Geoscience Australia currently uses two commercial petroleum system modelling software packages, PetroMod https://www.software.slb.com/products/petromod and Zetaware http://www.zetaware.com, to undertake burial and thermal history modelling on wells in Australian sedimentary basins. From the integration of geological (age-based sedimentary packages, uplift and erosional events), petrophysical (porosity, permeability, and thermal conductivity) and thermal (downhole temperature, heat flow, vitrinite reflectance, and Tmax) input data, to name the most significant, a best-fit model of the time-temperature history is generated. Since the transformation of sedimentary organic matter (kerogen) into petroleum (oil and gas) is a chemical reaction, it is governed by chemical kinetics i.e. time and temperature (in the geological setting pressure is of secondary importance). Thus, the use of chemical kinetics associated with a formation-specific, immature potential source rock (where available) from the basin of interest is considered a better practical approach rather than relying on software kinetic defaults, which are generally based on the chemical kinetics determined experimentally on Northern Hemisphere organic matter types. As part of the Australian source rock and fluids atlas project being undertaken by the Energy Systems Group’s Exploring for the Future (EFTF) program, compositional kinetics (1-, 2-, 4- and 14-component (phase) kinetics) were undertaken by GeoS4, Germany. The phase kinetics approach is outlined in Appendix 1. This report provides the compositional kinetics for potential source rocks from the Ordovician Goldwyer (Dapingian–Darriwilian) Formation and the Bongabinni (Sandbian) Formation, Carribuddy Group, Canning Basin, Western Australia.

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 report presents the results of an elemental and carbon and oxygen isotope chemostratigraphy study on three historic wells; Kidson-1, Willara-1 and Samphire Marsh-1, from the southern Canning Basin, Western Australia. The objective of this study was to correlate the Early to Middle Ordovician sections of the three wells to each other and to wells with existing elemental and carbonate carbon isotope chemostratigraphy data from the Broome Platform, Kidson and Willara sub-basins, and the recently drilled and fully cored stratigraphic Waukarlycarly 1 well from the Waukarlycarly Embayment.

This animation shows how passive seismic 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 animation includes a simplified view of what passive seismic 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.

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.

Australia has a significant number of surface sediment geochemical surveys that have been undertaken by industry and government over the past 50 years. These surveys represent a vast investment and have up to now only been able to be used in isolation, independently from one another. The key to maximising the full potential of these data and the information they provide for mineral exploration, environmental management and agricultural purposes is using all the surveys together, seamlessly. These disparate geochemical surveys not only sampled various landscape elements and analysed a range of size fractions, but also used multiple analytical techniques, instrument types and laboratories. The geochemical data from these surveys require levelling to eliminate, as much as possible, non-geological variation. Using a variety of methodologies, including reanalysis of both international standards and small subsets of samples from previous surveys, we have created a seamless surface geochemical map for northern Australia, from nine surveys with 15,605 samples. We tested our approach using two surveys from the southern Thomson Orogen, which demonstrated the successful removal of inter-laboratory and other analytical variation. Creation of the new combined and levelled northern Australian dataset paves the way for the application of statistical and data analytics techniques, such as principal component analysis and machine learning, thereby maximising the value of these legacy data holdings. The methodology documented here can be applied to additional geochemical datasets as they become available.

