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.

<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.

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)

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

<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.

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.

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.

Final report on the backgrounds, collaboration structure, methods, and findings from the EIRAPSI project

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’.

Linked Data refers to a collection of interrelated datasets on the Web expressed in a standard structure. These Linked Data and relationships among them can be reached and managed by Semantic Web tools. Linked Data enables large scale integration of and reasoning on data on the Web. This cookbook is to documents the processes and workflows required to create a Linked Data API for a dataset in the Foundation Base Project in Geoscience Australia (GA) and further publish it on the AWS.