1 Predictive Modelling of Seabed Sediment Parameters Using Multibeam Acoustic Data: A Case Study on the Carnarvon Shelf, Western Australia Z. Huang 1 , S. Nichol 2 , J. Daniell 3 , J. Siwabessy 4 , B. Brooke 5 1 Geoscience Australia, GPO Box 378, Canberra ACT 2601, Australia Telephone: +61 2 62495876 Fax: +61 62499920 Email: Zhi.Huang@ga.gov.au 2 Geoscience Australia, GPO Box 378, Canberra ACT 2601, Australia Telephone: +61 2 62499346 Fax: +61 62499920 Email: Scott.Nichol@ga.gov.au 3 Geoscience Australia, GPO Box 378, Canberra ACT 2601, Australia Telephone: +61 2 62499691 Fax: +61 62499920 Email: James.Daniell@ga.gov.au 4 Geoscience Australia, GPO Box 378, Canberra ACT 2601, Australia Telephone: +61 2 62499514 Fax: +61 62499920 Email: Justy.Siwabessy@ga.gov.au 5 Geoscience Australia, GPO Box 378, Canberra ACT 2601, Australia Telephone: +61 2 62499434 Fax: +61 62499920 Email: Brendan.Brooke@ga.gov.au 1. Introduction Previous studies have shown that seabed sediment parameters such as %Mud, %Sand, and %Gravel are useful surrogates for predicting the distribution of benthic species (e.g., Beaman and Harris 2007; Degraer et al. 2008). Typically, these parameters are derived from a limited number of widely distributed sediment grab samples. To improve predictions from these point data, continuous layers of these parameters are needed. Apart from often used geostatistic techniques, predictive modelling techniques can be used for large area mapping. In particular, machine learning models offer most potential because they are able to handle both linear and non-linear relationships. Multibeam data with high resolution coverage is now routinely collected in marine surveys. From multibeam bathymetry we can derive a range of terrain and morphometric variables that have known relationships with sediment distribution patterns. Multibeam backscatter intensity depends on both acoustic impedance contrast and the roughness of the seafloor, which are seabed habitat dependent. Various first and second order texture measures derived from backscatter data may be useful in predicting sediment. Variables that measure spatial autocorrelation are also considered to be useful. This paper reports the results of predictive spatial modeling of two seabed sediment parameters: %Mud and %Sand for a 700 km 2 area of the Carnarvon Shelf, Western Australia. Multiple machine learning models were applied to create prediction maps and prediction uncertainty maps.