Helicopter-borne electromagnetic (HEM) surveys are an effective tool for investigating the spatial conductivity distribution in the subsurface, e.g. for groundwater or mineral exploration. As a standard procedure, the multi-frequency HEM data are inverted to resistivity-depth models using a 1-D inversion method. Since the footprint of the HEM system is rather small and smooth conductivity structures are close to 1-D settings this is a valid approach. However, conductivity structures with strong lateral variations (anomalies) are not reproducible by 1-D inversion and a multi-dimensional inversion is required. Our aim is to combine 1-D and 3-D inversion of HEM data. A 3-D inversion is only carried out for those parts of a HEM survey which are affected by an anomaly. For all other parts a 1-D inversion method is used. Thus, the knowledge where such anomalies occur in a HEM data set is crucial. We present a new method for identification, selection, and extraction of anomalies in HEM data sets. The identified anomalies are handed over to the 3-D inversion. The resulting 3-D inversion models are integrated in the quasi 1-D background. The technique is demonstrated on a synthetic HEM data set.


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