نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشکده مهندسی معدن، پردیس فنی دانشگاه تهران، تهران، ایران
2 شرکت مهندسی کوشا معدن، تهران، ایران
3 دانشگاه شهید باهنر، کرمان، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Summary
This study presents an integrated remote sensing–based methodology for mapping hydrothermal argillic alteration zones in the Jebal Barez region, southeastern Iran, using ASTER satellite imagery. The novelty lies in the combined use of Spectral Angle Mapper (SAM), Matched Filtering (MF), and fractal value–area modeling for anomaly detection and classification. Atmospheric correction was performed using the IARR method, followed by extraction of kaolinite spectral signatures from the USGS spectral library. SAM and MF outputs were thresholded and classified, and their results were refined using fractal modeling to detect scale-invariant spatial patterns. Ground validation with 34 field samples confirmed the presence of Pb–Zn mineralization in high-response zones. This approach provides a replicable framework for mineral exploration in complex terrains using freely available remote sensing data.
Introduction
Hydrothermal alteration mapping is a key component in mineral exploration, particularly in structurally complex regions. ASTER multispectral imagery provides critical spectral bands in the VNIR and SWIR ranges, enabling detection of alteration minerals such as kaolinite. While SAM and MF are well-established spectral classifiers, their integration with fractal modeling for anomaly refinement remains underexplored in southeastern Iran. This study addresses this gap by integrating SAM and MF with a fractal value–area approach to enhance the separation of alteration anomalies from background noise, thereby improving target delineation for exploration.
Methodology and Approaches
ASTER Level 1T imagery (scene acquired on June 17, 2007) underwent radiometric, geometric, and atmospheric correction (IARR). Kaolinite reference spectra were resampled to match ASTER bands and used for SAM and MF classification. Thresholds for SAM (0.1 radians) and MF (mean + 1.5σ) were applied based on histogram analysis. The fractal value–area model was then used to classify anomalies by detecting breakpoints on log–log plots of cumulative area versus pixel value. Thirty-four ground control points were collected, with petrographic analysis conducted on six representative samples. A detailed methodological workflow was developed to ensure reproducibility.
Results
SAM and MF successfully delineated argillic alteration zones, with MF achieving higher overall accuracy (82.35%) compared to SAM (73.52%). MF outputs provided more spatially compact anomalies, while SAM captured broader alteration halos. Fractal modeling effectively separated background, weak, and strong anomalies, revealing strong spatial correlation with Pb–Zn mineralization sites. Field observations confirmed alteration assemblages dominated by kaolinite, sericite, and iron oxides, consistent with SWIR absorption features in the remote sensing data.
Conclusions
The integrated SAM–MF–fractal modeling approach improves the accuracy and spatial refinement of hydrothermal alteration mapping using ASTER imagery. This methodology is particularly effective in complex geological terrains, offering both scalability and reproducibility. While ASTER’s moderate spatial resolution poses limitations for detecting small-scale features, the workflow can be enhanced with hyperspectral data and automated threshold detection. The approach is recommended for mineral exploration in similar arid and mountainous environments.
کلیدواژهها [English]
)