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Application of natural field airborne electromagnetics in conductive geo-environments

Authors: Prikhodko, Alexander; Aamna Sirohey; Carpenter, Andrew; Bagrianski, Andrei;

Application of natural field airborne electromagnetics in conductive geo-environments

Abstract

The presence of conductive overburden and conductive host rocks impose significant limitations on the application of airborne electromagnetic (EM) methods which make use of controlled source transmitters (active source systems) for subsurface exploration. The limitations in applicability arise mainly due to limited depth of penetration because of the screening effect of conductive materials coupled with limitations in the energy transmitted by systems with controlled primary field sources. Airborne EM methods exploiting natural fields (passive field systems) push the boundaries of these limitations affecting active source systems to a great degree by utilizing the naturally occurring EM waves resulting from thunderstorms, on a global scale, and activity in the ionosphere as the primary field sources. The naturally excited fields occur over a broad frequency range and energize the geological environment over a wide depth range, which in all cases exceeds the depth of investigation (DOI) of existing active source systems. The lowest frequency of current airborne natural field EM technologies is approximately 20-25 Hz, which provides the greatest depth of detection by these systems. Based on the results of theoretical modeling, including an example that considers relatively conductive targets located in conductive host rock as well as beneath conductive overburden, and a field case study in a conductive environment, the capabilities of the MobileMT technology, based on the natural field EM principle, are demonstrated.

Open-Access Online Publication: May 29, 2023

Keywords

mineral exploration, electromagnetics, MobileMT, airborne geophysics

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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