Preamble: The 'National Geochemical Survey of Australia: The Geochemical Atlas of Australia' was published in July 2011 along with a digital copy of the NGSA geochemical dataset (http://dx.doi.org/10.11636/Record.2011.020). The NGSA project is described here: www.ga.gov.au/ngsa. The present dataset contains additional geochemical data obtained on NGSA samples: the Lead Isotopes Dataset. Abstract: Over 1200 new lead (Pb) isotope analyses were obtained on catchment outlet sediment samples from the NGSA regolith archive to document the range and patterns of Pb isotope ratios in the surface regolith and their relationships to geology and anthropogenic activity. The selected samples included 1204 NGSA Top Outlet Sediment (TOS) samples taken from 0 to 10 cm depth and sieved to <2 mm (or ‘coarse’ fraction); three of these were analysed in duplicate for a total of 1207 Pb isotope analyses. Further, 12 Northern Australia Geochemical Survey (NAGS; http://dx.doi.org/10.11636/Record.2019.002) TOS samples from within a single NGSA catchment, also sieved to <2 mm, were analysed to provide an indication of smaller scale variability. Combined, we thus present 1219 new TOS coarse, internally comparable data points, which underpin new national regolith Pb isoscapes. Additionally, 16 NGSA Bottom Outlet Sediment (BOS; ~60 to 80 cm depth) samples, also sieved to <2 mm, and 16 NGSA TOS samples sieved to a finer grainsize (<75 um, or ‘fine’) fraction from selected NGSA catchments were also included to inform on Pb mobility and residence. Lead isotope analyses were performed by Candan Desem as part of her PhD research at the School of Geography, Earth and Atmospheric Sciences, University of Melbourne. After an initial ammonium acetate (AmAc) leach, the samples were digested in aqua regia (AR). Although a small number of samples were analysed after the AmAc leach, all samples were analysed after the second, AR digestion, preparation step. The analyses were performed without prior matrix removal using a Nu Instruments Attom single collector Sector Field-Inductively Coupled Plasma-Mass Spectrometer (SF-ICP-MS). The dried soil digests were redissolved in 2% HNO3 run solutions containing high-purity thallium (1 ppb Tl) and diluted to provide ~1 ppb Pb in solution. Admixture of natural, Pb-free Tl (with a nominal 205Tl/203Tl of 2.3871) allowed for correction of instrumental mass bias effects. Concentrations of matrix elements in the diluted AR digests are estimated to be in the range of 1–2 ppm. The SF-ICP-MS was operated in wet plasma mode using a Glass Expansion cyclonic spray chamber and glass nebuliser with an uptake rate of 0.33 mL/min. The instrument was tuned for maximum sensitivity and provided ~1 million counts per second/ppb Pb while maintaining flat-topped peaks. Each analysis, performed in the Attom’s ‘deflector peak jump’ mode, consists of 30 sets of 2000 sweeps of masses 202Hg, 203Tl, 204Pb, 205Tl, 206Pb, 207Pb and 208Pb, with dwell times of 500 μs and a total analysis time of 4.5 min. Each sample acquisition was preceded by a blank determination. All corrections for baseline, sample Hg interference (202Hg/204Pb ratios were always <0.043) and mass bias were performed online, producing typical in-run precisions (2 standard errors) of ±0.047 for 206Pb/204Pb, ±0.038 for 207Pb/204Pb, ±0.095 for 208Pb/204Pb, ±0.0012 for 207Pb/206Pb and ±0.0026 for 208Pb/206Pb. A small number of samples with low Pb concentrations exhibited very low signal sizes during analysis resulting in correspondingly high analytical uncertainties. Samples producing within-run uncertainties of >1% relative (measured on the 207Pb/204Pb ratio) were discarded as being insufficiently precise to contribute meaningfully to the dataset. Data quality was monitored using interspersed analyses of Tl-doped ~1 ppb solutions of the National Institute of Standards and Technology (NIST) SRM981 Pb standard, and several silicate reference materials: United States Geological Survey ‘BCR-2’ and ‘AGV-2’, Centre de Recherches Pétrographiques et Géochimiques ‘BR’ and Japan Geological Survey ‘JB-2’. In a typical session, up to 50 unknowns plus 15 standards were analysed using an ESI SC-2 DX autosampler. Although previous studies using the Attom SF-ICP-MS used sample-standard-bracketing techniques to correct for instrumental Pb mass bias, Tl doping was found to produce precise, accurate and reproducible results. Based upon the data for the BCR-2 and AGV-2 secondary reference materials, for which we have the most analyses, deviations from accepted values (accuracy) were typically <0.17%. Data for the remaining silicate standards appear slightly less accurate but these results may, to some extent, reflect uncertainty in the assigned literature values for these materials. Replicate runs of selected AR digests yielded similar reproducibility estimates. The results show a wide range of Pb isotope ratios in the NGSA (and NAGS) TOS <2 mm fraction samples across the continent (N = 1219): 206Pb/204Pb: Min = 15.558; Med ± Robust SD = 18.844 ± 0.454; Mean ± SD = 19.047 ± 1.073; Max = 30.635 207Pb/204Pb; Min = 14.358; Med ± Robust SD = 15.687 ± 0.091; Mean ± SD = 15.720 ± 0.221; Max = 18.012 208Pb/204Pb; Min = 33.558; Med ± Robust SD = 38.989 ± 0.586; Mean ± SD = 39.116 ± 1.094; Max = 48.873 207Pb/206Pb; Min = 0.5880; Med ± Robust SD = 0.8318 ± 0.0155; Mean ± SD = 0.8270 ± 0.0314; Max = 0.9847 208Pb/206Pb; Min = 1.4149; Med ± Robust SD = 2.0665 ± 0.0263; Mean ± SD = 2.0568 ± 0.0675; Max = 2.3002 These data allow the construction of the first continental-scale regolith Pb isotope maps (206Pb/204Pb, 207Pb/204Pb, 208Pb/204Pb, 207Pb/206Pb, and 208Pb/206Pb isoscapes) of Australia and can be used to understand contributions of Pb from underlying bedrock (including Pb-rich mineralisation), wind-blown dust and possibly from anthropogenic sources (industrial, transport, agriculture, residential, waste handling). The complete dataset is available to download as a comma separated values (CSV) file from Geoscience Australia's website (http://dx.doi.org/10.26186/5ea8f6fd3de64). Isoscape grids (inverse distance weighting interpolated grids with a power coefficient of 2 prepared in QGis using GDAL gridding tool based on nearest neighbours) are also provided for the five Pb isotope ratios (IDW2NN.TIF files in zipped folder). Alternatively, the new Pb isotope data can be downloaded from and viewed on the GA Portal (https://portal.ga.gov.au/).

<div>This contribution presents the distribution and geology of Australian alkaline and related rocks of Paleozoic age, one in a series within the Alkaline Rocks Atlas of Australia that collectively document alkaline rocks across the continent through time. </div><div><br></div><div>In general, alkaline and related rocks are a relatively rare class of igneous rocks worldwide. Alkaline rocks encompass a wide range of rock types and are mineralogically and geochemically diverse. They are typically thought to have been derived by generally small to very small degrees of partial melting of a wide range of mantle compositions. As such these rocks have the potential to convey considerable information on the evolution of the Earth’s mantle (asthenosphere and lithosphere), particularly the role of metasomatism, which may have been important in their generation, or to which such rocks may themselves have contributed. Such rocks, by their unique compositions and/or enrichments in their source protoliths, also have considerable metallogenic potential, e.g., diamonds, Th, U, Zr, Hf, Nb, Ta, REEs. It is evident that the geographic occurrences of many of these rock types are also important, and may relate to presence of old cratons, craton margins or major lithospheric breaks. Finally, many alkaline rocks also carry with them mantle xenoliths providing a snapshot of the lithospheric mantle composition at the time of their emplacement.</div><div><br></div><div>Accordingly, although alkaline and related rocks comprise only a volumetrically minor component of the geology of Australia, they are of considerable importance to studies of lithospheric composition, evolution and architecture and to helping constrain the temporal evolution of the lithosphere. They are also directly related to metallogenesis and mineralisation, particularly for a number of the critical minerals, e.g., rare earth elements, niobium. In light of this, Geoscience Australia is undertaking a compilation of the distribution and geology of Australian alkaline and related rocks, of all ages, and producing a GIS and associated database of such rocks, to both document such rocks and for use in metallogenic and mineral potential studies.&nbsp;</div><div><br></div><div>The broadening of the definition of alkaline rocks within the Alkaline Rocks Atlas herein, to include ultra-high K mafic to felsic silica-saturated rocks (alkaline-shoshonites), which are commonly formed at convergent margin settings, manifests in some divergences in the presentation of alkaline rocks that are particularly relevant to the Phanerozoic, and Paleozoic Australia in particular.&nbsp;</div><div><br></div><div>Paleozoic alkaline and related rocks occur throughout eastern Australia, with occurrences in the Northern Territory, and in all States excluding Western Australia. However, with a few exceptions they are principally located within the Tasman Element, and are over-represented in NSW – with respect to other states jurisdictions (based on available data). Paleozoic alkaline rocks range from ultramafic through to felsic, and from strongly alkaline (undersaturated) through to mildly alkaline.&nbsp;</div><div><br></div><div>Strongly alkaline rocks – congruent with the outline of alkaline rocks presented above – are comparatively rare in the Paleozoic, and are compositionally diverse incorporating alkali basalt, kimberlite, carbonatite-related rocks, and lamprophyre, with wide-ranging ages.