Articles | Volume 23, issue 6
Research article
13 Jun 2019
Research article |  | 13 Jun 2019

High-resolution paleovalley classification from airborne electromagnetic imaging and deep neural network training using digital elevation model data

Zhenjiao Jiang, Dirk Mallants, Luk Peeters, Lei Gao, Camilla Soerensen, and Gregoire Mariethoz

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Cited articles

Ahl, A.: Automatic 1D inversion of multifrequency airborne electromagnetic data with artificial neural networks: discussion and a case study, Geophys. Prospect., 51, 89–98, 2003. 
Amit, S. N. K. B., Shiraishi, S., Inoshita, T., and Aoki, Y.: Analysis of satellite images for disaster detection, Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International, 5189–5192, 2016. 
Archie, G. E.: The electrical resistivity log as an aid in determining some reservoir characteristics, T. AIME, 146, 54–62, 1942. 
Auken, E., Christiansen, A. V., Westergaard, J. H., Kirkegaard, C., Foged, N., and Viezzoli, A.: An integrated processing scheme for high-resolution airborne electromagnetic surveys, the SkyTEM system, Explor. Geophys., 40, 184–192, 2009. 
Auken, E., Christiansen, A. V., Kirkegaard, C., Fiandaca, G., Schamper, C., Behroozmand, A. A., Binley, A., Nielsen, E., Effersø, F., and Christensen, N. B.: An overview of a highly versatile forward and stable inverse algorithm for airborne, ground-based and borehole electromagnetic and electric data, Explor. Geophys., 46, 223–235, 2014. 
Short summary
Paleovalleys often form productive aquifers in the semiarid and arid areas. A methodology based on deep learning is introduced to automatically generate high-resolution 3-D paleovalley maps from low-resolution electrical conductivity data derived from airborne geophysical surveys. It is validated by borehole logs and the surface valley indices that the proposed method in this study provides an effective tool for regional-scale paleovalley mapping and groundwater exploration.