Water, energy and mineral resources are vital for Australia’s economic prosperity and sustainable development. However, continued supply of these resources cannot be taken for granted. It is widely accepted that the frontier of exploration now lies beneath the Earth’s surface, making characterisation of the subsurface a unifying challenge. Between 2016 and 2020, the $100.5 million Exploring for the Future program focused on addressing this challenge across northern Australia in order to better define resource potential and boost investment. The program applied a multiscale systems approach to resource assessment based on characterisation of the Australian plate from the surface down to its base, underpinned by methodological advances. The unprecedented scale and diversity of new data collected have resulted in many world-first achievements and breakthrough insights through integrated systems science. Through this multi-agency effort, new continental-scale datasets are emerging to further enhance Australia’s world-leading coverage. The program has identified prospective regions for a wide range of resources and pioneered approaches to exploration undercover that can be applied elsewhere. The outcomes so far include extensive tenement uptake for minerals and energy exploration in covered terranes, and development of informed land-management policy. Here, we summarise the key scientific achievements of the program by reviewing the main themes and interrelationships of 62 contributions, which together constitute the Exploring for the Future: extended abstracts volume. <b>Citation:</b> Czarnota, K., Roach, I.C., Abbott, S.T., Haynes, M.W., Kositcin, N., Ray, A. and Slatter, E., 2020. Exploring for the Future: advancing the search for groundwater, energy and mineral resources. 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 Exploring for the Future (EFTF) program is unmatched for the scale of geoscience data acquisition across northern Australia, covering 37 648 individual land parcels and 2.9 million km2. The scale of works has raised many new challenges for Geoscience Australia (GA) through the diverse range of field activities, and the numerous stakeholders from different social and cultural backgrounds, across multiple jurisdictions. Success of the program depended on the development and maintenance of a social licence to operate. This was broadly achieved for fieldwork activities through early engagement with stakeholders, including free, prior and informed consent. Here, we present two case studies of stakeholder engagement for facilitation of data collection: one focuses on broadscale, low-impact field activities associated with the AusAEM survey; and the other on Indigenous-focused engagement related to the Barkly Seismic Survey. Because of the complexity of project planning and managing such a large number of stakeholders, GA’s project governance team was expanded; it now includes a dedicated Land and Marine Access team and a commitment to adopt world-leading engagement practices. One practice is to consider impacts and benefits for all stakeholders—not just landholders—of field activities and data acquisition programs. This includes a plan for how the data are delivered back to the communities and stakeholders, and how information exchanges can be built into projects. The aim is for stakeholders to hold GA in the highest regard and understand the benefits to the Australian people, and ultimately their own communities, from the geoscientific data that GA produces. <b>Citation:</b> Mouthaan, R., Buchanan, S. and Sweeney, M, 2020. Land access and Indigenous engagement for Australian geoscience. 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.

Seismological data are used for a variety of purposes, from earthquake hazard zonation to mapping Earth structure and mineral resource exploration. The immense volumes of seismic data now available challenge the application of routine seismic analysis techniques using existing tools. These tools fail to take advantage of recent advances in computing hardware and data formats. Given the scale of data to process and the computational complexity of algorithms involved, a more efficient approach that scales on high-performance computing and data (HPC-HPD) platforms is needed. In addition, different agencies have tended to use bespoke and ad hoc data formats, data curation processes and quality standards, hindering large-scale analyses and modelling. High-performance seismological tools (HiPerSeis) facilitate the transformation of source seismological data into formats geared towards HPC-HPD platforms. HiPerSeis also implements optimised seismological workflows that can be run at large scale on HPC-HPD platforms. <b>Citation:</b> Hassan, R., Hejrani, B., Medlin, A., Gorbatov, A. and Zhang, F., 2020. High-performance seismological tools (HiPerSeis). 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.

Petroleum geochemical datasets and information are essential to government for evidence-based decision making on natural resources, and to the petroleum industry for de-risking exploration. Geoscience Australia’s newly built Data Discovery Portal (https://portal.ga.gov.au/) enables digital discoverability and accessibility to key petroleum geochemical datasets. The portal’s web map services and web feature services allow download and visualisation of geochemical data for source rocks and petroleum fluids, and deliver a petroleum systems framework for northern Australian basins. The Petroleum Source Rock Analytics Tool enables interrogation of source rock data within boreholes and field sites, and facilitates correlation of these elements of the petroleum system within and between basins. The Petroleum Systems Summary Assessment Tool assists the user to search and query components of the petroleum system(s) identified within a basin. The portal functionality includes customised data searches, and visualisation of data via interactive maps, graphs and geoscientific tools. Integration of the petroleum systems framework with the supporting geochemical data enables the Data Discovery Portal to unlock the value of these datasets by affording the user a one-stop access to interrogate the data. This allows greater efficiency and performance in evaluating the petroleum prospectivity of Australia’s sedimentary basins, facilitating and accelerating decision making around exploration investment to ensure Australia’s future resource wealth <b>Citation:</b> Edwards, D.S., MacFarlane, S.K., Grosjean, E., Buckler, T., Boreham, C.J., Henson, P., Cherukoori, R., Tracey-Patte, T., van der Wielen, S., Ray, J. and Raymond, O., 2020. Australian source rocks, fluids and petroleum systems – a new integrated geoscience data discovery portal for maximising data potential. 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.

One of the aims of the Exploring for the Future (EFTF) program is to characterise the geochemistry of sedimentary and volcanic units, overbank sediments and groundwater in northern Australia to de-risk resource exploration and inform decision making. Underpinning this effort has been the generation of high-quality geochemical data from Geoscience Australia’s laboratories. A streamlined workflow from sample collection to reporting ensures that samples are processed consistently and to a high standard, and use of rigorously tested methodologies and appropriate QA/QC practices ensures data quality. This abstract highlights many of the processes undertaken in the laboratories, ranging from new sample preparation procedures (including automated milling and setting up temporary remote processing facilities) to organic geochemistry, mineralogy, inorganic geochemistry and geochronology. The laboratories were also instrumental in assisting with fieldwork, outsourcing sample analyses and storing data in corporate databases. The large volume of new data generated over the EFTF program has been used to characterise the geology and geochemistry of a range of rocks, regolith, oils, gas and groundwater, and has been instrumental in increasing knowledge of the resource potential of northern Australia and informing decision making. <b>Citation:</b> Jarrett, A.J.M., Thun, C., Champion, D.C., Boreham, C.J., Main, P., Waltenberg, K., Schroder, I., Bastrakov, E., DiBugnara, D., Long, I., Chen, J., Hong, Z., Sohn, J., Jinandasa, N., Palatty, P.,Webber, S., Webster, T., Byass, J., Gilmore, S., Williamson, A., Tubby, J., Long, R., Linehan, B. and Magee, C., 2020. Generation of high-quality data for energy, minerals and groundwater by Geoscience Australia’s laboratories. 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.