Hydrocarbon shows data are part of Geoscience Australia’s Reservoir, Facies and Shows (RESFACS) database, which contains depth-based information regarding hydrocarbon shows identified or interpreted during the drilling and evaluation of offshore and onshore petroleum wells. A hydrocarbon show is considered to be any indication of oil or gas observed during the drilling or evaluation of a petroleum well. Shows include data collected from well site observations, well logging, petrophysical analysis and well testing and/or sampling. A show evaluation is the complete analysis of a hydrocarbon-bearing formation with respect to lithology, depth and thickness, type and show value which indicates the potential productivity of the formation. Data entered into the shows table are most commonly sourced from both the Basic and Interpretive volumes of the Well Completion Reports (WCR) provided by the petroleum well operator under the Offshore Petroleum and Greenhouse Gas Storage Act (OPGGSA) 2006 and previous Petroleum (submerged Lands) Act (PSLA) 1967. Data is also sourced from hydrocarbon shows evaluations conducted by Geoscience Australia and its predecessor organisations, the Australian Geological Survey Organisation (AGSO) and the Bureau of Mineral Resources (BMR), and from state and territory geological organisations. Other open file data from company announcements and reports, scientific publications and university theses are also captured. The database structure has evolved over time and will keep changing as different types of petroleum data become available and the delivery platform changes. Data was initially delivered through the Petroleum Wells web page, http://dbforms.ga.gov.au/www/npm.well.search, which is in the process of being decommissioned. The hydrocarbon shows data will be available for viewing and download via the Geoscience Australia Portal Core, https://portal.ga.gov.au/.

Geoscience Australia's Australian National Hydrocarbon Geochemistry Data Collection comprises Oracle database tables from the Organic Geochemistry (ORGCHEM) schema and derivative information in the Petroleum Systems Summary database (Edwards et al., 2020, 2023; Edwards and Buckler, 2024). The ORGCHEM schema includes organic geochemistry, organic petrology and stable isotope database tables that capture the analytical results from sample-based datasets used for the discovery and evaluation of sediment-hosted resources. A focus is to capture open file data relevant to energy (i.e., petroleum and hydrogen) exploration, including source rocks, crude oils and natural gases from both onshore and offshore Australian sedimentary basins. The database tables also include complementary physical properties and complementary inorganic analyses on sedimentary rocks and hydrocarbon-based earth materials. The data are produced by a wide range of destructive analytical techniques conducted on samples submitted by industry under legislative requirements, as well as on samples collected by research projects undertaken by Geoscience Australia, other government agencies and scientific institutions. Some of these results have been generated by Geoscience Australia, whereas other data are compiled from service company reports, well completions reports, government reports, published papers and theses. The data is non-confidential and available for use by Government, the energy exploration industry, research organisations and the community. The Petroleum Systems Summary database stores the compilation of the current understanding of petroleum systems information, including the statistical evaluation of the analytical data by basin across the Australian continent. <b>Value: </b>These data in the ORGCHEM database tables comprise the raw organic geochemistry, organic petrological and stable isotopic values generated for Australian source rocks, crude oils and natural gases and is the only public comprehensive database at the national scale. The raw data are used as input values to other studies, such as basin analysis, petroleum systems evaluation and modelling, resource assessments, enhanced oil recovery projects, and national mapping projects. Derived datasets and value-add products are created based on calculated values and interpretations to provide information on the subsurface petroleum prospectivity of the Australian continent, as summarised in the Petroleum Systems Summary database. The data collection aspires to build a national scale understanding of Australia's petroleum and hydrogen resources. This data collection is useful to government for evidence-based decision making on sediment-hosted energy resources and the energy industry for de-risking both conventional and unconventional hydrocarbon exploration programs, hydrogen exploration programs, and carbon capture, utilisation and storage programs. <b>Scope: </b>The database initially comprised organic geochemical and organic petrological data on organic-rich sedimentary rocks, crude oils and natural gas samples sourced from petroleum wells drilled in the onshore and offshore Australian continent, including those held in the Australian National Offshore Wells Data Collection. Over time, other sample types (e.g., fluid inclusions, mineral veins, bitumen) from other borehole types (e.g., minerals, stratigraphic including the Integrated Ocean Drilling Program, and coal seam gas), marine dredge samples and field sites (outcrop, mines, surface seepage samples, coastal bitumen strandings) have been analysed for their molecular and stable isotopic chemical compositions and are captured in the databases. The organic geochemical database tables and derivative data compiled in the Petroleum Systems Summary database are delivered by web services and analytical tools in the <a href="https://portal.ga.gov.au/">Geoscience Australia Data Discovery Portal </a> and specifically in the <a href="https://portal.ga.gov.au/persona/sra">Source Rock and Fluid Atlas Persona</a>. These web services enable interrogation of source rock and petroleum fluids data within boreholes and from field sites and facilitate correlation of these elements of the petroleum system within and between basins. <b>Reference</b> Edwards, D.S., Buckler, T., Grosjean, E. & Boreham, C.J. 2024. Organic Geochemistry (ORGCHEM) Database. Australian Source Rock and Fluid Atlas. Geoscience Australia, Canberra. https://pid.geoscience.gov.au/dataset/ga/149422 Edwards, D., Hawkins, S., Buckler, T., Cherukoori, R., MacFarlane, S., Grosjean, E., Sedgmen, A., Turk, R. 2023. Petroleum Systems Summary database. Geoscience Australia, Canberra. https://dx.doi.org/10.26186/148979 Edwards, D.S., MacFarlane, S., Grosjean, E., Buckler, T., Boreham, C.J., Henson, P., Cherukoori, R., Tracey-Patte, T., van der Wielen, S.E., Ray, J., Raymond, O. 2020. Australian source rocks, fluids and petroleum systems – a new integrated geoscience data discovery portal for maximising data potential. Geoscience Australia, Canberra. http://dx.doi.org/10.11636/133751.

