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Abstract. Paleovalleys are buried ancient river valleys that often form productive aquifers, especially in the semiarid 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 paleovalley 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, converted unimodal but skewed EC values into a high-resolution paleovalley index following a bimodal distribution. The latter allows us to distinguish valley from non-valley pixels. Furthermore, a realistic spatial connectivity structure of the paleovalley was predicted when compared with borehole lithology logs and a valley bottom flatness indicator. Overall the methodology permitted us to better constrain the three-dimensional paleovalley geometry from AEM images that are becoming more widely available for groundwater prospecting.

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The groundwater tracers, deuterium ( 2 H or D) and oxygen-18 ( 18 O) are widely used to determine groundwater recharge origin, rate and temperature. Quantitative methods able to interpret δD and δ 18 O mobility in the subsurface, especially under high pressure and temperature conditions, remain the focus of ongoing research.Here we developed an isotope transport module as an add-on to the TOUGH2 simulator, which is able to describe the evolution of δD and δ 18 O in groundwater induced by advection, dispersion, water-rock interaction, and fractionation due to changes of water phase and density. The new model was tested by modelling the groundwater flow, temperature, fluid density and isotopic composition in a faulted, geothermal system in the Guide Basin, China. We found that the density-driven flow induced by temperature variations can lead to isotope fractionation between low-3 ] Highlights