From 1 - 10 / 39
  • The Exploring for the Future program is an initiative by the Australian Government dedicated to boosting investment in resource exploration in Australia. The initial phase of this program led by Geoscience Australia focussed on northern Australia to gather new data and information about the potential mineral, energy and groundwater resources concealed beneath the surface. The northern Lawn Hill Platform is an intracratonic poly-phased history region of Paleoproterozoic to Mesoproterozic age consisting of mixed carbonates, siliciclastics and volcanics. It is considered a frontier basin with very little petroleum exploration to date, but with renewed interest in shale and tight gas, that may present new exploration opportunities. An understanding of the geochemistry of the sedimentary units, including the organic richness, hydrocarbon-generating potential and thermal maturity, is therefore an important characteristic needed to understand the resource potential of the region. As part of this program, Rock-Eval pyrolysis analyses were undertaken by Geoscience Australia on selected rock samples from 2 wells of the northern Lawn Hill Platform.

  • Geoscience Australia, CSIRO, and the Australian Space Agency collaboratively developed a 2-page A4 flyer to promote education and careers in space to students and teachers. The flyer showcases Australia's unique capability in the space sector, far beyond astronomers and astronauts. It also lists QR codes of several Australian educational resources on a diversity of space topics for preschoolers through to university students. It is designed to be shared virtually or in person with stakeholders interested in promoting space science literacy and careers.

  • As part of the Onshore Energy Systems Group’s program, organic maturation levels were determined using polar compounds from potential source rocks from the Georgina and Canning basins. The Early Paleozoic organic matter is devoid of the vitrinite maceral so unsuitable of the measurement of the industry-standard vitrinite reflectance (Ro%) measurement.

  • This animation shows how Airborne Electromagnetic Surveys Work. It is part of a series of Field Activity Technique Engagement Animations. The target audience are the communities that are impacted by our data acquisition activities. There is no sound or voice over. The 2D animations include a simplified view of what AEM equipment looks like, what the equipment measures and how the survey works.

  • Weathering intensity or the degree of weathering 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. 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.

  • Well and seismic correlation schemes exist for the Western Australian and South Australian parts of the Officer Basin but there are inconsistencies between the western and eastern regions. Hence, as part of the Exploring for the Future Officer-Musgrave Project, a chemostratigraphic correlation has been determined for the sedimentary fill of the Officer Basin with emphasis on Neoproterozoic to Cambrian rocks. The correlations have been developed on whole rock inorganic geochemical data obtained from the analysis of 10 study wells which span the basin from Western Australia and into South Australia. A total of 8 chemostratigraphic mega-sequences (MS) are recognised across the basin, that in turn are subdivided into a total of 24 chemostratigraphic sequences. MS1 to MS6 include the Neoproterozoic to Cambrian sedimentary rocks and are the focus of this study. The Neoproterozoic–Cambrian mega-sequences MS1 to MS4 broadly correspond to the previously defined Centralian supersequences CS1 to CS4 and provide robust well-control to the regional seismic correlations. Confidence in the correlation of these old rocks are important since they contain both potential source and reservoir rocks for petroleum generation and accumulation. MS7 is equivalent to the Permian Paterson Formation, while MS8 is equivalent to the Mesozoic section. The elemental data has also been used to elucidate aspects of the petroleum system by characterising reservoirs and identifying fine-grained siliciclastics deposited in anoxic environments which may have source potential. This work is expected to further improve geological knowledge and reduce the energy exploration risk of the Officer Basin, a key focus of this program. <b>Citation:</b> Edwards D.S., Munday S., Wang L., Riley D. & Khider K., 2022. Neoproterozoic and Cambrian chemostratigraphic mega-sequences of the Officer Basin; a regional framework to assist petroleum and mineral exploration. In: Czarnota, K. (ed.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, https://dx.doi.org/10.26186/146285