A predictive model of weathering intensity or the degree of weathering has been generate over the Australian continent. The model has been generated using the Random Forest decision tree machine learning algorithm. The algorithm is used to establish predictive relationships between field estimates of the degree of weathering and a comprehensive suite of covariate or predictive datasets. The covariates used to generate the model include satellite imagery, terrain attributes, airborne radiometric imagery and mapped geology. The weathering intensity model is an estimate of the degree of surface weathering only. The interpretation of the weathering intensity is different for in-situ or residual landscapes compared with transported materials within depositional landscapes. In residual landscapes, weathering process are operating locally whereas in depositional landscapes the model is reflecting the degree of weathering either prior to erosion and subsequent deposition, or weathering of sediments after being deposited. The degree of surface weathering is particularly important in Australia where variations in weathering intensity correspond to the nature and distribution of regolith (weathered bedrock and sediments) which mantles approximately 90% of the Australian continent. The weathering intensity prediction has been generated using the Random Forest decision tree machine learning algorithm. The algorithm is used to establish predictive relationships between field estimates of the degree of weathering and a comprehensive suite of covariate or predictive datasets. The covariates used to generate the model include satellite imagery, terrain attributes, airborne radiometric imagery and mapped geology. Correlations between the training dataset and the covariates were explored through the generation of 300 random tree models. An r-squared correlation of 0.85 is reported using 5 K-fold cross-validation. The mean of the 300 models is used for predicting the weathering intensity and the uncertainty in the weathering intensity is estimated at each location via the standard deviation in the 300 model values. The predictive weathering intensity model is an estimate of the degree of surface weathering only. The interpretation of the weathering intensity is different for in-situ or residual landscapes compared with transported materials within depositional landscapes. In residual landscapes, weathering process are operating locally whereas in depositional landscapes the model is reflecting the degree of weathering either prior to erosion and subsequent deposition, or weathering of sediments after being deposited. The weathering intensity model has broad utility in assisting mineral exploration in variably weathered geochemical landscapes across the Australian continent, mapping chemical and physical attributes of soils in agricultural landscapes and in understanding the nature and distribution of weathering processes occurring within the upper regolith. <b>Value: </b>Weathering intensity is an important characteristic of the earth's surface that has a significant influence on the chemical and physical properties of surface materials. Weathering intensity largely controls the degree to which primary minerals are altered to secondary components including clay minerals and oxides. In this context the weathering intensity model has broad application in understanding geomorphological and weathering processes, mapping soil/regolith and geology. <b>Scope: </b>National dataset which over time can be improved with additional sites for training and thematic datasets for prediction.