&nbsp;</div><div><br></div><div>Overwhelmingly, the Paleozoic alkaline rock compilation is dominated by very high K alkali mafic to felsic silica-saturated rocks. Mafic-intermediate rocks within this grouping typically have an “arc signature” (i.e., low Nb/Y) but incorporate both arc magmas as well as rocks associated with backarc rifting. These rocks typically occur within rock units or packages that comprise a diverse array of rock types and compositions from volcanic rocks, related volcaniclastics and epiclastics through to sedimentary rocks. Igneous rocks within these packages commonly range from subalkaline / calc-alkaline through to mildly alkaline (trachybasalt to trachyandesite, and less commonly trachyte) based on alkali contents. Quartz-saturated felsic alkaline rocks are dominated by near peralkaline–peralkaline A-types and high-temperature transitional I-A compositions, but locally include rarer mildly alkaline (based on HFSE) rocks. The inclusion of whole rock units, which may only incorporate a small volume of alkaline rocks, necessarily means that the volume of these alkaline rocks is both poorly constrained and over-represented with this dataset.</div><div><br></div>

<div>A national compilation of airborne electromagnetic (AEM) conductivity–depth models from AusAEM (Ley-Cooper et al. 2020) survey line data and other surveys (see reference list in the attachments) has been used to train a conductivity model prediction for the 0-4 m and 30 m depth intervals. Over 460,000 training points/measurements were used in a 5 K-Fold training and validation split. A further 28,626 points/measurements were used to assess the out of sample performance (OOS; i.e. points not used in the model validation). Modelling of the conductivity values (i.e. measurements along the AEM survey lines) was performed using the gradient boosted (GB) tree algorithm. The GB model is a machine learning (ML) ensemble technique used for both regression and classification tasks (https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html). Samples along the flight-line were thinned to approximately one sample per 300 m. This avoided the situation where we could have more than one sample per pixel (i.e. features or covariates used in the model prediction have a cell or pixel size of 80 m) that could otherwise lead to over fitting. In addition, out of sample set used label clusters or groups to minimise overfitting. Here we use the median of the models as the conductivity prediction and the upper and lower percentiles (95th and 5th respectively) to measure the model uncertainty. Grids show conductivity (S/m) in log 10 units. The methodology used to generate these conductivity grids are overall similar to that described by Wilford, et al. 2022.</div><div>&nbsp;</div><div>Reported out-of-sample r-squares for the 0-4 m and 3 m depths are 0.76 and 0.74, respectively. The ML approach allows estimation of conductivity into areas where we do not have airborne electromagnetic survey coverage. Hence these model have a national extent. Where we do not have AEM survey coverage the model is finding relationships with the covariates and making informed estimates of conductivity in those areas. Where those relationships are not well understood (i.e. where we see a departure in the feature space characteristics from what the model can ‘see’) the model prediction is likely to be less certain. Differences in the features and their corresponding values ‘seen’ and used in the model versus the full feature space covering the entire continent are captured in the covariate shift map. High values in the shift model can indicate higher potential uncertainty or unreliability of the model prediction. Users therefore need to be mindful when interpreting this dataset, of the uncertainties shown by the 5th-95th percentiles, and high values in the covariate shift map.</div><div>&nbsp;</div><div>Datasets in this data package include:</div><div>&nbsp;</div><div>1. 0_4m_conductivity_prediction_median.tif</div><div>2. 0_4m_conductivity_lower_percentile_5th.tif</div><div>3. 0_4m_conductivity_upper_percentile_95th.tif</div><div>4. 30m_conductivity_prediction_median.tif</div><div>5.30m_conductivity_lower_percentile_5th.tif</div><div>6. 30m_conductivity_upper_percentile_95th.tif</div><div>7. National_conductivity_model_shift.tif</div><div>8. Full list of referenced AEM survey datasets used to train the model (word document)</div><div>9. Map showing the distribution of training and out-of-sample sites</div><div><br></div><div>All the Geotiffs (1-6) are in log (10) electrical conductivity siemens per metre (S/m).