This service has been created specifically for display in the National Map and the chosen symbology may not suit other mapping applications. The Australian Topographic web map service is seamless national dataset coverage for the whole of Australia. These data are best suited to graphical applications. These data may vary greatly in quality depending on the method of capture and digitising specifications in place at the time of capture. The web map service portrays detailed graphic representation of features that appear on the Earth's surface. These features include the administration boundaries from the Geoscience Australia 250K Topographic Data, including state forest and reserves.

This service is produced for the National Map project. It provides seamless topographic greyscale mapping for the whole of Australia, including the external territories of Cocos (Keeling) Islands, Christmas Island, Norfolk Island and Lord Howe Island. The service consists of Geoscience Australia data at smaller scales and OpenStreetMap data is used at larger scales. The service contains layer scale dependencies.

Spatial distribution of sponge species richness and its relationship with environmental variables are important for the informed monitoring of ecosystem health and marine environmental management and conservation within the Oceanic Shoals Commonwealth Marine Reserve, in the Timor Sea region, northern Australia. However, the spatially continuous data of sponge species richness is not readily available, and the relationship is largely unknown. In this study, we modelled sponge species richness data of 77 samples using random forest (RF) and generalised linear model (glm) and their hybrid methods with geostatistical techniques (i.e. ordinary kriging (OK) and inverse distance weighting (IDW)) based on seabed biophysical variables. These methods are RF, RFOK, RFIDW, glm, glmok and glmidw that is a new hybrid method. We also examined effects of model averaging using four averaged methods (RFOKRFIDW, RFRFOKRFIDW, glmokglmidw and glmglmokglmidw) and the effects of various predictor sets on the accuracy of predictive models. Four feature selection methods, 1) averaged variable importance (AVI), 2) Boruta, 3) knowledge informed AVI (KIAVI) and 4) recursive feature selection (rfe), were used for RF; and four variable selection methods: 1) stepAIC, 2) dropterm, 3) anova and 4) RF, were employed to select glm predictive models. Predictive models were validated based on 10-fold cross validation. Finally the spatial distribution of sponge richness was predicted using the most accurate model and examined. The main findings are 1) the initial input predictors affect the status of important and unimportant variables; 2) AVI is not always reliable and KIAVI is recommended for selecting RF predictive model, 3) using Boruta can improve the accuracy in comparison with the full model, but it may lead to sub-optimal models; and features selected using rfe are not optimal and can be even misleading; 4) the accuracy of glm predictive model did not align with AIC, deviance explained (%) and deviance explained adjusted (%), suggesting that conventional model selection approaches for glm is unable to identify reliable predictive models; 5) joint application of RF and AIC is a useful model selection approach for developing glm predictive models; 6) the goodness of fit should not be used to assess glm predictive models; 7) the hybrid methods have significantly improved the predictive accuracy for both RF and glm; and the hybrid methods of RF and geostatistical methods are considerably more accurate and able to effectively model count data; and 8) the relationships of sponge species richness with the predictors are non-linear, and high sponge species richness is usually associated with hard seabed features. This study further confirms that: 1) the initial input predictors affect the model selection for RF; 2) the inclusion of highly correlated predictors could improve predictive accuracy, providing important guideline for pre-selecting predictors for RF; and 3) the effects of model averaging are method dependent or even data dependent. This study also provides important information for future monitoring design, particularly on the areas where the management and conservation of sponge gardens should be focused.

Anthropogenic mercury (Hg) is a global pollutant capable of undergoing long-range atmospheric transport. Understanding biogeochemical controls on the spatial distribution of Hg in Australia at the continental scale with its unique biota, soil types, and climatic variables, is critical for modelling Hg emission rates and transport at regional and global scales. Surface (0-10 cm) catchment outlet sediment samples from the National Geochemical Survey of Australia (NGSA) were analysed for aqua regia soluble element content including Hg in coarse (<2 mm) and fine (<75 µm) grain-size fractions by ICP-MS analysis. We hypothesise that natural controls including soil type (organic carbon and clay content), vegetation type and climate variables (precipitation, temperature, evapotranspiration, solar radiation) explain the Hg variability at the continental scale in Australia. For this purpose, we are utilizing digital maps of the above-mentioned variables, and the NGSA continental-scale geochemical data to perform geostatistical modelling of Hg distribution at the continental scale. Our preliminary results indicate that organic carbon and selenium concentrations correlate with Hg concentrations in coastal landscapes in the cool and warm temperate bioclimatic zones of southern and eastern Australia across latitudinal and longitudinal gradients.

Short abstract for 35th International Geological Congress, Capetown, South Africa, August/September 2016

Airborne electromagnetic (AEM) data are an immensely useful tool for mapping cover thickness and under cover geology in Australia. The regional AEM surveys conducted by Geoscience Australia (GA) are an ideal starting point for integrating legacy AEM datasets across a range of scales with other information, e.g. borehole stratigraphy and shallow seismic data, to add to a national cover thickness map. Geoscience Australia is working towards this end as part of the UNCOVER Initiative.