The AEM method measures regolith and rocks' bulk subsurface electrical conductivity, typically to a depth of several hundred meters. AEM survey data is widely used in Australia for mineral exploration (i.e. mapping undercover and detection of mineralisation), groundwater assessment (i.e. hydro-stratigraphy and water quality) and natural resource management (i.e. salinity assessment). Geoscience Australia (GA) has flown Large regional AEM surveys over Northern Australia, including Queensland, Northern Territory and Western Australia. The surveys were flown nominally at 20-kilometre line spacing, using the airborne electromagnetic systems that have signed technical deeds of staging with GA to ensure they can be modelled quantitatively. Geoscience Australia commissioned the survey as part of the Exploring for the Future (EFTF) program. The EFTF program is led by Geoscience Australia (GA), in collaboration with the Geological Surveys of the Northern Territory, Queensland, South Australia and Western Australia, and is investigating the potential mineral, energy and groundwater resources in northern Australia and South Australia. We have used a machine learning modelling approach that establishes predictive relationships between the inverted flight-line modelled conductivity with a suite of national environmental and geological covariates. These covariates include terrain derivatives, gamma-ray radiometric, geological maps, climate derived surfaces and satellite imagery. Conductivity-depth values were derived from a single model using GA's deterministic 1D smooth-30-layer layered-earth-inversion algorithm. (Brodie and Richardson 2015). Three conductivity depth interval predictions are generated to interpolate the actual modelled conductivity data, which is 20km apart. These depth slices include a 0-50cm, 9-11m and 22-27m depth prediction. Each depth interval was modelled and individually optimised using the gradient boosted tree algorithm. The training cross-validation step used label clusters or groups to minimise over-fitting. Many hundreds of conductivity models are generated (i.e. ensemble modelling). Here we use the median of the models as the conductivity prediction and the upper and lower percentiles (95th and 5th) to measure model uncertainty. Grids show conductivity (S/m) in log 10 units. Reported out-of-sample r-squares for each interval in order of increasing depth are 0.74, 0.64, and 0.67. A decline in model performance with increasing depth was expected due to the decrease in suitable covariates at greater depths. Modelled conductivities seem to be consistent with the geological, regolith, geomorphological, and climate processes in the study area. The conductivity grids are at the resolution of the covariates, which have a nominal pixel size of 85 meters. Datasets in this data package include; 1. 0-50cm depth interval 0_50cm_median.tif; 0_50_upper.tif; 0_50_lower.tif 2. 9-11m depth interval 9_11m_median.tif; 9_11m_upper.tif; 9_11m_lower.tif 3. 22-27m depth interval 22_27_median.tif; 22_27_upper.tif; 22_27_lower.tif 4. Covariate shift; Cov_shift.tif (higher values = great shift in covariates) Reference: Ross C Brodie & Murray Richardson (2015) Open Source Software for 1D Airborne Electromagnetic Inversion, ASEG Extended Abstracts, 2015:1, 1-3, DOI: 10.1071/ ASEG2015ab197

A `weighted geometric median' approach has been used to estimate the median surface reflectance of the barest state (i.e., least vegetation) observed through Landsat-8 Operational Land Image (OLI) observations from 2013 to September 2018 to generate a six-band Landsat-8 Barest Earth pixel composite mosaic over the Australian continent. The bands include BLUE (0.452 - 0.512), GREEN (0.533 - 0.590), RED, (0.636 - 0.673) NIR (0.851 - 0.879), SWIR1 (1.566 - 1.651) and SWIR2 (2.107 - 2.294) wavelength regions. The weighted median approach is robust to outliers (such as cloud, shadows, saturation, corrupted pixels) and also maintains the relationship between all the spectral wavelengths in the spectra observed through time. The product reduces the influence of vegetation and allows for more direct mapping of soil and rock mineralogy. Reference: Dale Roberts, John Wilford, and Omar Ghattas (2018). Revealing the Australian Continent at its Barest, submitted. <b>Value: </b>Has broad application in mapping surface geochemistry and mineralogy of exposed soil and bedrock. Has applications in geological mapping and natural resource management including mapping of soil characteristics. <b>Scope: </b>Two enhanced bare earth products have been generated reflecting different Landsat satellites and acquisition periods. The first only uses Landsat 8 observations from 2013 to 2018. The second incorporates the full 30+ year archive combining Landsat 5, 7, and 8 from 1986 to 2018.

Alkaline and related rocks are a relatively rare class of igneous rocks worldwide. Alkaline rocks encompass a wide range of rock types and are mineralogically and geochemically diverse. They are typically though to have been derived by generally small to very small degrees of partial melting of a wide range of mantle compositions. As such these rocks have the potential to convey considerable information on the evolution of the Earth’s mantle (asthenosphere and lithosphere), particularly the role of metasomatism which may have been important in their generation or to which such rocks may themselves have contributed. Such rocks, by their unique compositions and or enriched source protoliths, also have considerable metallogenic potential, e.g., diamonds, Th, U, Zr, Hf, Nb, Ta, REEs. It is evident that the geographic occurrences of many of these rock types are also important, and may relate to presence of old cratons, craton margins or major lithospheric breaks. Finally, many alkaline rocks also carry with them mantle xenoliths providing a snapshot of the lithospheric mantle composition at the time of their emplacement. Accordingly, although Alkaline and related rocks comprise only a volumetrically minor component of the geology of Australia, they are of considerable importance to studies of lithospheric composition, evolution and architecture and to helping constrain the temporal evolution of the lithosphere, as well as more directly to metallogenesis and mineralisation. This GIS product presents the first part of an ongoing compilation of the distribution and geology of alkaline and related rocks throughout Australia. The accompanying report document alkaline and related rocks of Archean age. All are from the Pilbara and Yilgarn Cratons of Western Australia. The report also reviews the nomenclature of alkaline rocks and classification procedures. GIS metadata is documented in the appendices.

