<div>Defining and characterising groundwater aquifers usually depends on the availability of data necessary to represent its spatial extent and hydrogeological properties, such as lithological information and aquifer pump test data.&nbsp;In regions where such data is of limited availability and/or variable quality, the characterisation of aquifers for the purposes of water resource assessment and management can be problematic.&nbsp;The Upper Darling River Floodplain region of western New South Wales, Australia, is an area where communities, natural ecosystems and cultural values are dependent on both surface and groundwater resources.&nbsp;Owing to a relative paucity of detailed geological and hydrogeological data across the region we apply two non-invasive geophysical techniques—airborne electromagnetics and surface magnetic resonance—to assist in mapping and characterising the regional alluvial aquifer system.&nbsp;The combination of these techniques in conjunction with limited groundwater quality data helps define an approximate extent for the low salinity alluvial aquifer in a key part of the Darling River valley system and provides insights into the relative water content and its variation within the aquifer materials.&nbsp;This work demonstrates the utility of these key geophysical data in developing a preliminary understanding of aquifer geometry and heterogeneity, thereby helping to prioritise targets for follow-up hydrogeological investigation. Presented at the 2024 Australian Society of Exploration Geophysicists (ASEG) Discover Symposium

<div>Surface magnetic resonance (SMR) techniques image subsurface water using the electromagnetic response of resonant hydrogen nuclei in water. Here we introduce the SMRPInv (Surface Magnetic Resonance Probabilistic Inversion) package, which couples a high-performance forward modeller for SMR data, and a Gaussian process based non-linear Bayesian inversion. Both the forward and inverse codes are part of the freely available, open source HiQGA (High Quality Geophysical Analysis) codebase written entirely in Julia. We summarise the relevant forward physics, the necessary data processing of free induction decay at an SMR sounding, followed by the estimation of subsurface water content with a non-linear parameterisation. Results are presented for synthetic inversions as well as field data from Western Davenport (Northern Territory). Comparisons are made against downhole logging data, together with results from a deterministic inversion of the same SMR soundings. Through this, we demonstrate that a probabilistic approach is key to conceptualising variability of subsurface water content.&nbsp;</div>