Spatial distribution of sponge species richness and its relationship with environmental variables are important for the informed monitoring of ecosystem health and marine environmental management and conservation within the Oceanic Shoals Commonwealth Marine Reserve, in the Timor Sea region, northern Australia. However, the spatially continuous data of sponge species richness is not readily available, and the relationship is largely unknown. In this study, we modelled sponge species richness data of 77 samples using random forest (RF) and generalised linear model (glm) and their hybrid methods with geostatistical techniques (i.e. ordinary kriging (OK) and inverse distance weighting (IDW)) based on seabed biophysical variables. These methods are RF, RFOK, RFIDW, glm, glmok and glmidw that is a new hybrid method. We also examined effects of model averaging using four averaged methods (RFOKRFIDW, RFRFOKRFIDW, glmokglmidw and glmglmokglmidw) and the effects of various predictor sets on the accuracy of predictive models. Four feature selection methods, 1) averaged variable importance (AVI), 2) Boruta, 3) knowledge informed AVI (KIAVI) and 4) recursive feature selection (rfe), were used for RF; and four variable selection methods: 1) stepAIC, 2) dropterm, 3) anova and 4) RF, were employed to select glm predictive models. Predictive models were validated based on 10-fold cross validation. Finally the spatial distribution of sponge richness was predicted using the most accurate model and examined. The main findings are 1) the initial input predictors affect the status of important and unimportant variables; 2) AVI is not always reliable and KIAVI is recommended for selecting RF predictive model, 3) using Boruta can improve the accuracy in comparison with the full model, but it may lead to sub-optimal models; and features selected using rfe are not optimal and can be even misleading; 4) the accuracy of glm predictive model did not align with AIC, deviance explained (%) and deviance explained adjusted (%), suggesting that conventional model selection approaches for glm is unable to identify reliable predictive models; 5) joint application of RF and AIC is a useful model selection approach for developing glm predictive models; 6) the goodness of fit should not be used to assess glm predictive models; 7) the hybrid methods have significantly improved the predictive accuracy for both RF and glm; and the hybrid methods of RF and geostatistical methods are considerably more accurate and able to effectively model count data; and 8) the relationships of sponge species richness with the predictors are non-linear, and high sponge species richness is usually associated with hard seabed features. This study further confirms that: 1) the initial input predictors affect the model selection for RF; 2) the inclusion of highly correlated predictors could improve predictive accuracy, providing important guideline for pre-selecting predictors for RF; and 3) the effects of model averaging are method dependent or even data dependent. This study also provides important information for future monitoring design, particularly on the areas where the management and conservation of sponge gardens should be focused.

The Coompana Province is one of the most poorly understood pieces of crystalline basement geology in the Australian continent. It lies entirely concealed beneath a variable thickness of Neoproterozoic to Cenozoic sedimentary rocks, and is situated between the Gawler Craton to the east, the Musgrave Province to the north, and the Madura and Albany-Fraser Provinces to the west. A recently-acquired reflection seismic transect (13GA-EG1) provides an east-west cross-section through the southern part of the Coompana Province, and yields new insights into the thickness, seismic character and gross structural geometry within the Coompana Province. To assist geological interpretation of the 13GA-EG1 seismic line, new SHRIMP U-Pb zircon ages have been acquired from samples from the limited drill-holes that intersect the Coompana Province. New results from several granitic and gneissic rocks from the Coompana Province yield magmatic and/or high-grade metamorphic ages in the interval 1100 1200 Ma. Magmatic or high-grade metamorphic ages in this interval have not been identified in the Gawler Craton, in which the last major magmatic and metamorphic event took place at ~1590 1570 Ma. The Gawler Craton was largely unaffected by ~1100 1200 Ma events, as evidenced by the preservation of pre-1400 Ma 40Ar/39Ar cooling ages. In contrast, magmatic and metamorphic ages of 1100 1200 Ma are characteristic of the Musgrave Province (Pitjantjatjara Supersuite) and Madura Province (Moodini Supersuite). The new results from the Coompana Province have also yielded magmatic or inherited zircon ages at ~1500 Ma and ~1640 Ma. Once again, these ages are not characteristic of the Gawler Craton and no pre-1700 Ma inherited zircon has been identified in Coompana Province magmatic rocks, as might be expected if the province was underlain by older, Gawler Craton-like crust. The emerging picture from this study and recent work from the Madura Province and the Forrest Zone of the western Coompana Province is that the Coompana Province has a geological history that is quite distinct from, and generally younger than, the Gawler Craton to its east, but that is very similar to the Musgrave and Madura Provinces to the north and west. The contact between the Coompana Province and the Gawler Craton is interpreted in the 13GA-EG1 seismic line as a prominent west-dipping crustal-scale structure, termed the Jindarnga Shear Zone. The nature and timing of this boundary remain relatively poorly constrained, but the seismic and geochronological evidence suggests that it represents the western edge of the Gawler Craton, marking the western limit of an older, more isotopically evolved and multiply re-worked craton to the east, from a younger, more isotopically primitive crust that separates the South Australian Craton from the West Australian Craton.

The Southern Thomson Project was established to develop a better understanding of the geology and mineral potential of the southern Thomson Orogen. One way in which the Southern Thomson Project is improving this understanding is through the collection of seismic refraction data at 16 greenfields sites to assess the cover thickness (i.e. the amount of regolith and sedimentary basin cover overlying the basement geology). Seismic refraction data was collected using a standard linear array with 48 geophones and a 40 kg propelled weight drop as the energy source. An estimate of the cover thickness was produced from the refraction data using the time-term inversion method. This resulted in the creation of a three-layer model for each site, which accounts for the layers associated with the regolith, sedimentary basin cover and the basement geology.

Infographic for the Kaggle Methane leakage competition.