The Browse Basin on Australia's North West Shelf is a NE-trending Paleozoic to Cenozoic depocentre that contains more than 15 km of sediments. These sediments host significant hydrocarbon reserves, some of which are currently under development. The basin also has the potential to store large volumes of carbon dioxide. Recently-acquired aeromagnetic data over the Browse Basin provide new impetus for studies of the nature of basement, the role of structural inheritance and controls on the distribution of volcanic rocks. Initial interpretation of the new magnetic data has utilised magnetic source polygons and depth estimates derived from the tilt-angle filter. Exploration wells that intersect mainly volcanic flows or tuffaceous rocks tend to lie on or adjacent to source polygons. Computed tilt depths show that these sources tend to coincide with the depth to the top of volcanics in wells and that tilt depths extend deep into the basin (up to ~10 km). The magnetic susceptibility distribution inferred from minimally-constrained, regional-scale inversion models also indicates that magnetic anomalies arise from features deep in the basin and within basement. These results highlight the importance of understanding the role of volcanic rocks in basin evolution and their influence on reservoirs that may host hydrocarbons or that may be suitable for CO2 storage.

The accuracy of spatially continuous environmental data, usually generated from point samples using spatial prediction methods (SPMs), is crucial for evidence-informed environmental management and conservation. Improving the accuracy by identifying the most accurate methods is essential, but also challenging since the accuracy is often data specific and affected by multiple factors. Because of the high predictive accuracy of machine learning methods, especially random forest (RF), they were introduced into spatial statistics by combining them with existing SPMs, which resulted in new hybrid methods with improved accuracy. This development opened an alternative source of methods for spatial prediction. In this study, we introduced these hybrid methods, along with the modelling procedure adopted to develop the final predictive models. These methods were compared with the commonly used SPMs in R using cross-validation techniques based on both marine and terrestrial environmental data. We also addressed the following questions: 1) whether they are data-specific for marine environmental data, 2) whether input predictors affect their performance, and 3) whether they are equally applicable to terrestrial environmental data? This study provides suggestions and guidelines for the application of these hybrid methods to spatial predictive modelling not only in environmental sciences, but also in other relevant disciplines.

This project consists of data that has been reprocessed by RPS and AAM for the purpose of creating an improved Victorian coastal DEM including contours based on the original data acquired in 2007. The purpose of this project is to reclassify the original level 2 classification LiDAR data into level 3 for input to a higher accuracy ICSM Level 3 classification (Level 3 DEM). LiDAR (Light Detection and Ranging) is an airborne remote sensing technique for rapid collection of terrain data. The sensor used for this LiDAR project collected XYZ and Intensity data for first and last return by bouncing a pulse from the aircraft to the surface that enables the height and intensity values to be calculated. From the raw LiDAR data, a suite of elevation products was generated including DEM and Contours. Project Products: DEM, Contours, raw LiDAR.

Presentation given to the Flood Management Australia Flood Data and Insurance Workshop on 25 November 2015 on the copyright assignmnet issue preventing the sharing of flood risk information to the Australian community.

Product to promote the GA graduate program with the Geological Timescale inside, and with provision to attach a USB with GA videos.

These datasets cover approximately 3360 sq km of coastal areas of Northern and South-eastern Tasmania. The project covered three areas: - Greater Hobart 1283 square km - Huon Valley 460 square km - Launceston, Burnie, Devonport 1612 square km This project, undertaken by RPS Mapping on behalf of Geoscience Australia produced accurate LiDAR and derived products to ICSM specifications and medium format digital ortho-photo mosaics.

This dataset details the Declared Indigenous Protected Areas (IPA) across Australia through the implementation of the Indigenous Protected Areas Programme. These boundaries are not legally binding. An Indigenous Protected Area (IPA) is an area of Indigenous-owned land or sea where traditional Indigenous owners have entered into an agreement with the Australian Government to promote biodiversity and cultural resource conservation. The Indigenous Protected Areas element of the Caring for our Country initiative supports Indigenous communities to manage their land as IPAs, contributing to the National Reserve System. Further information can be found at the website below. http://www.environment.gov.au/indigenous/ipa/index.html Declared IPAs in order of gazettal date: Nantawarrina Preminghana Risdon Cove putalina Deen Maar Yalata Warul Kawa Watarru Walalkara Mount Chappell Island Badger Island Dhimurru Guanaba Wattleridge Mount Willoughby Paruku Ngaanyatjarra Tyrendarra Toogimbie Anindilyakwa Laynhapuy - Stage 1 Ninghan North Tanami Warlu Jilajaa Jumu Kaanju Ngaachi Great Dog Island Babel Island lungatalanana Angas Downs Pulu Islet Tarriwa Kurrukun Warddeken Djelk Jamba Dhandan Duringala Kurtonitj Framlingham Forest Kalka - Pipalyatjara Boorabee and The Willows Lake Condah Marri-Jabin (Thamurrurr - Stage 1) Brewarrina Ngemba Billabong Uunguu - Stage 1 Apara - Makiri - Punti Antara - Sandy Bore Dorodong Weilmoringle Yanyuwa (Barni - Wardimantha Awara) Minyumai Gumma Mandingalbay Yidinji Southern Tanami Angkum - Stage 1 Ngunya Jargoon Birriliburu Eastern Kuku Yalanji Bardi Jawi Girringun Wilinggin Dambimangari Balanggarra Thuwathu/Bujimulla Yappala Wardaman - Stage 1 Karajarri - Stage 1 Nijinda Durlga - Stage 1 Warraberalgahl and Porumalgal Kiwirrkurra Nyangumarta Warrarn Matuwa Kurrara-Kurrara

Australia's Identified Mineral Resources is an annual national assessment that takes a long-term view of Australian mineral resources likely to be available for mining. The assessment also includes evaluations of long-term trends in mineral resources, world rankings, summaries of significant exploration results and brief reviews of mining industry developments.

This Record contains new zircon U-Pb geochronological data obtained via Sensitive High-Resolution Ion Micro Probe (SHRIMP) from 19 samples of volcanic and plutonic igneous rocks of the central and eastern Lachlan Orogen, New South Wales. These data were obtained during the reporting period July 2013-June 2014, under the auspices of the collaborative Geochronology Project between the Geological Survey of New South Wales (GSNSW) and Geoscience Australia (GA), which is part of the National Geoscience Accord.

The Sea Level Monitoring Project was initially developed in the early 1990's (known at that time as the South Pacific Sea Level and Climate Monitoring Project) as a response to concerns expressed by South Pacific Forum Leaders about the potential impacts of global warming on sea levels and climate in the South Pacific. This Australian aid project was established with the goal of providing an accurate, long term record of sea levels in the South Pacific both for Forum countries and for the international scientific community that need such information to better understand how the Pacific oceanographic and meteorological environment is changing. During the 1990's a network of high resolution sea level and climate monitoring stations was established in the South West Pacific and observations from those stations were made available to stakeholders. In 2001, a Continuous Global Positioning System (CGPS) monitoring component was added and CGPS instruments were established near and linked to the sea level tide gauges stations in all partner countries so as to enable the measurement of vertical land movements. Vertical land motion at sea level stations can be equal or larger than the local absolute sea level signal, thus masking sea level change related information recorded at those stations. Therefore, the CGPS network is a crucial component for reliably determining absolute sea level change. This document reports the analysis results of CGPS coordinate time series from 2001 to 2013.