Random forest (RF) is one of the top performed methods in predictive modelling. Because of its high predictive accuracy, we introduced it into spatial statistics by combining it with the existing spatial interpolation methods, resulting a few hybrid methods and improved prediction accuracy when applied to marine environmental datasets (Li et al., 2011). The superior performance of these hybrid methods was partially attributed to the features of RF, one component of the hybrids. One of these features inherited from its trees is to be able to deal with irrelevant inputs. It is also argued that the performance of RF is not much influenced by parameter choices, so the hybrids presumably also share this feature. However, these assumptions have not been tested for the spatial interpolation of environmental variables. In this study, we experimentally examined these assumptions using seabed sand and gravel content datasets on the northwest Australian marine margin. Four sets of input variables and two choices of 'number of variables randomly sampled as candidates at each split' were tested in terms of predictive accuracy. The input variables vary from six predictors only to combinations of these predictors and derived variables including the second and third orders and/or possible two-way interactions of these six predictors. However, these derived predictors were regarded as redundant and irrelevant variables because they are correlated with these six predictors and because RF can do implicit variable selection and can model complex interactions among predictors. The results derived from this experiment are analysed, discussed and compared with previous findings. The outcomes of this study have both practical and theoretical importance for predicting environmental variables.

This record is a review and synthesis of geological research undertaken along the south western margin of Australia. The record has been written in support of regional marine planning and provides fundamental baseline scientific information for the South Western Marine Planning Area.

This record is a review and synthesis of geological research undertaken along the northern margin of Australia. The record has been written in support of regional marine planning and provides fundamental baseline scientific information for the Northern Planning Area.

From 1995 to 2000 information from the federal and state governments was compiled for Comprehensive Regional Assessments (CRA), which formed the basis for Regional Forest Agreements (RFA) that identified areas for conservation to meet targets agreed by the Commonwealth Government with the United Nations. These 3 CDs were created as part of GA's contribution to the SW Western Australia CRA. CD1 contains final versions of all data coverages, images and shapefiles used in the project. CD2 contains the final CRA report, Executive Summary, and associated maps and figures in Arcinfo (.gra), postscript (.ps) and Web ready (.gif) formats. CD3 contains the final Minerals Assessment report and associated maps and figures in Arcinfo, postscript and Web ready formats.

The Vlaming Sub-basin Marine Survey GA-0334 was undertaken in March and April 2012 as part of the Commonwealth Government's National CO2 Infrastructure Plan (NCIP). The purpose was to acquire geophysical and biophysical data to help identify sites suitable for the long term storage of CO2 within reasonable distances of major sources of CO2 emissions. This dataset contains identifications of animals collected from 32 Van Veen grabs deployed during GA-0334. Sediment was elutriated for ~ 5 minutes over a 500um sieve. Retained sediments and animals were then preserved in 70% ethanol for later laboratory sorting and identification (see `lineage'). During sorting, all worms were separated and sent to Infaunal Data Pty Ltd (Lynda Avery) for identification to species or operational taxonomic unit (OTU). Lynda Avery completed identifications on 17 April 2013, and specimens were lodged at the Museum of Victoria. All other taxa were identified to morphospecies at GA by an ecologist. Gray shading indicates taxa identified to species level by Lynda Avery (Refer to GeoCat # 76463 for raw data of species identifications by taxonomist); all other taxa were identified to morphospecies. Data is presented here exactly as delivered by the taxonomist/ecologist, and Geoscience Australia is unable to verify the accuracy of the taxonomic identifications. Stations are named XXGRYY where XX indicates the station number, GR indicates Van Veen grabs, and YY indicates the sequence of grabs deployed (i.e. the YYth grab on the entire survey). H indicates heavy fraction animals and HS indicates animals found on a sponge. The dataset is current as of November 2014, but will be updated as taxonomic experts contribute. See GA Record 2013/09 for further details on survey methods and specimen acquisition.

Geoscience Australia is supporting the exploration and development of offshore oil and gas resources and establishment of Australia's national representative system of marine protected areas through provision of spatial information about the physical and biological character of the seabed. Central to this approach is prediction of Australia's seabed biodiversity from spatially continuous data of physical seabed properties. However, information for these properties is usually collected at sparsely-distributed discrete locations, particularly in the deep ocean. Thus, methods for generating spatially continuous information from point samples become essential tools. Such methods are, however, often data- or even variable- specific and it is difficult to select an appropriate method for any given dataset. Improving the accuracy of these physical data for biodiversity prediction, by searching for the most robust spatial interpolation methods to predict physical seabed properties, is essential to better inform resource management practises. In this regard, we conducted a simulation experiment to compare the performance of statistical and mathematical methods for spatial interpolation using samples of seabed mud content across the Australian margin. Five factors that affect the accuracy of spatial interpolation were considered: 1) region; 2) statistical method; 3) sample density; 4) searching neighbourhood; and 5) sample stratification by geomorphic provinces. Bathymetry, distance-to-coast and slope were used as secondary variables. In this study, we only report the results of the comparison of 14 methods (37 sub-methods) using samples of seabed mud content with five levels of sample density across the southwest Australian margin. The results of the simulation experiment can be applied to spatial data modelling of various physical parameters in different disciplines and have application to a variety of resource management applications for Australia's marine region.