  • The Exploring for the Future program is an initiative by the Australian Government dedicated to boosting investment in resource exploration in Northern Australia. The Paleo- to Mesoproterozoic sedimentary and volcanic sequences of the Mount Isa–McArthur Basin region of Northern Territory and Queensland are host to a range of world class mineral deposits (Hutton et al., 2012) and include the basin-hosted base metal deposits of the North Australian Zinc Belt, the world’s richest belt of zinc deposits (Huston et al., 2006; Large et al., 2005). The region demonstrably has potential for additional world class mineral systems (Hutton et al. 2012), as well as potential to host shale gas plays (Gorton & Troup, 2018). An improved understanding of the chemistry of the host sedimentary units, including associated volcanic and intrusive rocks (potential metal source rocks) within these regions is therefore an important requisite to further understand the resource potential of the region. To assist in this we have undertaken a multi-year campaign (2016-2019) of regional geochemical sampling of geological units in the southeastern McArthur Basin, it’s continuation into the Tomkinson Province, and the Lawn Hill Platform regions of Northern Territory and northwest Queensland. Chief aims of the project were to characterise, as much as possible, the inorganic geochemistry of units of the Paleoproterozoic Tawallah, McArthur, Fickling and McNamara Groups and the Mesoproterozoic Roper and South Nicholson groups, with most emphasis on the Tawallah, McNamara and Fickling Groups. Minimal attention was paid to units of the McArthur Group which have been extensively previously sampled. The project also involved exploratory geochemical characterisation of sedimentary and igneous rocks from Paleoproterozoic and Mesoproterozoic rocks of the Tomkinson Province (Tomkinson, Namerinni and Renner groups) in Northern Territory. Minimal regional geochemical data exists for these rocks which are considered time equivalents of the Tawallah, McArthur, Nathan and Roper groups. The approach followed was based on targeting as many units as possible from drill core held within the core repository facilities of the Northern Territory and Queensland Geological surveys. Sampling strategy for individual units was based on targeting all lithological variability with particular emphasis on units not previously extensively sampled. Units were sampled at moderate to high resolution, with sampling density ranging from one sample per ~10 m intervals in organic rich intervals or lithological variable units, up to one sample per 20 to 50 m intervals in lithologically-monotonous units or in units recently sampled recently by GA or others. This data release contains the results of elemental analyses (XRF, ICP-MS), ferrous iron oxide content (FeO) and Loss-on-ignition (LOI) on 805 samples selected from 42 drill cores housed in the Geological Survey of Northern Territory’s Darwin and Alice Springs core repositories and in the Geological Survey of Queensland’s Brisbane and Mount Isa core repositories. Drillholes sampled include the Amoco holes DDH 83-1, DDH 83-2, DDH 83-3, DDH 83-4, and DDH 83-5, as well as 14MCDDH001, 14MCDDH002, 87CIIDH1, 87CIIDH2, Bradley 1, Broughton 1, DD81CY1, DD91RC18, DD91DC1, DD91HC1, DD95GC001, GCD-1, GCD-2A, GSQ Lawn Hill 3, GSQ Lawn Hill 4, GSQ Westmoreland 2, MWSD05, ND1, ND2, 12BC001, and Willieray (1DD, 3DD, 8DD), Hunter (1DD, 2DD, 3DD) and HSD001, HSD002 holes from the Tomkinson Province. The data also include a small number of non-basin samples (from drill holes AAI POTALLAH CREEK 1, ADRIA DOWNS 1, Bradley 1, GSQ Normanton 1, GSQ Rutland Plains 1, MULDDH001 and MURD013), collected at the same time, largely for isotopic studies. The resultant geochemical data was largely generated at the Inorganic Geochemistry Laboratory at Geoscience Australia (509 of the 805 analyses), with two batches (296 samples) analysed by Bureau Veritas in Perth. Eighteen samples analysed at GA were also reanalysed at Bureau Veritas for QA/QC purposes. All data was collected as part of the Exploring for the Future program. The report also includes a statistical treatment of the geochemical data looking at laboratory performance, based on certified reference material (CRMs) and sample duplicates, and interlaboratory agreement, based on samples analysed at both laboratories. Results show accuracies were within acceptable tolerances (±2 SD) for the majority of major and trace elements analysed at both laboratories. Notable exceptions included significant negative bias for Fe2O3 and positive bias for Na2O at Geoscience Australia. The results also showed that Mo (and As and Be) measurements were a consistent problem at GA, and Zn a consistent problem at BV. Precision (reproducibility) for major elements at both laboratories was very good, generally between 1 to 5%. Precisions for trace elements, varied from generally 5% or better at Geoscience Australia, and mostly between 5 and 10% for Bureau Veritas. Importantly, agreement between laboratories was good, with the majority of elements falling within ±5% agreement, and a few within 5-10% (Th, Tb, Sr, Zn, Ta, and Cr). Major exceptions to this included Na2O, K2O, Rb, Ba and Cs, as well as P2O5 and SO3, as well as those trace elements commonly present in low concentrations (e.g., Cu, As, Be, Mo, Sb, Ge, Bi). The mismatch between the alkalis is notable and of concern, with differences (based on median values) of 17% and 22% for K2O and Ba (higher at Bureau Veritas) and 32% and 300% for Ba and Na2O (higher at Geoscience Australia). The geochemical data presented here have formed the basis for ongoing studies into aspects of basin-hosted mineral systems in the McArthur–Mount Isa region, including insights into sources of metals for such deposits and delineating alteration haloes around those deposits (Champion et al., 2020a, b).

  • Geochemical surveys conducted by BMR since 1980 in the southern Kakadu region have highlighted the natural occurrence in specific areas of well above crustal concentrations of uranium, thorium, arsenic, mercury and lead. The natural levels of concentration in the land and possibly the water systems of the South Alligator Valley area could constitute an environmental hazard. A large part of this area coincides with the area delineated as the "sickness country". SUBMISSION TO THE RESOURCE ASSESSMENT COMMISSION BY THE BUREAU OF MINERAL RESOURCES, GEOLOGY AND GEOPHYSICS.

  • This report presents the results of chemostratigraphic analyses for samples of the Waukarlycarly 1 deep stratigraphic well drilled in in the Waukarlycarly Embayment of the Canning Basin. The drilling of the well was funded by Geoscience Australia’s Exploring for the Future initiative to improve the understanding of the sub-surface geology of this underexplored region of the southern Canning Basin. The well was drilled in partnership with Geological Survey of Western Australia (GSWA) as project operator. Waukarlycarly 1 reached a total depth (TD) of 2680.53 m at the end of November 2019 and was continuously cored from 580 mRT to TD. The work presented in this report constitutes part of the post-well data acquisition. An elemental and isotope chemostratigraphic study was carried out on 100 samples of the well to enable stratigraphic correlations to be made across the Canning Basin within the Ordovician section known to host source rocks. Nine chemostratigraphically distinct sedimentary packages are identified in the Waukarlycarly 1 well and five major chemical boundaries that may relate to unconformities, hiatal surfaces or sediment provenance changes are identified. The Ordovician sections in Waukarlycarly 1 have different chemical signals in comparison to those in other regional wells, suggestive of a different provenance for the origin of the sediments in the Waukarlycarly Embayment compared to the Kidson Sub-basin (Nicolay 1) and Broome Platform (Olympic 1).

  • 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.