Generation of enhanced and augmented evidence maps corresponding to operation of ore-forming geological processes improves mineral exploration success. Structural lineaments have a substantial impact on such processes. Therefore, precise recognition of the lineaments facilitates mineral exploration targeting toward undiscovered mineralized zones. The paper aims 1) to recognize and analyze structural lineaments, especially those which are spatially and genetically associated with orogenic gold mineral system, through analysis of a multidisciplinary geospatial dataset including aeromagnetic geophysics, the Advanced Spaceborne Thermal Emission, and Reflection Radiometer imagery and Digital Elevation Model data and 2) to generate an efficient structural evidence layer for exploration targeting. The methods applied are edge enhancement filters with line detection algorithms. The detection algorithm, Hough Transform, and total horizontal derivative of upward continued filter and its tilt derivative) were utilized to extract structural lineaments. Evaluation of the spatial association of known Au mineralization in the study area with the recognized structural features demonstrated that abundance, density, and trend of such features strongly control the localization of Au mineralization. In addition, band ratio, principal component analysis, spectral angle mapper, and spectral feature fitting were applied to map and generate mineralization-related alteration layer. The evidence layers were then integrated through the conventional TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method to delineate reliable exploration targets.
