A helicopter electromagnetic (HEM) survey acquired at the U.S. Idaho National Engineering and Environmental Laboratory (INEEL) used a modification of a traditional mining airborne method flown at low levels for detailed characterization of shallow waste sites. The low sensor height, used to increase resolution, invalidates standard assumptions used in processing HEM data. Although the survey design strategy was sound, traditional interpretation techniques, routinely used in industry, proved ineffective. Processed data and apparent resistivity maps were severely distorted, and hence unusable, due to low flight height effects, high magnetic permeability of the basalt host, and the conductive, three-dimensional nature of the waste site targets.To accommodate these interpretation challenges, we modified a one-dimensional inversion routine to include a linear term in the objective function that allows for the magnetic and three-dimensional electromagnetic responses in the in-phase data. Although somewhat ad hoc, the use of this term in the inverse routine, referred to as the shift factor, was successful in defining the waste sites and reducing noise due to the low flight height and magnetic characteristics of the host rock. Many inversion scenarios were applied to the data and careful analysis was necessary to determine the parameters appropriate for interpretation, hence the approach was empirical. Data from three areas were processed with this scheme to highlight different interpretational aspects of the method. Wastes sites were delineated with the shift terms in two of the areas, allowing for separation of the anthropomorphic targets from the natural one-dimensional host. In the third area, the estimated resistivity and the shift factor were used for geological mapping. The high magnetic content of the native soil enabled the mapping of disturbed soil with the shift term. Published by Elsevier Science B.V.
Additional Publication Details
An empirical approach to inversion of an unconventional helicopter electromagnetic dataset