Compositional data from a soil survey over north Canberra, Australian Capital Territory, are used to develop and test an empirical soil provenancing method. Mineralogical data from Fourier Transform InfraRed spectroscopy (FTIR) and Magnetic Susceptibility (MS), and geochemical data from X-Ray Fluorescence (XRF; for total major oxides) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS; for both total and aqua regia-soluble trace elements), are performed on the survey’s 268 topsoil samples (0-5 cm depth; 1 sample per km2). Principal components (PCs) are calculated after imputation of censored data and centred logratio transformation. The sequential provenancing approach is underpinned by (i) the preparation of interpolated raster grids of the soil properties (including PCs); (ii) the explicit quantification and propagation of uncertainty; (iii) the intersection of the soil property rasters with the values of the evidentiary sample (± uncertainty); and (iv) the computation of cumulative provenance rasters (‘heat maps’) for the various analytical techniques. The sequential provenancing method is tested in the north Canberra soil survey with three ‘blind’ samples representing simulated evidentiary samples. Performance metrics of precision and accuracy indicate that the FTIR and MS (mineralogy), as well as XRF and total ICP-MS (geochemistry) analytical methods offer the most precise and accurate provenance predictions. Inclusions of PCs in provenancing adds marginally to the performance. Maximising the number of analytes/analytical techniques is advantageous in soil provenancing. Despite acknowledged limitations and gaps, it is concluded that the empirical soil provenancing approach can play an important role in forensic and intelligence applications. <b>Citation:</b> de Caritat, P., Woods, B., Simpson, T., Nichols, C., Hoogenboom, L., Ilheo, A., Aberle, M.G. and Hoogewerff, J. (2021), Forensic soil provenancing in an urban/suburban setting: A sequential multivariate approach. <i>J Forensic Sci</i>, 66: 1679-1696. https://doi.org/10.1111/1556-4029.14727

This report describes the 2018 Probabilistic Tsunami Hazard Assessment for Australia (henceforth PTHA18). The PTHA18 estimates the frequency with which tsunamis of any given size occur in deep waters around the Australian coastline. To do this it simulates hundreds of thousands of possible tsunami scenarios from key earthquake sources in the Pacific and Indian Oceans, and models the frequency with which these occur. To justify the PTHA18 methodologies a significant fraction of the report is devoted to testing the tsunami scenarios against historical observations, and comparing the modelled earthquake rates against alternative estimates. Although these test provide significant justification for the PTHA18 results, there remain large uncertainties in “how often” tsunamis occur at many sites. This is due to fundamental limitations in present-day scientific knowledge of how often large earthquakes occur.

We are often faced with uncertainty when making decisions – from trivial decisions such as whether to take an umbrella, or major decisions such as whether to buy that house. Appreciating the uncertainty in future conditions (‘will it rain today?’; ‘will house prices continue to go up?’) is crucial to making good decisions. This is no different for water resource managers, who need to make decisions on flood prevention, climate adaptation or coal resource developments. As scientists, we strive to inform decision-makers about uncertainties in a comprehensive, unbiased and transparent manner. In this paper, we discuss some of the challenges and approaches used to communicate uncertainty during our contributions to the Bioregional Assessments Programme, a federally funded research project to assess the potential impacts of coal resource development on water resources and water-dependent assets in eastern Australia. A first step in analysing potential impacts, is to identify the causal pathways that detail how development activities can possibly affect the groundwater and surface water systems, and how these changes might affect the economic, social and ecological functioning of a region. This conceptual model provides the framework for detailed geological, hydrogeological, hydrological and ecological modelling. Predictions have traditionally been made using a single deterministic model, a calibrated model that best fits the available observations. However, to assess the likelihood of potential impacts, we used a stochastic approach to create an ensemble of possible predictions (hundreds and thousands of possible answers) that are all consistent with the available observations. This results in a range or distribution of predictions. However, communicating the range of model results, as well as all of the complexities and underlying assumptions in a way that is relevant and accessible to decision-makers is very challenging. For bioregional assessments, we have worked with decision makers to improve communication of uncertainty using a consistent, calibrated language, tables, plots of the range of predictions and maps designed to convey probabilistic information in an intuitive manner. Further, model details and assumptions are documented in technical reports, and the data, models and predictions are made publicly available to increase transparency and reproducibility. The amount and technical detail of that information can be challenging for decision-makers to identify what is important and what is not. To support decision-makers, we use a qualitative uncertainty analysis to summarise the rationale for and effect on prediction of each major assumption. This table, in combination with a plain English discussion, allows readers to rapidly appreciate the limitations, as well as opportunities for further data collection or modelling. Bioregional assessments have highlighted the importance of early consultation with target audiences, which has enabled us to tailor the uncertainty communication products to decision-makers, as well as avoid the potential for biased interpretation of results, where decision-makers are drawn to the extremes. <b>Citation:</b> Peeters, L.J.M., Crosbie, R.S., Henderson, B.H., Holland, K., Lewis, S., Post, D.A., Schmidt, R.K., The importance of being uncertain, <i>Water e-Journal</i>, Vol 3, No.2, 2018. ISSN 2206-1991. https//doi.org/10.21139/wej.2018.006

PTHA18 estimates the frequency with which tsunamis of any given size occur in deep waters around the Australian coastline. To do this it simulates hundreds of thousands of possible tsunami scenarios from key earthquake sources in the Pacific and Indian Oceans, and models the frequency with which these occur.

