE KANDUNGAN DAN NILAI SUSEPTIBILITAS MINERAL MAGNETIK PASIR BESI PANTAI SUNUR KABUPATEN PADANG PARIAMAN SUMATERA BARAT." Jurnal Fisika Unand 3, no. 4 (2024): 242–48. https://doi.org/10.25077/jfu.3.4.242-248.2014.
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ABSTRAKTelah dilakukan penelitian tentang pemetaan persentase kandungan dan nilai suseptibilitas mineral magnetik pasir besi Pantai Sunur Kabupaten Padang  Pariaman menggunakan metode Anisotropy of Magnetic Susceptibility (AMS). Hasil perhitungan persentase kandungan mineral magnetik dari 55 sampel menunjukkan rentang nilai 6,5% hingga 61,3%. Hasil perhitungan suseptibilitas mineral magnetik dari 55 sampel menunjukkan rentang nilai 333,65 × 10-8 m3/kg hingga 2883,67 × 10-8 m3/kg, berdasarkan nilai ini dapat diperkirakan bahwa mineral utama penyusun pasir besi Pantai Sunur adalah hematit dan ilmenit. Pemetaan dilakukan dengan menggunakan software surfer 9.0 pada 5 lintasan yang sejajar garis pantai. Lintasan pertama berada di tepi laut, dan lintasan berikutnya menjauhi laut dengan jarak antar lintasan 25 m. Hasil pemetaan persentase kandungan mineral magnetik menunjukkan bahwa Lintasan 3 (tengah lintasan) yang berjarak 50 meter dari lintasan pertama (tepi laut) mempunyai nilai kandungan persentase mineral magnetik terbesar. Hasil pemetaan nilai suseptibilitas mineral magnetik menunjukkan bahwa Lintasan 5 yang berjarak 100 meter dari lintasan pertama mempunyai nilai suseptibilitas terbesar.Kata Kunci : pasir besi, anisotropi, magnetik, dan suseptibilitas.AbstractThe mapping of content percentage and magnetic susceptibility of iron sand from Sunur beach, Padang Pariaman, using Anisotropy of Magnetic Susceptibility (AMS) methods has been conducted. Based on the calculation, the content percentage of the magnetic mineral of iron sand of 55 samples are from 6.5% to 61.3%. The value of magnetic susceptibility of 55 samples are from 333.65 × 10-8 m3/kg to 2883.67 × 10-8 m3/kg. From the value, it can be estimated that the main magnetic minerals of iron sand Sunur beach are hematite and ilmenite. The mapping is done by using Surfer 9.0 software at 5 tracks parallel to the shoreline. The first track is near the sea, and the next track is 25 m away from the first track to the land side. It shows that track 3 (middle path) within 50 meters from the first track (waterfront) has the largest percentage of magnetic mineral. The magnetic susceptibility mapping shows that track 5 within 100 meters from the first track has the greatest value of susceptibility.Keywords : iron sand, anisotropy, magnetic, and susceptibility.
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Bogdanov, Kamen, Timo Dönsberg, Teemu Kääriäinen, et al. "AHS mapping for hydrothermal alterations detection and mineral deposits exploration." Review of the Bulgarian Geological Society 83, no. 3 (2022): 171–74. http://dx.doi.org/10.52215/rev.bgs.2022.83.3.171.
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The Actve Hyperspectral Sensing (AHS) application for a mineral mapping have been tested on outcrops with porphyry-copper style of mineralization in the Vlaykov Vruh and Tsar Assen deposits. As a new tool used in mineral exploration AHS reveals the advantages for mineral detection and targeting as an express new innovative technology and efficient tool for mineral prospecting.
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Agrawal, Neelam, Himanshu Govil, Gaurav Mishra, Manika Gupta, and Prashant K. Srivastava. "Evaluating the Performance of PRISMA Shortwave Infrared Imaging Sensor for Mapping Hydrothermally Altered and Weathered Minerals Using the Machine Learning Paradigm." Remote Sensing 15, no. 12 (2023): 3133. http://dx.doi.org/10.3390/rs15123133.
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Satellite images provide consistent and frequent information that can be used to estimate mineral resources over a large spatial extent. Advances in spaceborne hyperspectral remote sensing (HRS) and machine learning can help to support various remote-sensing-based applications, including mineral exploration. Leveraging these advances, the present study evaluates recently launched PRISMA spaceborne satellite images to map hydrothermally altered and weathered minerals using various machine-learning-based classification algorithms. The study was performed for the town of Jahazpur in Rajasthan, India (75°06′23.17″E, 25°25′23.37″N). The distribution map for minerals such as kaolinite, talc, and montmorillonite was generated using the spectral angle mapper technique. The resultant mineral distribution map was verified through an intensive field validation survey on surface exposures of the minerals. Furthermore, the obtained pixels of the end-members were used to develop the machine-learning-based classification models. Measures such as accuracy, kappa coefficient, F1 score, precision, recall, and ROC curve were employed to evaluate the performance of developed models. The results show that the stochastic gradient descent and artificial-neural-network-based multilayer perceptron classifiers were more accurate than other algorithms. Results confirm that the PRISMA dataset has enormous potential for mineral mapping in mountainous regions utilizing a machine-learning-based classification framework.
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Yang, Chen, Hekun Jia, Lifang Dong, Haishi Zhao, and Minghao Zhao. "Selection of Landsat-8 Operational Land Imager (OLI) Optimal Band Combinations for Mapping Alteration Zones." Remote Sensing 16, no. 2 (2024): 392. http://dx.doi.org/10.3390/rs16020392.
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In typical alteration extraction methods, e.g., band math and principal component analysis (PCA), the bands or band combinations unitized to extract altered minerals are usually selected based on empirical models or previous rules. This results in significant differences in the alteration of mineral mapping even in the same area, thus greatly increasing the uncertainty of mineral resource prediction. In this paper, an intelligent alteration extraction approach was proposed in which an optimization algorithm, i.e., a genetic algorithm (GA), was introduced into the PCA; this approach is termed GA-PCA and is used for selecting the optimized band combinations of mineralized alterations. The proposed GA-PCA was employed to map iron oxides and hydroxyl minerals using the most commonly adopted multispectral data, i.e., Landsat-8 OLI data, at the Lalingzaohuo polymetallic deposits, China. The results showed that the spectral characteristics of GA-PCA-selected OLI band combinations in the research area were beneficial for enhancing alteration information and were more capable of suppressing the interference of vegetation information. The mapping alteration zones using the GA-PCA approach had a higher agreement with known ore spots, i.e., 25% and 33.3% in ferrous-bearing and hydroxyl-bearing deposits, compared to the classical PCA. Furthermore, two predicted targets (not shown in the classical PCA results) were precisely obtained via analyzing the GA-PCA alteration maps combined with the ore-forming geological conditions of the mine and its tectonic characteristics. This indicated that the intelligent selection of mineral alteration band combinations increased the reliability of remote sensing-based mineral exploration.