As a standard procedure, multi-frequency helicopter-borne electromagnetic (HEM) data are inverted to conductivity–depth models using 1-D inversion methods, which may, however, fail in areas of strong lateral conductivity contrasts (so-called induction anomalies). Such areas require more realistic multi-dimensional modelling. Since the full 3-D inversion of an entire HEM data set is still extremely time consuming, our idea is to combine fast 1-D and accurate but numerically expensive 3-D inversion of HEM data in such a way that the full 3-D inversion is only carried out for those parts of a HEM survey which are affected by induction anomalies. For all other parts, a 1-D inversion method is sufficient. We present a newly developed algorithm for identification, selection, and extraction of induction anomalies in HEM data sets and show how the 3-D inversion model of the anomalous area is re-integrated into the quasi-1-D background. Our proposed method is demonstrated to work properly on a synthetic and a field HEM data set from the Cuxhaven tunnel valley in Germany. We show that our 1-D/3-D approach yields better results compared to 1-D inversions in areas where 3-D effects occur.
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