In spite of the dominance of traditional mineral exploration methods that demand physical characterization of rocks and intense field work, remote sensing technologies have also evolved in the recent past to facilitate mineral exploration. In the present study, we have processed visible near infrared (VNIR) and shortwave infrared (SWIR) bands of Advanced space-borne thermal emission and reflection radiometer (ASTER) data to detect surface mineralization signatures in Mundiyawas - Khera area in Alwar basin, north-eastern Rajasthan, India using spectral angle mapper (SAM). The potential of SAM method to detect target under variable illumination condition was used to delineate galena, chalcopyrite, malachite etc. as surface signatures of mineralization. It was ensured that the identified surface anomalies were spectrally pure using pixel purity index. Spectral anomalies were validated in the field and also using X-Ray diffraction data. Spectral anomaly maps thus derived were integrated using weight of evidence method with the lineament density, geochemical anomaly, bouger anomaly maps to identify few additional potential areas of mineralization. This study thus establishes the importance of remote sensing in mineral exploration to zero in on potentially ore rich but unexplored zones.

尽管传统的矿物勘探方法占主导地位，需要对岩石进行物理表征和进行大量野外工作，但最近遥感技术也在不断发展，以促进矿物勘探。在本研究中，我们已经处理了先进星载热发射和反射辐射计（ASTER）数据的可见近红外（VNIR）和短波红外（SWIR）波段，以检测北部阿尔瓦盆地Mundiyawas-Khera地区的表面矿化特征。印度东部的拉贾斯坦邦，使用光谱角度映射器（SAM）。SAM方法在可变光照条件下检测目标的潜力被用来描绘方铅矿，黄铜矿，孔雀石等作为矿化的表面特征。使用像素纯度指数确保所识别的表面异常在光谱上是纯净的。在现场以及使用X射线衍射数据验证了光谱异常。使用证据权重方法将由此得出的光谱异常图与线粒体密度，地球化学异常，bouger异常图整合在一起，以识别出很少的其他潜在矿化区域。因此，这项研究确立了在矿产勘探中将潜在矿藏丰富但尚未勘探的区域归零的重要性。