A review article on airborne geoscanning in Norway

This paper investigate the current state of airborne electromagnetic surveys in Norway for geological and geotechnical investigations.



A review article on airborne geoscanning in Norway

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From the first use of airborne electromagnetic (AEM) systems for remote sensing in the 1950s, AEM data acquisition, processing and inversion technology have rapidly developed. Once used extensively for mineral exploration in its early days, the technology is increasingly being applied in other industries alongside ground-based investigation techniques. This paper reviews the application of onshore AEM in Norway over the past decades.

Norway’s rugged terrain and complex post-glacial sedimentary geology have contributed to the later adoption of AEM for widespread mapping compared to neighbouring Nordic countries. We illustrate AEM’s utility by using two detailed case studies, including time-domain and frequency domain AEM. In both cases, we combine AEM with other geophysical, geological and geotechnical drillings to enhance interpretation, including machine learning methods. The end results included bedrock surfaces predicted with an accuracy of 25% of depth, identification of hazardous quick clay deposits, and sedimentary basin mapping. These case studies illustrate that although today’s AEM systems do not have the resolution required for late-phase, detailed engineering design, AEM is a valuable tool for early-phase site investigations. Intrusive, ground-based methods are slower and more expensive, but when they are used to complement the weaknesses of AEM data, site investigations can become more efficient. With new developments of drone-borne (UAV) systems and increasing investment in AEM surveys, we see the potential for continued global adoption of this technology.

Helicopter and fixed-wing AEM coverage undertaken by NGU, NGI and EMerald Geomodelling in Norway. Background map from Kartverket.


With the aid of two case studies, we have demonstrated the applications and benefits of using AEM data in subsurface investigations. AEM provides a rapid acquisition method to cover significantly larger areas than common ground-based surveying methods in a short amount of time. Their used cases are broad and add significant value from applications such as hazard mapping, engineering projects, resource discovery or simply improved geological mapping from the surface to several hundred meters below.

AEM data alone can provide detailed answers of large-scale subsurface features and even lithological properties, though the addition of external data such as geological mapping and geotechnical soundings will provide greater subsurface information when combined with AEM data. The technological advancements in AEM hardware and development of machine learning algorithms have allowed AEM data to be used for accurate tools such as bedrock prediction and hazard (quick clay) predictions.

In Norway, AEM coverage is perhaps more sparse than other Scandinavian countries such as Denmark, though this is assumed to be a result of geographic conditions. These geographic conditions have influenced its use within Norwegian industry and the methodological choices of system, survey design, and data processing. These lessons may help other countries with similarly rugged terrain or complex post-glacial geology.

With the benefits AEM surveys provide and the increasing use of drones and continuous technological development, we expect the use of AEM methods for ground investigations to remain a key tool for remote sensing in both Norway and the world in the coming years.


We want to thank the Norwegian Public Roads Administration and all the project partners of Ramså Basin project for allowing us to use the data in this study.


The full paper can be requested through the download link above or found directly at www.mdpi.com.

Harrison, E. J.; Baranwal,V. C.; Pfaffhuber, A. A.; Christensen, C. W.; Skurdal, G. H.; Rønning, J. S.; Anschütz, H.; Brönner, M. AEM in Norway: A Review of the Coverage, Applications and the State of Technology. Remote Sensing, 2021, 13, 4687.

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