</div><div>&nbsp;</div><div>This work is part of Geoscience Australia’s Exploring for the Future program which provides precompetitive information to inform decision-making by government, community and industry on the sustainable development of Australia's mineral, energy and groundwater resources. By gathering, analysing and interpreting new and existing precompetitive geoscience data and knowledge, we are building a national picture of Australia’s geology and resource potential. This leads to a strong economy, resilient society and sustainable environment for the benefit of all Australians. This includes supporting Australia’s transition to net zero emissions, strong, sustainable resources and agriculture sectors, and economic opportunities and social benefits for Australia’s regional and remote communities. The Exploring for the Future program, which commenced in 2016, is an eight year, $225m investment by the Australian Government.</div><div><br></div><div><br></div><div><strong>Reference:</strong></div><div><br></div><div>Ley-Cooper, A. Y., Brodie, R.C., and Richardson, M. 2020. AusAEM: Australia’s airborne electromagnetic continental-scale acquisition program, Exploration Geophysics, 51:1, 193-202, DOI: 10.1080/08123985.2019.1694393</div><div><br></div><div>Wilford, J., LeyCooper, Y., Basak, S., Czarnota, K. 2022. High resolution conductivity mapping using regional AEM survey and machine learning. Geoscience Australia, Canberra. https://dx.doi.org/10.26186/146380</div>

<div>The lithology, geochemistry, and architecture of the continental lithospheric mantle (CLM) underlying the Kimberley Craton of north-western Australia has been constrained using pressure-temperature estimates and mineral compositions for &gt;5,000 newly analyzed and published garnet and chrome (Cr) diopside mantle xenocrysts from 25 kimberlites and lamproites of Mesoproterozoic to Miocene age. Single-grain Cr diopside paleogeotherms define lithospheric thicknesses of 200–250 km and fall along conductive geotherms corresponding to a surface heat flow of 37–40 mW/m 2. Similar geotherms derived from Miocene and Mesoproterozoic intrusions indicate that the lithospheric architecture and thermal state of the CLM has remained stable since at least 1,000 Ma. The chemistry of xenocrysts defines a layered lithosphere with lithological and geochemical domains in the shallow (&lt;100 km) and deep (&gt;150 km) CLM, separated by a diopside-depleted and seismically slow mid-lithosphere discontinuity (100–150 km). The shallow CLM is comprised of Cr diopsides derived from depleted garnet-poor and spinel-bearing lherzolite that has been weakly metasomatized. This layer may represent an early (Meso to Neoarchean?) nucleus of the craton. The deep CLM is comprised of high Cr2O3 garnet lherzolite with lesser harzburgite, and eclogite. The peridotite components are inferred to have formed as residues of polybaric partial mantle melting in the Archean, whereas eclogite likely represents former oceanic crust accreted during Paleoproterozoic subduction. This deep CLM was metasomatized by H2O-rich melts derived from subducted sediments and high-temperature FeO-TiO2 melts from the asthenosphere.</div><div><br></div><div>Geoscience Australia’s Exploring for the Future program provides precompetitive information to inform decision-making by government, community and industry on the sustainable development of Australia's mineral, energy and groundwater resources. By gathering, analysing and interpreting new and existing precompetitive geoscience data and knowledge, we are building a national picture of Australia’s geology and resource potential. This leads to a strong economy, resilient society and sustainable environment for the benefit of all Australians. This includes supporting Australia’s transition to net zero emissions, strong, sustainable resources and agriculture sectors, and economic opportunities and social benefits for Australia’s regional and remote communities. The Exploring for the Future program, which commenced in 2016, is an eight year, $225m investment by the Australian Government.</div><div><br></div><div><strong>Citation:</strong></div><div>Sudholz, Z.J., et al. (2023) Mapping the Structure and Metasomatic Enrichment of the Lithospheric Mantle Beneath the Kimberley Craton, Western Australia,&nbsp;<em><i>Geochemistry, Geophysics, Geosystems</i>,</em>&nbsp;24, e2023GC011040.</div><div>https://doi.org/10.1029/2023GC011040</div>