This Record presents 40Ar/39Ar chronologic results acquired in support of collaborative regional geoscientific investigations and mapping programs conducted by Geoscience Australia (GA) and the Northern Territory Geological Survey (NTGS). Argon isotopic data and interpretations from hornblende, muscovite, and biotite from seven samples collected from the Aileron Province in ALCOOTA , HUCKITTA, HALE RIVER, and ILLOGWA CREEK in the Northern Territory are presented herein. The results complement pre-existing geochronological constraints from U–Pb zircon and monazite analyses of the same or related samples, and provide new constraints on the thermal and deformation history of the Aileron Province. Three samples (2003082017, 2003082021, 2003083040) were taken from ALCOOTA in the northeastern portion of the Aileron Province. Biotite in sample 2003082017 from the ca 1.81 Ga Crooked Hole Granite records cooling below 320–280°C at 441 ± 5 Ma. Biotite in sample 2003082021 from the ca 1.73 Ga Jamaica Granite records cooling below 320–280°C at or after 414 ± 2 Ma. Muscovite in sample 2003083040 from the Delny Metamorphics, which were deposited after ca 1.82 Ga and preserve evidence for metamorphism at ca 1.72 Ga and 1.69 Ga, records cooling below 430–390°C at 399 ± 2 Ma. The fabrics preserved in the samples from the Crooked Hole Granite and Delny Metamorphics are interpreted to have formed due to dynamic metamorphism related to movement on the Waite River Shear Zone, an extension of the Delny Shear Zone, during the Palaeoproterozoic. Portions of the northeastern Aileron Province are unconformably overlain by the Neoproterozoic–Cambrian Georgina Basin, indicating these samples were likely at or near the surface by the Neoproterozoic. Together, these data indicate that rocks of the Aileron Province in ALCOOTA were subjected to heating above ~400°C during the Palaeozoic. Two samples (2003087859K, 2003087862F) of exoskarn from an indeterminate unit were taken from drillhole MDDH4 in the Molyhil tungsten–molybdenum deposit in central HUCKITTA. The rocks hosting the Molyhil tungsten–molybdenum deposit are interpreted as ca 1.79 Ga Deep Bore Metamorphics and ca 1.80 Ga Yam Gneiss. They experienced long-lived metamorphism during the Palaeoproterozoic, with supersolidus metamorphism observed until at least ca 1.72 Ga. Hornblende from sample 2003087859K indicates cooling below 520–480°C by 1702 ± 5 Ma and may closely approximate timing of skarn-related mineralisation at the Molyhil deposit; hornblende from sample 2003087862F records a phase of fluid flow at the Molyhil deposit at 1660 ± 4 Ma. The Salthole Gneiss has a granitic protolith that was emplaced at ca 1.79 Ga, and experienced alteration at ca 1.77 Ga. Muscovite from sample 2010080001 of Salthole Gneiss from the Illogwa Shear Zone in ILLOGWA CREEK records cooling of the sample below ~430–390°C at 327 ± 2 Ma. This may reflect the timing of movement of, or fluid flux along, the Illogwa Shear Zone. An unnamed quartzite in the Casey Inlier in HALE RIVER has a zircon U–Pb maximum depositional age of ca 1.24 Ga. Muscovite from sample HA05IRS071 of this unnamed quartzite yields an age of 1072 ± 8 Ma, which likely approximates, or closely post-dates, the timing of deformation in this sample; it provides the first direct evidence for a Mesoproterozoic episode of deformation in this part of the Aileron Province.

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.

<div>This record one in a series of reports detailing the geochemical and mineralogical results of sampling collected at mine waste sites across Australia as part of Geoscience Australia's Exploring for the Future program. It presents new data and information regarding the tenor and deportment of indium, gallium, germanium, cadmium, antimony, and bismuth, as well as silver, lead, zinc, and copper at the Zeehan tailings site in western Tasmania.</div><div><br></div><div>Geoscience Australia’s Exploring for the Future program provides precompetitive information to inform decision-making by government, community and industry on the sustainable development of Australia's mineral, energy and groundwater resources. By gathering, analysing and interpreting new and existing precompetitive geoscience data and knowledge, we are building a national picture of Australia’s geology and resource potential. This leads to a strong economy, resilient society and sustainable environment for the benefit of all Australians. This includes supporting Australia’s transition to net zero emissions, strong, sustainable resources and agriculture sectors, and economic opportunities and social benefits for Australia’s regional and remote communities. The Exploring for the Future program, which commenced in 2016, is an eight year, $225m investment by the Australian Government.</div><div><br></div>