application of fractal modeling for mapping hydrothermal alteration zones using aster imagery in the southeastern part of iran

This study presents an integrated approach to map hydrothermal argillic alteration zones using aster satellite imagery in the jebal barez region of southeastern iran. the novelty of this research lies in the combination of spectral angle mapper (sam), matched filtering (mf), and fractal value–area modeling for anomaly detection and classification. after atmospheric correction using the iarr method, kaolinite spectral signatures were extracted and used in the sam and mf techniques to delineate altered zones. a total of 34 ground control points were collected across representative lithologies to validate remote sensing outputs. sam and mf both identified key alteration zones, with mf demonstrating higher classification accuracy (82.35%) compared to sam (73.52%). the fractal model enabled effective separation of anomalous zones by detecting scale-invariant spatial patterns and extracting critical breakpoints. the integration of fractal modeling with spectral analysis provided improved anomaly delineation and exploration targeting. field validation confirmed the presence of pb–zn mineralization and silica-rich alteration in high-response zones. this methodology offers a replicable framework for mineral exploration in complex terrains using freely available remote sensing data. a detailed workflow chart is also proposed to enhance clarity and reproducibility. this study presents an integrated approach to map hydrothermal argillic alteration zones using aster satellite imagery in the jebal barez region of southeastern iran. the novelty of this research lies in the combination of spectral angle mapper (sam), matched filtering (mf), and fractal value–area modeling for anomaly detection and classification. after atmospheric correction using the iarr method, kaolinite spectral signatures were extracted and used in the sam and mf techniques to delineate altered zones. a total of 34 ground control points were collected across representative lithologies to validate remote sensing outputs. sam and mf both identified key alteration zones, with mf demonstrating higher classification accuracy (82.35%) compared to sam (73.52%). the fractal model enabled effective separation of anomalous zones by detecting scale-invariant spatial patterns and extracting critical breakpoints. the integration of fractal modeling with spectral analysis provided improved anomaly delineation and exploration targeting. field validation confirmed the presence of pb–zn mineralization and silica-rich alteration in high-response zones. this methodology offers a replicable framework for mineral exploration in complex terrains using freely available remote sensing data. a detailed workflow chart is also proposed to enhance clarity and reproducibility.