ABSTRACT

New multispectral, hyperspectral and radar sensors provide an opportunity to improve integrated interpretation of geophysical and remote sensing data. A methodology has been developed to extract geological signatures from these data, and predict ore deposit targets using neural network simulation. It has been successfully tested on a number of deposit types.

Keywords:
escondida area, neural network analysis, analytic signal amplitude, upstream oil & gas, alteration zone, machine learning, geophysical data, calama region, metals & mining, artificial intelligence
Subjects:
Information Management and Systems, Neural networks
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2004. Society of Exploration Geophysicists
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