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In this study we used a towable time-domain tTEM system, to collect high-resolution transient electromagnetic data in the Euregio Meuse-Rhine region of the Netherlands. The data were processed and inverted to create a 3D resistivity model. Using few manual interpretation points as a-priori training data, a specialized artificial neuronal network interpreted the 3D resistivity model to produce a bedrock surface. High resolution geophysical surveys often contain large amounts of data. Manual interpretation of these datasets is often tedious and labour intensive. Using a specialized artificial neural network, trained on a-priori information, we were able to automatically interpret a bedrock surface at every model location in a time efficient manner.