The Flying Hellfish provide Geoscience Australia with web portals of an unprecedented quality and impact. They have achieved this by embracing automation, digital culture and cloud to uplift Geoscience Australia's web portal presence to scale and meet the demands of the modern user. In 2014 these concepts were only ideas and experiments. However, since the team formed in 2016 they have been on a transformational journey towards a new way of working which has delivered radically better digital products than what was available at the outset. User experience is now at the forefront of our web portals, with the common look and feel providing a seamless experience across more than 15 digital products on any device (including smartphones). The security has been proven to be state-of-the-art, and the products are designed to be fast and responsive. In this presentation you will learn how the team utilises NoOps (the No Operations paradigm) to build, operate and support these products while continuing to quickly and efficiently deliver new and innovative digital products.

One of the aims of the Exploring for the Future program is to promote the discovery of new mineral deposits in undercover frontiers. Iron oxide–copper–gold mineral systems are a desirable candidate for undercover exploration, because of their potential to generate large deposits with extensive alteration footprints. This mineral potential assessment uses the mineral systems concept: developing mappable proxies of required theoretical criteria, combined to demonstrate where conditions favourable for mineral deposit formation are spatially coincident. This assessment uses a 2D geographical information system workflow to map the favourability of the key mineral system components. Two outputs were created: a comprehensive assessment, using all available spatial data; and a coverage assessment, which is constrained to data that have no reliance on outcrop. The results of these assessment outputs were validated with spatial statistics, demonstrating how the assessment can predict the presence of known ore deposits. Both assessment outputs present new areas of interest with prospectivity in under-explored regions of undercover northern Australia. The intended aims are already being realised, as this tool has aided area selection for pre-competitive stratigraphic drilling as part of the MinEx CRC National Drilling Initiative. <b>Citation:</b> Murr, J., Skirrow, R.G., Schofield, A., Goodwin, J., Coghlan, R., Highet, L., Doublier, M.P., Duan, J. and Czarnota, K., 2020. Tennant Creek – Mount Isa IOCG mineral potential assessment. 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 purpose of this document is to define an Emergency Management (EM) Metadata Profile Extension to the ISO 19115-1:2014/AMD 1:2018 to identify the metadata required to accurately describe EM resources. The EM Metadata Profile is designed to support the documentation and discovery of EM datasets, services, and other resources. This version of the Profile was developed to reflect extensions made to the current version of the international metadata standard: ISO 19115-1:2014/AMD 1:2018.

The pace, with which government agencies, researchers, industry, and the public need to react to national and international challenges of economic, environmental, and social natures, is constantly changing and rapidly increasing. Responses to the global COVID-19 pandemic event, the 2020 Australian bushfire and 2021 flood crisis situations are recent examples of these requirements. Decisions are no longer made on information or data coming from a single source or discipline or a solitary aspect of life: the issues of today are too complex. Solving complex issues requires seamless integration of data across multiple domains and understanding and consideration of potential impacts on businesses, the economy, and the environment. Modern technologies, easy access to information on the web, abundance of openly available data shifts is not enough to overcome previous limitations of dealing with data and information. Data and software have to be Findable, Accessible, Interoperable and Reusable (FAIR), processes have to be transparent, verifiable and trusted. The approaches toward data integration, analysis, evaluation, and access require rethinking to: - Support building flexible re-usable and re-purposeful data and information solutions serving multiple domains and communities. - Enable timely and effective delivery of complex solutions to enable effective decision and policy making. The unifying factor for these events is location: everything is happening somewhere at some time. Inconsistent representation of location (e.g. coordinates, statistical aggregations, and descriptions) and the use of multiple techniques to represent the same data creates difficulty in spatially integrating multiple data streams often from independent sources and providers. To use location for integration, location information needs to be embedded within the datasets and metadata, describing those datasets, so those datasets and metadata would become ‘spatially enabled’.

<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.

<p>Iron oxide-copper-gold (IOCG) mineral systems are a desirable undercover exploration target due to their large alteration footprint and potentially high metal content. To assist in understanding the potential for IOCG mineral systems beneath cover in the Tennant Creek to Mount Isa region as part of Exploring for the Future, a predictive mineral potential assessment has been undertaken using a knowledge-based, mineral systems approach.<p>This mineral potential assessment uses a 2D, GIS-based workflow to qualitatively 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 criteria are weighted and combined using an established workflow to produce the final map of IOCG potential, all of which is well documented in the accompanying IOCG Assessment Criteria Table.<p>Two assessments have been undertaken. The first is a comprehensive assessment containing all available geospatial information and is highly reliant on the level of geological knowledge. As such, it preferentially highlights mineral potential in well-understood areas, such as outcropping regions and performs less well in covered areas, where there is a greater likelihood of data gaps. 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.<p>Both assessment highlight new areas of interest in underexplored regions, of particular interest a SW-NE corridor to the East of Tennant Creek of moderate/high potential in the Barkly region. This corridor extends to an area of moderate potential in the Murphy Inlier region near the Gulf of Carpentaria on the NT/QLD border.

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.

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.

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.