High-resolution palaeovalley classification from airborne
electromagnetic imaging and deep neural network training using
digital elevation model data
Abstract. Palaeovalleys are buried ancient river valleys that often form productive aquifers, especially in the semi-arid and arid areas of Australia. Delineating their extent and hydrostratigraphy is however a challenging task in groundwater system characterization. This study developed a methodology based on the deep learning super-resolution convolutional neural network (SRCNN) approach, to convert electrical conductivity (EC) estimates from an airborne electromagnetic (AEM) survey in South Australia to a high-resolution binary palaeovalley map. The SRCNN was trained and tested with a synthetic training dataset, where valleys were generated from readily available digital elevation model (DEM) data from the AEM survey area. Electrical conductivities typical of valley sediments were generated by Archie’s Law, and subsequently blurred by down-sampling and bicubic interpolation to represent noise from the AEM survey, inversion and interpolation. After a model training step, the SRCNN successfully removed such noise, and reclassified the low-resolution, unimodal but skewed EC values into a high-resolution palaeovalley index following a bimodal distribution. The latter allows distinguishing valley from non-valley pixels. Furthermore, a realistic spatial connectivity structure of the palaeovalley was predicted when compared with borehole lithology logs and valley bottom flatness indicator. Overall the methodology permitted to better constrain the three-dimensional palaeovalley geometry from AEM images that are becoming more widely available for groundwater prospecting.