Damaging earthquakes are less frequent in Australia when compared to other weather-related events, but when they do occur close to a community they can cause major damage and injury. This risk to property and life exists for building owners, particularly if the building is of vulnerable construction. The good news is that your building can be retrofitted to improve its earthquake resilience within a sensible budget without compromising its heritage value. This document seeks to show you how. It explains the nature of earthquake risk and provides resources for building owners on how the risk can be reduced for the most vulnerable building construction type: unreinforced masonry.

Background These are the statistics generated from the DEA Water Observations (Water Observations from Space) suite of products, which gives summaries of how often surface water was observed by the Landsat satellites for various periods (per year, per season and for the period from 1986 to the present). Water Observations Statistics (WO-STATS) provides information on how many times the Landsat satellites were able to clearly see an area, how many times those observations were wet, and what that means for the percentage of time that water was observed in the landscape. What this product offers Each dataset in this product consists of the following datasets: - Clear Count: how many times an area could be clearly seen (i.e. not affected by clouds, shadows or other satellite observation problems) - Wet Count: how many times water was detected inobservations that were clear - Water Summary: what percentage of clear observations were detected as wet (i.e. the ratio of wet to clear as a percentage) As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the input water classifications, and can be difficult to interpret on its own. The confidence layer and filtered summary are contained in the Water Observations Filtered Statistics (WO-FILT-STATS) product, which provides a noise-reduced view of the all-of-time water summary. WO-STATS is available in multiple forms, depending on the length of time over which the statistics are calculated. At present the following are available: WO-STATS:statistics calculated from the full depth of time series (1986 to present) WO-STATS-ANNUAL:statistics calculated from each calendar year (1986 to present) WO-STATS-NOV-MAR:statistics calculated yearly from November to March (1986 to present) WO-STATS-APR-OCT:statistics calculated yearly from April to October (1986 to present)

Australia has a low seismicity when compared to countries located along tectonic plate boundaries. Seismic risk, however, is the combination of hazard, community exposure and infrastructure vulnerability. The legacy of older unreinforced masonry buildings is a particular subset of the built environment that may contribute disproportionately to community risk. Documented information on the damage to buildings caused by earthquake events is fundamental to understanding this risk. The Earthquake Earlier this year on the 20th April a magnitude 5.0 (ML) earthquake shook the Western Australian goldfields town of Kalgoorlie. The earthquake was shallow (1.7 km) and was located immediately south of the business district of the Kalgoorlie suburb of Boulder (refer Figure 1). The severity of ground motion was found to vary markedly across the town with the older masonry building stock in Boulder experiencing a greater intensity of shaking than the corresponding building age group in the Kalgoorlie business district 4 km away. The event has provided the best opportunity to examine the earthquake vulnerability of Australian buildings since the Newcastle Earthquake of the 28th December 1989, over twenty years prior. The Survey Following the earthquake Geoscience Australia (GA) arranged a staged collaborative survey that would capture information from which vulnerability knowledge could be derived.

While damaging earthquakes are less frequent in Australia when compared to other weather related events, when they do occur close to communities they can cause major damage and injury. This community risk to life, property, social fabric and the local economy is significant. The risk also presents associated challenges for government agencies with a role in emergency response, health care and community recovery both in the short and longer term. For some communities recovery to pre-event conditions may never be fully realised due to the destruction of heritage value that may be central to local business activity. Resources for building resilience to earthquakes need to be prioritised against those needed for other hazards. What are the benefits of earthquake retrofit of high risk buildings to communities and what exemplars of risk management driven from government exist? What resources exist for a business case to be articulated for limited resources and for motivating investment by property owners to reduce their individual risk? This document seeks provide useful answer these questions. It presents information that explains the nature of earthquake hazard in Australia, the risk it presents and vulnerability factors behind it. It also provides information on the effectiveness of retrofit in reducing the impact of earthquakes, emergency management logistics and recovery needs. It further provides links to resources that can be used to advance local programs for building community resilience. The primary focus is the most vulnerable building construction type, unreinforced masonry, but the principles are informative to the address of other high risk building types in communities.