9ba57037-0bf8-4da3-90d9-d13ebe0d3a7d urn:uuid eng UTF-8 nonGeographicDataset nonGeographicDataset 2025-10-17T01:15:58 creation ISO 19115 2016 An open source modelling and Bayesian inversion package for surface magnetic resonance data 9ba57037-0bf8-4da3-90d9-d13ebe0d3a7d 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.  {"type": "Polygon", "coordinates": [[[112.0, -44.0], [154.0, -44.0], [154.0, -9.0], [112.0, -9.0], [112.0, -44.0]]]} 112.0 154.0 -44.0 -9.0 EFTF – Exploring for the Future Electrical and Electromagnetic Methods in Geophysics bayesian electromagnetic modeling surface magnetic resonance Link to Australasian Groundwater Conference 2022 documentDigital Link to Australasian Groundwater Conference 2022 HTML