Automated interpretation of airborne electromagnetic data

Geophysics ◽  
1984 ◽  
Vol 49 (8) ◽  
pp. 1301-1312 ◽  
Author(s):  
G. T. DeMoully ◽  
A. Becker

Recent improvements in equipment quality make it possible to increase the usefulness of airborne electromagnetic (EM) systems in areas of moderate electrical conductivity for the purpose of constructing simple electrical property maps which can be related to surficial geology. This application of airborne electromagnetics may be demonstrated and evaluated using Barringer/Questor Mark VI Input® survey results in places where independent verifications of the airborne data interpretation are available. For this purpose we have developed a set of computer algorithms which read digitally recorded Input data and interpret them automatically in terms of a simple electrical section that is defined by a single conductive layer whose thickness, conductivity, and subsurface depth are determined from the data. Because this technique is formally based on a one‐dimensional, three‐layer, three‐parameter, horizontally stratified earth model, it is only applicable in regions where the surficial formations are mildly dipping and the conductive layer is covered by, and rests on, highly resistive materials. The interpretation method is illustrated by three field examples. At the first field survey site, in Alberta, Canada, airborne EM survey data are used to map the depth of the interface between coarse and clayey sands. Data from a second survey site, this time in the Western USA, are interpreted to yield the section of a subsurface valley filled with conductive clay. The final example, taken from British Columbia, Canada, involves the mapping of all the three parameters for a weathered volcanic unit.

Geophysics ◽  
2002 ◽  
Vol 67 (2) ◽  
pp. 492-500 ◽  
Author(s):  
James E. Reid ◽  
James C. Macnae

When a confined conductive target embedded in a conductive host is energized by an electromagnetic (EM) source, current flow in the target comes from both direct induction of vortex currents and current channeling. At the resistive limit, a modified magnetometric resistivity integral equation method can be used to rapidly model the current channeling component of the response of a thin-plate target energized by an airborne EM transmitter. For towed-bird transmitter–receiver geometries, the airborne EM anomalies of near-surface, weakly conductive features of large strike extent may be almost entirely attributable to current channeling. However, many targets in contact with a conductive host respond both inductively and galvanically to an airborne EM system. In such cases, the total resistive-limit response of the target is complicated and is not the superposition of the purely inductive and purely galvanic resistive-limit profiles. Numerical model experiments demonstrate that while current channeling increases the width of the resistive-limit airborne EM anomaly of a wide horizontal plate target, it does not necessarily increase the peak anomaly amplitude.


2020 ◽  
Vol 224 (1) ◽  
pp. 543-557
Author(s):  
Thomas M Hansen

SUMMARY Probabilistic inversion methods, typically based on Markov chain Monte Carlo, exist that allow exploring the full uncertainty of geophysical inverse problems. The use of such methods is though limited by significant computational demands, and non-trivial analysis of the obtained set of dependent models. Here, a novel approach, for sampling the posterior distribution is suggested based on using pre-calculated lookup tables with the extended rejection sampler. The method is (1) fast, (2) generates independent realizations of the posterior, and (3) does not get stuck in local minima. It can be applied to any inverse problem (and sample an approximate posterior distribution) but is most promising applied to problems with informed prior information and/or localized inverse problems. The method is tested on the inversion of airborne electromagnetic data and shows an increase in the computational efficiency of many orders of magnitude as compared to using the extended Metropolis algorithm.


Geophysics ◽  
2015 ◽  
Vol 80 (6) ◽  
pp. K25-K36 ◽  
Author(s):  
Michael S. McMillan ◽  
Christoph Schwarzbach ◽  
Eldad Haber ◽  
Douglas W. Oldenburg

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