<p>The Isotopic Atlas of Australia is one of the fundamental datasets in Geoscience Australia (GA)’s Exploring for the Future program. It is underpinned by a nationwide coverage of high-quality U-Th-Pb radiometric dates, mostly determined by Sensitive High Resolution Ion Micro Probe (SHRIMP). For the past decade, GA and the international SHRIMP community have relied on SQUID 2.50 software to process isotopic data acquired by SHRIMP for U-Th-Pb geochronology. However, SQUID 2.50 is obsolete because of dependency on Excel 2003, which is unsupported by Microsoft and will not operate on Windows 10. As a result, GA collaborated with the Cyber Infrastructure Research and Development Laboratory for Earth Sciences (CIRDLES.org) at the College of Charleston (USA) to redeploy SQUID 2.50 algorithms in an open-source, platform-independent and freely available Java application (Squid3). Squid3 replicates (rather than seeking to enhance) SQUID 2.50 logic and arithmetic, with substantial improvements in flexibility and interactivity. In this paper, we review documentation detailing widely trusted but little-known SQUID 2.50 algorithms and provide an overview of Squid3, focusing on the implementation and improvement of SQUID 2.50 functionality. The beta version of Squid3 is capable of end-to-end U-Th-Pb data processing, from ingestion of raw SHRIMP .xml files, through finalised summary calculations, to reporting of data arrays suitable for visualisation via packages such as Isoplot, Topsoil and IsoplotR. In production, Squid3 will enable users to sever links with Excel 2003, while ensuring the sustainability, reliability and relevance of SHRIMP data. <p><b>Citation:</b> Bodorkos, S., Bowring, J.F., and Rayner, N.M., 2020. Squid3: Next-generation data processing software for Sensitive High Resolution Ion Micro Probe (SHRIMP). In: Czarnota, K., Roach, I., Abbott, S., Haynes, M., Kositcin, N., Ray, A. and Slatter, E. (eds.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, 1–4.

<p>Airborne electromagnetic (AEM) data can be acquired cost-effectively, safely and efficiently over large swathes of land. Inversion of these data for subsurface electrical conductivity provides a regional geological framework for water resources management and minerals exploration down to depths of ~200 m, depending on the geology. However, for legacy reasons, it is not uncommon for multiple deterministic inversion models to exist, with differing details in the subsurface conductivity structure. This multiplicity presents a non-trivial problem for interpreters who wish to make geological sense of these models. In this article, we outline a Bayesian approach, in which various spatial locations were inverted in a probabilistic manner. The resulting probability of conductivity with depth was examined in conjunction with multiple existing deterministic inversion results. The deterministic inversion result that most closely followed the high-credibility regions of the Bayesian posterior probability was then selected for interpretation. Examining credibility with depth also allowed interpreters to examine the ability of the AEM data to resolve the subsurface conductivity structure and base geological interpretation on this knowledge of uncertainty. <p> <b>Citation:</b> Ray, A., Symington, N., Ley-Cooper, Y. and Brodie, R.C., 2020. A quantitative Bayesian approach for selecting a deterministic inversion model. In: Czarnota, K., Roach, I., Abbott, S., Haynes, M., Kositcin, N., Ray, A. and Slatter, E. (eds.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, 1–4.