<div>Airborne electromagnetics surveys are at the forefront of addressing the challenge of exploration undercover. They have been essential in the regional mapping programmes to build Australia's resource potential inventory and provide information about the subsurface. In collaboration with state and territory geological surveys, Geoscience Australia (GA) leads a national initiative to acquire AEM data across Australia at 20 km line spacing, as a component of the Australian government Exploring for The Future (EFTF) program. Regional models of subsurface electrical conductivity show new undercover geological features that could host critical mineral deposits and groundwater resources. The models enable us to map potential alteration and structural zones and support environmental and land management studies. Several features observed in the AEM models have also provided insights into possible salt distribution analysed for its hydrogen storage potential. The AusAEM programme is rapidly covering areas with regional AEM transects at a scale never previously attempted. The programme's success leans on the high-resolution, non-invasive nature of the method and its ability to derive subsurface electrical conductivity in three dimensions – made possible by GA's implementation of modern high-performance computing algorithms. The programme is increasingly acquiring more AEM data, processing it, and working towards full national coverage.</div> This Abstract was submitted/presented to the 2023 Australian Exploration Geoscience Conference 13-18 Mar (https://2023.aegc.com.au/)

Improvements in discovery and management of minerals, energy and groundwater resources are spurred along by advancements in surface and subsurface imaging of the Earth. Over the last half decade Australia has led the world in the collection of regionally extensive airborne electromagnetic (AEM) data coverage, which provides new constraints on subsurface conductivity structure. Inferring geology and hydrology from conductivity is non-trivial as the conductivity response of earth materials is non-unique, but careful calibration and interpretation does provide significant insights into the subsurface. To date utility of this new data is limited by its spatial extent. The AusAEM survey provides conductivity constraints every 12.5 m along flight lines with no constraints across vast areas between flight lines spaced 20 km apart. Here we provide a means to infer the conductivity between flight lines as an interim measure before infill surveys can be undertaken. We use a gradient boosted tree machine learning algorithm to discover relationships between AEM conductivity models across northern Australia and other national data coverages for three depth ranges: 0–0.5 m, 9–11 m and 22–27 m. The predictive power of our models decreases with depth but they are nevertheless consistent with our knowledge of geological, landscape evolution and climatic processes and an improvement on standard interpolation methods such as kriging. Our models provide a novel complementary methodology to gridding/interpolating from AEM conductivity alone for use by the mining, energy and natural resource management sectors. <b>Citation: </b>Wilford J., Ley-Cooper Y., Basak S., & Czarnota K., 2022. High resolution conductivity mapping using regional AEM survey and machine learning. In: Czarnota, K. (ed.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, https://dx.doi.org/10.26186/146380.

<div>This data package contains interpretations of airborne electromagnetic (AEM) conductivity sections in the Exploring for the Future (EFTF) program’s Eastern Resources Corridor (ERC) study area, in south eastern Australia. Conductivity sections from 3 AEM surveys were interpreted to provide a continuous interpretation across the study area – the EFTF AusAEM ERC (Ley-Cooper, 2021), the Frome Embayment TEMPEST (Costelloe et al., 2012) and the MinEx CRC Mundi (Brodie, 2021) AEM surveys. Selected lines from the Frome Embayment TEMPEST and MinEx CRC Mundi surveys were chosen for interpretation to align with the 20&nbsp;km line-spaced EFTF AusAEM ERC survey (Figure 1).</div><div>The aim of this study was to interpret the AEM conductivity sections to develop a regional understanding of the near-surface stratigraphy and structural architecture. To ensure that the interpretations took into account the local geological features, the AEM conductivity sections were integrated and interpreted with other geological and geophysical datasets, such as boreholes, potential fields, surface and basement geology maps, and seismic interpretations. This approach provides a near-surface fundamental regional geological framework to support more detailed investigations. </div><div>This study interpreted between the ground surface and 500&nbsp;m depth along almost 30,000 line kilometres of nominally 20&nbsp;km line-spaced AEM conductivity sections, across an area of approximately 550,000&nbsp;km2. These interpretations delineate the geo-electrical features that correspond to major chronostratigraphic boundaries, and capture detailed stratigraphic information associated with these boundaries. These interpretations produced approximately 170,000 depth estimate points or approximately 9,100 3D line segments, each attributed with high-quality geometric, stratigraphic, and ancillary data. The depth estimate points are formatted for compliance with Geoscience Australia’s (GA) Estimates of Geological and Geophysical Surfaces (EGGS) database, the national repository for standardised depth estimate points. </div><div>Results from these interpretations provided support to stratigraphic drillhole targeting, as part of the Delamerian Margins NSW National Drilling Initiative campaign, a collaboration between GA’s EFTF program, the MinEx CRC National Drilling Initiative and the Geological Survey of New South Wales. The interpretations have applications in a wide range of disciplines, such as mineral, energy and groundwater resource exploration, environmental management, subsurface mapping, tectonic evolution studies, and cover thickness, prospectivity, and economic modelling. It is anticipated that these interpretations will benefit government, industry and academia with interest in the geology of the ERC region.</div>

