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Abstract

Airborne EM provides quick and relatively cheap spatial coverage of resistivity distribution in 3 dimensions. After data processing and inversion, the main challenge is to extract relevant geological and geohydrological information from the resistivity distribution for further use in 3 dimensional modeling. Two case studies are described, the first one in which airborne EM was successfully integrated with available borehole data to create a 3 dimensional model of the distribution of clay and sand. In another study, Artificial Neural Networks were used to extract geological information from AEM resistivity Resistivity derived from AEM can be linked to geological features in a number of ways. Besides manual interpretation, statistical techniques are used, either in the form of regression or by means of Neural Networks, to extract geological and geohydrological meaningful interpretations from the resistivity model

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/content/papers/10.3997/2214-4609-pdb.383.AEM2013_DAY1_SESSION_2A_Gunnink
2013-10-10
2021-10-24
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609-pdb.383.AEM2013_DAY1_SESSION_2A_Gunnink
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