A publicly available AGOL Dashboard that periodically updates to show the status of requests made to the Australian Exposure Information Platform (AEIP), categorised as Running, Queued and Completed (www.aeip.ga.gov.au)

<p>Digital Earth Australia manages a cloud based service that makes use of open source software and open standards to deliver satellite imagery to its clients. <p>In conjunction with Frontier SI and Commonwealth Scientific and Industrial Research Organisation, Geoscience Australia’s Digital Earth Australia project has developed a cloud architecture that utilizes the Open Data Cube (ODC) to deliver Earth Observation (EO) data through Open Geospatial Consortium (OGC) API standards, interactive Jupyter notebooks and direct file access.​ <p>This infrastructure enables EO data to be used to make decisions by industry and government partners, and reduces the time required to deliver new EO data products. ​ <p>To store the data, DEA utilises Amazon Web Services (AWS) Object store: Simple Storage Service (S3) to hold an archive of Cloud Optimised GeoTIFFs (COGs). ​ <p>This data is indexed by Open Data Cube (ODC) an open source python library. DEA deploy processing, visualisation and analysis applications that make use of the indexed data. This method reduces the duplication of code and effort and creates an extensible framework for delivering data.

HiQGA is a general purpose software package for spatial statistical inference, geophysical forward modeling, Bayesian inference and inversion (both deterministic and probabilistic). It includes readily usable geophysical forward operators for airborne electromagnetics (AEM), controlled-source electromagnetics (CSEM) and magnetotellurics (MT). Physics-independent inversion frameworks are provided for probabilistic reversible-jump Markov chain Monte Carlo (rj-MCMC) inversions, with models parametrised by Gaussian processes (Ray and Myer, 2019), as well as deterministic inversions with an "Occam inversion" framework (Constable et al., 1987). In development software for EFTF since 2020

The gnssanalysis Python package is designed to provide the public with a source of useful python functions and classes that help with processing of GNSS observations. The functionality found within the package includes: - reading of many standard file formats commonly used in the geodetic community including SP3, SNX, RNX, CLK, PSD, etc. into pandas dataframes (Also writing certain file formats) - transformation of data, for example datetime conversions, helmert inversions, rotations, transforming geodata from XYZ to longitude-latitude-altitude, etc. - functions for the download of standard files and upload to other sources (e.g. s3)

The magnetotelluric (MT) method is increasingly being applied to map tectonic architecture and mineral systems. Under the Exploring for the Future (EFTF) program, Geoscience Australia has invested significantly in the collection of new MT data. The science outputs from these data are underpinned by an open-source data analysis and visualisation software package called MTPy. MTPy started at the University of Adelaide as a means to share academic code among the MT community. Under EFTF, we have applied software engineering best practices to the code base, including adding automated documentation and unit testing, code refactoring, workshop tutorial materials and detailed installation instructions. New functionality has been developed, targeted to support EFTF-related products, and includes data analysis and visualisation. Significant development has focused on modules to work with 3D MT inversions, including capability to export to commonly used software such as Gocad and ArcGIS. This export capability has been particularly important in supporting integration of resistivity models with other EFTF datasets. The increased functionality, and improvements to code quality and usability, have directly supported the EFTF program and assisted with uptake of MTPy among the international MT community. <b>Citation:</b> Kirkby, A.L., Zhang, F., Peacock, J., Hassan, R. and Duan, J., 2020. Development of the open-source MTPy package for magnetotelluric data analysis and visualisation. 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.

The geosciences are a data-rich domain where Earth materials and processes are analysed from local to global scales. However, often we only have discrete measurements at specific locations, and a limited understanding of how these features vary across the landscape. Earth system processes are inherently complex, and trans-disciplinary science will likely become increasingly important in finding solutions to future challenges associated with the environment, mineral/petroleum resources and food security. Machine learning is an important approach to synthesise the increasing complexity and sheer volume of Earth science data, and is now widely used in prediction across many scientific disciplines. In this context, we have built a machine learning pipeline, called Uncover-ML, for both supervised and unsupervised learning, prediction and classification. The Uncover-ML pipeline was developed from a partnership between CSIRO and Geoscience Australia, and is largely built around the Python scikit-learn machine learning libraries. In this paper, we briefly describe the architecture and components of Uncover-ML for feature extraction, data scaling, sample selection, predictive mapping, estimating model performance, model optimisation and estimating model uncertainties. Links to download the source code and information on how to implement the algorithms are also provided. <b>Citation:</b> Wilford, J., Basak, S., Hassan, R., Moushall, B., McCalman, L., Steinberg, D. and Zhang, F, 2020. Uncover-ML: a machine learning pipeline for geoscience data analysis. 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.

