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  Applying machine learning on airborne geophysical models to map bedrock

  Large scale infrastructure projects are often plagued by significant cost overruns as a result of unknown ground conditions. These delays are often a result of the difficulties of obtaining accurate and seamless information of the subsurface geology. Airborne Electromagnetic (AEM) systems provide rapid acquisition of large amounts of geophysical data, penetrating several hundred meters into the subsurface, and particularly when integrating with geotechnical data through an artificial neural network (ANN) can significantly reduce uncertainty and aid in the planning of such projects. We show that from two case studies from Norway, airborne geoscanning can provide accurate bedrock models, classify hazardous sediments and delineate rock and sediment boundaries, resulting in significant cost and time savings of large-scale linear infrastructure projects.

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