f Automater airborne EM anomaly picking and interpretation

Airborne electromagnetic (AEM) systems can collect very significant quantities of data on a daily basis. Traditional methods of data interpretation for urineral prospecting involve the visual scanning of 20 or more wiggle traces on rolls of paper, looking for local "anomalies" , from each of which a few characteristic parameters are estimated such as amplitudes on a few channels, anomaly width and shape. These extracted parameters are then compiled onto a map for subsequent ground follow-up. In conductive areas alternative presentations such as contours of individual channels or channel ratios are commonly attempted. Only the "best" anomalies are usually looked at in detail or quantitatively interpreted, and experience has shown that economically significant responses may at times be misinterpreted or even completely missed, patticularly if the source is deep and its anomaly small and broad .