Predicted seabed gravel, mud and sand content in the Timor Sea region in the Australian continental EEZ 2018
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<p>This dataset provides the spatially continuous data of seabed gravel (sediment fraction >2000 µm), mud (sediment fraction < 63 µm) and sand content (sediment fraction 63-2000 µm) expressed as a weight percentage ranging from 0 to 100%, presented in 10 m resolution raster grids format and ascii text file.</p>
<p>The dataset covers the eight areas in the Timor Sea region in the Australian continental EEZ.</p>
<p>This dataset supersedes previous predictions of sediment gravel, mud and sand content for the basin with demonstrated improvements in accuracy. Accuracy of predictions varies with sediment types, with a VEcv = 71% for mud, VEcv = 72% sand and VEcv = 42% for gravel. Artefacts occur in this dataset as a result of noises associated predictive variables (e.g., horizontal and vertical lines resulted from predictive variables derived from backscatter data are the most apparent ones). To obtain the most accurate interpretation of sediment distribution in these areas, it is recommended that noises with backscatter data should be reduced and predictions updated.</p>
<p>This research is supported by the National Environmental Science Program (NESP) Marine Biodiversity Hub through Project D1.
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Sediment samples were exported from Geoscience Australia’s Marine Sediments database (MARS), an Oracle database developed by Geoscience Australia in line with ANZLIC data standards. These samples were collected during surveys SOL4934, SOL5117 and SOL5650. Predicting the spatial distribution of gravel, mud and sand content at a 10 m resolution was undertaken using a combined method of random forest and ordinary kriging (rfok) for mud and gravel, and using the average of rfok and the hybrid method of random forest and inverse distance weighting (RFOKRFIDW) in an R package, spm (see Li 2018). The spatial predictive models used were selected from all methods in spm based on their predictive accuracy for each sediment type (i.e., gravel, mud and sand) (Li 2016 and 2017, Li et al. 2016 and 2017). The predictions in raster grids and ascii text file were generated using spm in R. Final file is in utm 52s with a 10 m spatial resolution. File sizes are approximately 2.6 GB (raster grids) and 2.6 GB (ascii text) for the seabed sediments (i.e., gravel, mud and sand).