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.

Short conference Abstract for Australian Earth Sciences Convention

Infographic for the Kaggle Methane leakage competition.

Several belts of poorly-exposed igneous rocks occur in the Grampians-Stavely Zone of western Victoria, close to the interpreted Cambrian east Gondwana continental margin. Previous geochemical studies on the outcropping igneous rocks around Mount Stavely, Mount Dryden and in the Black Range have recognised characteristics similar to those found in modern magmatic arcs. These rocks are collectively considered to form part of a single Middle to Late Cambrian arc system, referred to as the Stavely Arc. While outcropping examples of the Stavely Arc magmas are well studied, the character of other (likely) arc-related rocks imaged by magnetic data beneath recent, thin cover has remained enigmatic. New geochemical data from a recent stratigraphic drilling program, together with analysis of rocks from government and industry drill holes has allowed for a more complete understanding of the Stavely Arc package. A range of rock associations have been recognised, including low-Ti boninite-like rocks, back-arc-related tholeiitic rocks, adakitic porphyry intrusives, serpentinites, and highly-depleted mafic to intermediate volcanics and intrusives. The majority of arc-related rocks comprise low- to high-K calc-alkaline basalt, andesite, dacite, and geochemically-related quartz diorite, which display similar N-MORB-normalised trace element patterns, LREE-enriched REE patterns and moderately evolved to weakly juvenile Nd isotopic compositions (Nd 500 Ma = -3.95 to +0.46). High-Al basalts intersected during stratigraphic drilling also show weakly-developed calc-alkaline compositions. However, these are distinguished from the other calc-alkaline rocks by higher Al2O3, N-MORB-like trace element patterns, relatively flat REE patterns and much more juvenile Nd isotopic compositions (Nd 500 Ma = +4.73 to +6.33). High-Al basalts are spatially associated with boninites intersected by mineral exploration drilling. The earliest geochronological evidence for Stavely Arc magmatism is provided by an isotopically juvenile felsic intrusive with an interpreted arc-related origin dated at ~510 Ma. This age is synchronous with tholeiitic dolerite from the western Grampians-Stavely Zone interpreted to have been emplaced in a back-arc extensional setting. Available ages for volcanic rocks of the Stavely Arc are only known from the Mount Stavely Belt, and show that arc magmatism reached maturity around ~505-500 Ma. Overall geochemical systematics suggest that the majority of calc-alkaline rocks of the Stavely Arc have affinities with modern island arcs with (limited) continental crust involvement. It is unlikely that the thickness of any pre-existing Precambrian crust was great, given the Nd isotopic compositions and lack of inherited Mesoproterozoic or older zircons. In comparison, the more juvenile isotopic characteristics, weakly-developed subduction-related features, and spatial association with boninites of the high-Al basalts are more consistent with a more primitive arc setting, and may represent an (early?) phase of Stavely Arc magmatism in which there was insignificant crustal involvement. Similar geochemical characteristics, ages, and inferred tectonic setting are consistent with the Stavely Arc forming part of a larger Middle to Late Cambrian arc system that also includes the Mount Wright Arc in New South Wales and the Jamison Volcanic Group (Selwyn Block) in central Victoria.

SUMMARY Geoscience Australia operates and maintains a state-of-the-art network of stations and sophisticated instrumentation that monitors natural and anthropogenic (human-made) hazards in Australia and around the globe through its Geophysical Network Section. The key responsibilities for the Geophysical Network Section are to: operate and maintain the Australian National Seismic Network (ANSN) and Urban Monitoring (UM) networks; operate and maintain Australian Comprehensive Nuclear-Test-Ban Treaty (CTBT) seismic, hydro-acoustic and infrasound technologies, as part of Australia's commitment to support monitoring of worldwide nuclear testing; operate and maintain a national network of geomagnetic observatories which forms a part of a global observatory network; provide technical expertise and advice to Geoscience Australia projects, such as the National Geospatial Reference Systems Risk Research Group and the JATWS (Joint Australian Tsunami Warning System); and, provide technical and operational support to the Risk Research Group for significant Australian earthquake events and aftershock deployment studies. Geophysical data archives are stored on-site and can be freely downloaded from GA or international data centres. Seismic data can be accessed at GA and Incorporated Research Institutions for Seismology (IRIS) and geomagnetic data at INTERMAGNET. Seismic data from Geoscience Australia's Geophysical Networks feeds into important hazard maps including the probabilistic national earthquake hazard map and the probabilistic Tsunami hazard map. Geomagnetic data feeds into the International Geomagnetic Reference Field and has been used to develop the first 3-D conductivity map of Australia. Key words: Geophysical Networks, geomagnetism, earthquake, tsunami, nuclear monitoring

This report summarises the outcomes of the NPI-AB's second meeting in July 2015.

This report identifies membership of the NPI-AB and summarises outcomes of the Board's first meeting held on 5th March 2015.

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.

Can you help the Geoscience Australia Library? We are seeking the field notebooks of any geologists who worked for the Bureau of Mineral Resources (BMR) in Antarctica, especially those from the 1950s-80s, to include in our digitisation project. Tucked away in archive boxes in the basement compactus of the Geoscience Australia Library in Canberra, lie over 3500 geol ogical field notebooks. These notebooks contain the observations of BMR geologists from the 1940s onwards as they worked their way across Australia, parts of Papua New Guinea and Pakistan, and the Australian Antarctic Territory. Around 100 Antarctic field notebooks are the focus of a pilot digitisation project to improve access to the rich data they contain and ensure they are preserved for future generations to use.

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.