Between March 2008 and August 2009, 65,445 tonnes of ~75 mol% CO2 gas were injected in a depleted natural gas reservoir approximately 2000 m below surface at the Otway project site in Victoria, Australia. Groundwater flow and composition were monitored biannually in 2 near-surface aquifers between June 2006 and March 2011, spanning the pre-, syn- and post-injection periods. The shallow (~0-100 m), unconfined, porous and karstic aquifer of the Port Campbell Limestone and the deeper (~600-900 m), confined and porous aquifer of the Dilwyn Formation contain valuable fresh water resources. Groundwater levels in either aquifer have not been affected by the drilling, pumping and injection activities that were taking place, or by the rainfall increase observed during the project. In terms of groundwater composition, the Port Campbell Limestone groundwater is fresh (electrical conductivity = 801-3900 ?S/cm), cool (temperature = 12.9-22.5 C), and near-neutral (pH 6.62-7.45), whilst the Dilwyn Formation groundwater is fresher (electrical conductivity 505-1473 ?S/cm), warmer (temperature = 42.5-48.5 C), and more alkaline (pH 7.43-9.35). Evapotranspiration and carbonate dissolution control the composition of the groundwaters. Comparing the chemical and isotopic composition of the groundwaters collected before, during and after injection shows either no sign of statistically significant changes or, where they are statistically significant, changes that are generally opposite those expected if CO2 addition had taken place. The monitoring program demonstrates that the physical and chemical properties of the groundwaters at the sampled bores have not been affected by CO2 sequestration.

This record gives a brief account of the conditions encountered in a geological reconnaissance of the south-western portion of the Canning Basin - an area covered mostly by sand and seif dune, interspersed by scattered low rock outcrops.

This dataset contains species identifications of molluscs collected during survey SOL5117 (R.V. Solander, 30 July - 27 August, 2010). Animals were collected from the Joseph Bonaparte Gulf with a benthic sled (SL) and Smith McIntyre grab (GR). Specimens were lodged at Northern Territory Museum on the 27 August 2010. Species-level identifications were undertaken by Richard Willan at the Northern Territory Museum and were delivered to Geoscience Australia on the December 2010 (for large samples) and 26 June 2012 (for smaller molluscs from grabs). See GA Record 2011/08 for further details on survey methods and specimen acquisition. Data is presented here exactly as delivered by the taxonomist, and Geoscience Australia is unable to verify the accuracy of the taxonomic identifications. Comments: The following comments relate to live-taken specimens only: 1. The SOL5117 molluscan samples contain at least one new species (Talabrica sp.), one new record for Australia (Oliva rufofulgurata), and five new records for Commonwealth waters north of the Northern Territory (Strombus hickeyi, Trigonostoma textilis, Dentalium formosum, Phyllidiopsis shireeenae, Ceratosoma trilobatum). 2. Many of the molluscan species in the SOL5117 grab samples, both live individuals and dead shells, are represented only by tiny juveniles, so identification to species level is not possible because the shell characters change considerably as the species reaches maturity. 3. Clearly the majority of molluscs in the SOL5117 samples are represented by dead shells only. 4. Species richness is far higher than suggested by these samples. Judging from the range of species present in the SOL4934 and SOL5117 samples plus the accumulation of species through the samples, the molluscan biodiversity in this area would be between 400 and 500 species, the great majority micromolluscs (i.e., < 5 mm in greatest dimension). 5. The SOL5117 molluscan samples are not as comprehensive as the earlier SOL4934 samples taken in the same areas(s). 6. The SOL5117 molluscan samples provide us with hardly any picture of the composition or abundance of molluscs within or between the sites. 7. The SOL5117 molluscan samples should not be used to assess the conservation status of the submarine communities in the area(s) sampled. 8. More targeted and intensive sampling is required to appropriately measure molluscan diversity, abundance and communities in this region. ~ R Willan

The National Geochemical Survey of Australia (NGSA) project (www.ga.gov.au/ngsa) was part of Geoscience Australia's Onshore Energy Security Program 2006-2011 and was carried out in collaboration with the geological surveys of all States and the Northern Territory. It delivered (1) Australia's first national geochemical atlas, (2) an underpinning geochemical database, and (3) a series of reports. Catchment outlet sediments (similar to floodplain sediments in most cases) were sampled in 1186 catchments covering ~80% of the country (average sample density 1 sample per 5500 km2). Samples were collected at 2 depths each sieved to 2 grain size fractions. Chemical analyses carried out on the samples fall into 3 main categories: (1) total (using mainly XRF and total digestion ICP-MS), (2) aqua regia, and (3) Mobile Metal Ion® element contents. Here results of robust multi-variate analysis performed on the total element content data are reported. After clr-transformation of the raw data, a principal components (PCs) analysis was undertaken. The first five PCs account for 59.2% of the total variance. For instance, PC1 (27.9%) is dominated by Mg, Ca, S and Sr (negative loadings) and rare earth elements and Y (positive loadings). Thus, the PC1 map should reflect the distribution of carbonates and gypsum as well as resistate minerals typical of highly weathered environments and heavy mineral sands. By comparing this to known occurrences of rock and soil types, weathering regimes, etc. inferences can be drawn about the major, continental-scale processes influencing the distribution of chemical elements in Australia's surficial environment.