Identification of mineral resources in Afghanistan—Detecting and mapping resource anomalies in prioritized areas using geophysical and remote sensing (ASTER and HyMap) data

As part of the U.S. Geological Survey (USGS) and Department of Defense Task Force for Business and Stability Operations (TFBSO) natural resources revitalization activities in Afghanistan (Peters and others, 2011), three new datasets have been collected, compiled, and analyzed. These data have been used to more fully evaluate the areas of interest (AOIs; fig. 1 ) where, on the basis of previous U.S.S.R. and Afghanistan studies, the opportunity for early economic development of a number of different mineral, commodity, and deposit types had been identified (Peters and others, 2007; Peters and others, 2011). The new data compilations include (1) regional magnetic and gravity data for use in the characterization of subsurface composition and structure (Sweeney and others, 2006a,b; Ashan and others, 2007; Sweeney and others, 2007; Ashan and others, 2008; Shenwary and others, 2011), (2) Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data to identify and evaluate surficial alteration patterns related to industrial minerals and other selected targets, and (3) HyMap imaging spectrometer data for characterization and mapping of surficial mineralogy (Cocks and others, 1998; Kokaly and others, 2008; Peters and others, 2011). These datasets have served as fundamental building blocks for the resource evaluation by Peters and others (2011).

During the independent analysis of the geophysical, ASTER, and imaging spectrometer (HyMap) data by USGS scientists, previously unrecognized targets of potential mineralization were identified using evaluation criteria most suitable to the individual dataset. These anomalous zones offer targets of opportunity that warrant additional field verification. This report describes the standards used to define the anomalies, summarizes the results of the evaluations for each type of data, and discusses the importance and implications of regions of anomaly overlap between two or three of the datasets.