Seabed gravel, mud and sand content in the Browse region in the Australian continental EEZ 2014

Full description

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 0.0025 decimal degree (dd) resolution raster grids format and ascii text file.

The dataset covers the Browse region in the Australian continental EEZ.

This dataset supersedes previous predictions of sediment gravel, mud and sand content for the basin with demonstrated improvements in accuracy. Accuracy of predictions varies based on density of underlying data and level of seabed complexity. Artefacts occur in this dataset as a result of insufficient samples in relevant regions. This dataset is intended for use at the basin scale. The dataset may not be appropriate for use at smaller scales in areas where sample density is insufficient to detect local variation in sediment properties. To obtain the most accurate interpretation of sediment distribution in these areas, it is recommended that additional samples be collected and predictions updated.

Lineage

Maintenance and Update Frequency: notPlanned

Statement: 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. A subset of this data was selected for use in predicting spatial distribution of gravel, mud and sand content based on a set of data quality control criteria (see Li et al. 2010 and 2012). Predicting the spatial distribution of gravel, mud and sand content at a 0.0025 decimal degree resolution was undertaken using random forest (RF), a combined method of random forest and the ordinary kriging (RFOK), and the average of RFOK and RFIDW (i.e. a combined method of random forest and the inverse distance weighting) respectively (see Li et al. 2010, 2011 and 2012 for their definitions). The spatial prediction methods used were experimentally selected from over 40 methods/sub-methods based on assessment of predictive errors (Li et al. 2010, 2011 and 2012) and was refined for gravel, mud and sand respectively based on further experimental testing during May-June 2014 using datasets QCed in November 2013. The predictions in raster grids and ascii text file were generated in R. Final file is in WGS 84 coordinate system with a 0.0025 dd spatial resolution. File sizes are approximately 150 MB (raster grids) and 315 MB (ascii text) for each sediment type (i.e., gravel, mud and sand), with data dimensions of 3,681 (row) x 3,400 (column), 12,515,400 (cell) and 7043034 data points.