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| Titel |
Combining ground-based and airborne EM through Artificial Neural Networks for modelling glacial till under saline groundwater conditions |
| VerfasserIn |
J. L. Gunnink, J. H. A. Bosch, B. Siemon, B. Roth, E. Auken |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 16, no. 8 ; Nr. 16, no. 8 (2012-08-29), S.3061-3074 |
| Datensatznummer |
250013446
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| Publikation (Nr.) |
 copernicus.org/hess-16-3061-2012.pdf |
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| Zusammenfassung |
| Airborne electromagnetic (AEM) methods supply data over large areas in a
cost-effective way. We used Artificial Neural Networks (ANN) to classify the
geophysical signal into a meaningful geological parameter. By using examples
of known relations between ground-based geophysical data (in this case
electrical conductivity, EC, from electrical cone penetration tests) and
geological parameters (presence of glacial till), we extracted learning
rules that could be applied to map the presence of a glacial till using the
EC profiles from the airborne EM data. The saline groundwater in the area
was obscuring the EC signal from the till but by using ANN we were able to
extract subtle and often non-linear, relations in EC that were
representative of the presence of the till. The ANN results were interpreted
as the probability of having till and showed a good agreement with drilling
data. The glacial till is acting as a layer that inhibits groundwater flow,
due to its high clay-content, and is therefore an important layer in
hydrogeological modelling and for predicting the effects of climate change
on groundwater quantity and quality. |
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