The 2018 Probabilistic Tsunami Hazard Assessmetn (PTHA18) outputs are can be accessed following the README instructions here: https://github.com/GeoscienceAustralia/ptha/tree/master/ptha_access

Numerical codes for probabilistic tsunami hazard assessment, available for download in github: https://github.com/GeoscienceAustralia/ptha

<div>Much of Australia has been surveyed with low-flying airborne electromagnetic (AEM) instrumentation under Geoscience Australia’s AusAEM program. Acquired AEM data allow for imaging the earth's buried geology down to depths of 300-500 m. Such imaging is crucial for managing Australia’s subsurface minerals, energy and groundwater resources, by allowing geoscientists to build a 3D framework of the shallow geological architecture. However, individual AEM lines can be up to 500 km long, data are acquired every 10-12 m, and conventional electromagnetic conductivity imaging methods based on optimisation are unable to accurately characterise the subsurface imaging resolution. Bayesian probabilistic methods can do so, but at significant computational cost if naively used. Efficient Markov chain sampling strategies with parameter dimension reduction, which leverage the high-performance distributed computing capabilities inherent in the Julia language, have now made large scale Bayesian AEM imaging possible. In this work we show the results of imaging using the Julia-based, open-source, High Quality Geophysical Analysis (HiQGA) package, on continent-wide data using Bayesian probabilistic methods. We are unaware of any similar analysis at this scale, routinely using 41,600 cpu-cores for up to three hours in semi-embarrassingly parallel fashion on the National Computational Infrastructure’s Gadi cluster at the Australian National University. Consequently, deeper geology can be mapped, and subsurface 3D geology can be rapidly demarcated using posterior percentiles of conductivity, when contrasted with deterministic methods. Compared to the cost of AEM acquisition, extraction of subsurface information with computation at scale greatly increases the economic and social return on public AEM data acquisition. Abstract presented at the 2024 Supercomputing Asia Conference, Sydney NSW (SAC2024)

This paper presents the application of a novel trans-dimensional sampling approach to a time domain airborne electromagnetic (AEM) inverse problem to solve for plausible conductivities of the subsurface. Geophysical inverse problems, such as time domain AEM, are well known to have a large degree of non-uniqueness. Common least-squares optimization approaches fail to take this into account and provide a single solution with linearized estimates of uncertainty which can result in overly optimistic appraisal of the conductivity of the subsurface. In this new non-linear approach, the spatial complexity of a 2D profile is controlled directly by the data. By examining an ensemble of proposed conductivity profiles it accommodates non-uniqueness and provides more robust estimates of uncertainties. <b>Citation:</b> Hawkins, R., Brodie, R. C., & Sambridge, M. (2018). Trans-dimensional Bayesian inversion of airborne electromagnetic data for 2D conductivity profiles. <i>Exploration Geophysics</i>, 49(2), 134–147. https://doi.org/10.1071/EG16139

<div>Airborne electromagnetic (AEM) surveying provides a rapid means of imaging shallow subsurface geology as represented by changes in electrical conductivity within the earth. Aircraft-borne systems fly at different heights and with different speeds, and the exciting transients for time-domain AEM systems provide different spectral content to image the earth with. Geoscience Australia</div><div>operates a test range over one of the Menindee lakes, in New South Wales, Australia, where different AEM systems have been flown over nearly a decade. Due to well studied geology and downhole induction data available in the area, this test range provides a useful proving ground for new AEM technology. For every test survey, certain lines within the range are repeatedly flown, and high-altitude lines are also acquired, such that robust data noise statistics can be established for all overflying AEM systems.</div><div><br></div><div>Test-range data and noise for various systems naturally allows us to compare AEM derived subsurface images of the test line. This study presents the results of both deterministic as well as Bayesian stochastic inversion over the same 13 km stretch of land, with six different systems flown between 2014-2023. While a deterministic inversion provides a first-pass image for comparing AEM systems, far more information is provided by the full posterior distribution of inverted conductivities, and in particular, the marginal quantiles of median and extremal conductivities over the entire image section. </div><div><br></div><div>Our findings indicate that there is generally good agreement with borehole logs, and the posterior conductivities for all systems agree well at the regional scale. The uncertainty (or the lack thereof) around ambiguous features in deterministic inversions is revealed through the stochastic inversions. Finally, we note that examination of water volumes in Menindee lakes do not show a simple relationship with inferred conductivity, indicating that unentangling environmental factors and system differences is a non-trivial matter. Presented at the 2024 Australian Society of Exploration Geophysicists (ASEG) Discover Symposium