<div>Lithospheric structure and composition have direct relevance for our understanding of mineral prospectivity. Aspects of the lithosphere can be imaged using geophysical inversion or analysed from exhumed samples at the surface of the Earth, but it is a challenge to ensure consistency between competing models and datasets. The LitMod platform provides a probabilistic inversion framework that uses geology as the fabric to unify multiple geophysical techniques and incorporates a priori geochemical information. Here, we present results from the application of LitMod to the Australian continent. The rasters summarise the results and performance of a Markov-chain Monte Carlo sampling from the posterior model space. Release FR23 is developed using primary-mode Rayleigh phase velocity grids adapted from Fishwick & Rawlinson (2012).</div><div><br></div><div>Geoscience Australia's Exploring for the Future program provides precompetitive information to inform decision-making by government, community and industry on the sustainable development of Australia's mineral, energy and groundwater resources. By gathering, analysing and interpreting new and existing precompetitive geoscience data and knowledge, we are building a national picture of Australia's geology and resource potential. This leads to a strong economy, resilient society and sustainable environment for the benefit of all Australians. This includes supporting Australia's transition to a low emissions economy, strong resources and agriculture sectors, and economic opportunities and social benefits for Australia's regional and remote communities. The Exploring for the Future program, which commenced in 2016, is an eight year, $225m investment by the Australian Government.</div>

<div>The Proterozoic alkaline and related igneous rocks of Australia is a surface geology compilation of alkaline and related igneous rocks of Proterozoic age in Australia. This dataset is one of five datasets, with compilations for Archean, Paleozoic, Mesozoic and Cenozoic alkaline and related igneous rocks already released.</div><div><br></div><div>Geological units are represented as polygon and point geometries and, are attributed with information that includes, but is not limited to, stratigraphic nomenclature and hierarchy, age, lithology, composition, proportion of alkaline rocks, body morphology, unit expression, emplacement type, presence of mantle xenoliths and diamonds, and primary data source. Source data for the geological unit polygons provided in Data Quality LINEAGE. Geological units are grouped into informal geographic “alkaline provinces”, which are represented as polygon geometries, and attributed with information similar to that provided for the geological units.</div>

The High Quality Geophysical Analysis (HiQGA) package is a fully-featured, Julia-language based open source framework for geophysical forward modelling, Bayesian inference, and deterministic imaging. A primary focus of the code is production inversion of airborne electromagnetic (AEM) data from a variety of acquisition systems. Adding custom AEM systems is simple using Julia’s multiple dispatch feature. For probabilistic spatial inference from geophysical data, only a misfit function needs to be supplied to the inference engine. For deterministic inversion, a linearisation of the forward operator (i.e., Jacobian) is also required. HiQGA is natively parallel, and inversions from a full day of production AEM acquisition can be inverted on thousands of CPUs within a few hours. This allows for quick assessment of the quality of the acquisition, and provides geological interpreters preliminary subsurface images of EM conductivity together with associated uncertainties. HiQGA inference is generic by design – allowing for the analysis of diverse geophysical data. Surface magnetic resonance (SMR) geophysics for subsurface water-content estimation is available as a HiQGA plugin through the SMRPInversion (SMR probabilistic inversion) wrapper. The results from AEM and/or SMR inversions are used to create images of the subsurface, which lead to the creation of geological models for a range of applications. These applications range from natural resource exploration to its management and conservation.