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| Titel |
Toward a rapid 3D spectral deconvolution of EMI conductivities measured with portable multi-configuration sensors |
| VerfasserIn |
Julien Guillemoteau, Jens Tronicke |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250143328
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| Publikation (Nr.) |
 EGU/EGU2017-7036.pdf |
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| Zusammenfassung |
| Portable loop-loop electromagnetic induction (EMI) sensors using multiple coil
configurations are of growing interest in hydrological, archaeological and agricultural studies
for mapping the subsurface electrical conductivity. In contrast with EMI methods
employing larger scale geometries (e.g., magnetotellurics, marine EM, airborne
EM, transient EM, large offset loop-loop harmonic source EM), the portable EMI
multi-configuration sensors operate in the low induction number (LIN) domain as they
employ a rather low frequency harmonic source (< 20 kHz) and rather small coil
separations (≤ 2 m). In the LIN domain, electrical conductivity has a minor effect
on the forward modelling kernel. Accordingly, we have developed an algorithm
to model this kind of data, which is based on a homogeneous half-space kernel.
By formulating the problem in the hybrid spectral-spatial domain (kx, ky, z), we
show that it is possible to generate large data maps containing more than 100,000
stations within a minute on a standard modern laptop computer. We compared this
forward modelling approach to a robust approach based on the integral equation (IE)
method. Our results show that, as long as the LIN approximation is fulfilled (i.e., for
the system of interest, if the electrical conductivity is smaller than 0.5 S/m), the
linear theory allows to accurately and robustly handle the structural characteristics
of the subsurface conductivity distribution. We therefore expect that our forward
modelling procedure can be implemented in rapid multi-channel deconvolution
procedures in order to rapidly extract the structural properties of the subsurface
conductivity distribution from data sets acquired across rather large (hectare scale) areas. |
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