Multivariate statistical pattern recognition techniques have been widely used in the analysis of multispectral scanner remote sensing data for crop surveys, forest mapping, land use surveys and in many other applications. These applications are restricted basically to surface cover reflectance and emissivity phenomena. In the study described in this paper multivariate analysis techniques were applied to geophysical remote sensing data which measures phenomena occurring beneath the surface of the earth. Three types of geophysical data: magnetic anomaly, induced pulse transient, and gamma ray data were digitized, registered and analyzed to observe relationships to known geology. In addition several types of surficial remote sensing data including LANDSAT multispectral scanner, side looking airborne radar (SLAR) and thermal infrared scanner data were included in the multivariate data set to enable evaluation of all the available remote sensing variables. The preprocessing and analysis techniques are discussed and results showing correlations between variables and relationships to geology is presented.

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