Following the successful outcomes of the Tennant Creek-Mt Isa (TISA) mineral potential assessment (Murr et al., 2019; Skirrow et al., 2019), the methodology has been expanded to encompass the entire North Australian Craton (NAC). Like its predecessor, this assessment uses a knowledge-based, data-rich mineral systems approach to predict the potential for iron oxide-copper-gold (IOCG) mineralisation. With their high metal yield and large alteration footprint, IOCG mineral systems remain an attractive target in directing exploration efforts towards undercover regions. This mineral potential assessment uses a 2D GIS-based workflow to map four key mineral system components: (1) Sources of metals, fluids and ligands, (2) Energy to drive fluid flow, (3) Fluid flow pathways and architecture, and (4) Deposition mechanisms, such as redox or chemical gradients. For each of these key mineral system components, theoretical criteria representing important ore-forming processes were identified and translated into mappable proxies using a wide range of input datasets. Each of these criterion are weighted and combined using an established workflow to produce a models of IOCG potential. Metadata and selection rational are documented in the accompanying NAC IOCG Assessment Criteria Table. Two scenarios were modelled for this assessment. The first is a comprehensive assessment, targeting pre-Neoproterozoic mineral systems (>1500 Ma), using a combination of interpreted, geological and geophysical datasets. As geological interpretations are subjective to the geological knowledge of the interpreter, well-documented areas, such as shallow pre-Neoproterozoic basement, have a greater density of data. This increase in data density can create an inherent bias in the modelled result towards previously explored shallow terrains. The second assessment utilises only datasets which can be mapped consistently across the assessment area. As such, these are predominately based on geophysical data and are more consistent in assessing exposed and covered areas. However, far fewer criteria are included in this assessment, and observations are reflective of only the modern geological environment. Both assessments highlight existing mineral fields in WA, NT and QLD, and suggest that these regions extend under cover. Furthermore, regions not previously known for IOCG mineralisation display a high modelled potential, offering exploration prospects in previously unknown or discounted areas.

All commercially produced hydrogen worldwide is presently stored in salt caverns. In eastern Australia, the only known thick salt accumulations are found in the Boree Salt of the Adavale Basin in central Queensland. Although the number of wells penetrating the basin is limited, salt intervals up to 555 m thick have been encountered. The Boree Salt consists predominantly of halite and is considered to be suitable for hydrogen storage. Using well data and historical 2D seismic interpretations, we have developed a 3D model of the Adavale Basin, particularly focussing on the thicker sections of the Boree Salt. Most of the salt appears to be present at depths greater than 2000 m, but shallower sections are found in the main salt body adjacent to the Warrego Fault and to the south at the Dartmouth Dome. The preliminary 3D model developed for this study has identified three main salt bodies that may be suitable for salt cavern construction and hydrogen storage. These are the only known large salt bodies in eastern Australia and therefore represent potentially strategic assets for underground hydrogen storage. There are still many unknowns, with further work and data acquisition required to fully assess the suitability of these salt bodies for hydrogen storage. Recommendations for future work are provided. <b>Citation:</b> Paterson R., Feitz A. J., Wang L., Rees S. & Keetley J., 2022. From A preliminary 3D model of the Boree Salt in the Adavale Basin, Queensland. In: Czarnota, K. (ed.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, https://dx.doi.org/10.26186/146935

Fuelled by a virtual explosion in digital capabilities the world is changing very fast and the field of Geoscience is no exception. Geoscience Australia (GA) has undergone a rapid digital transformation in the past 5 years. This has in part been driven by increasing organisational ICT costs, and partly by whole of government and more broadly global changes to the digital environment such as; growth in data, cheaper, more available cloud and infrastructure, new advances in AI and ML, new models for continuous development (DevOps) and public expectations that data will be available and accessible. The overarching principle of Geoscience Australia's Digital Strategy 2019-22 acknowledges that GA’s Enterprise ICT is inherently integrated with scientific data and ICT requirements, and more broadly these are the foundation of the digital science we are respected for at GA. As such, this strategy brings all three of these components together in a whole of agency digital strategy. The Digital Strategy focuses on strong foundations, systematic experimentation, data-driven science and digital culture and capability. Eighteen different strategies lie under these themes to help guide GA's focus for the next 3 years. There are also a number of high level KPI’s designed to help us measure our success.

Effective mineral, energy and groundwater resource management and exploration rely on accurate geological maps. While geological maps of the surface exist and increase in resolution, maps of the subsurface are sparse, and the underpinning geological and geophysical constraints are disordered or non-existent. The Estimates of Geological and Geophysical Surfaces (EGGS) database seeks to enable robust subsurface geological mapping by establishing an ordered collection of precious geological and geophysical interpretations of the subsurface. EGGS stores the depth to geological boundaries derived from boreholes as well as interpretations of depth to magnetic top assessments, airborne electromagnetics inversions and reflection seismic profiles. Since geological interpretation is iterative, links to geophysical datasets and processing streams used to image the subsurface are stored. These metadata allow interpretations to be readily associated with the datasets from which they are derived and re-examined. The geological basis for the interpretation is also recorded. Stratigraphic consistency is maintained by linking each interpretation to the Australian Stratigraphic Units Database. As part of the Exploring for the Future program, >170 000 points were entered into the EGGS database. These points underpin construction of cover thickness models and economic fairway assessments. <b>Citation:</b> Mathews, E.J., Czarnota, K., Meixner, A.J., Bonnardot, M.-A., Curtis, C., Wilford, J., Nicoll, M.G., Wong, S.C.T., Thorose, M. and Ley-Cooper, Y., 2020. Putting all your EGGS in one basket: the Estimates of Geological and Geophysical Surfaces database. 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.