Using Multibeam Backscatter Data to Investigate Sediment-Acoustic Relationships

Sediment properties are known to influence acoustic backscatter intensity. This sediment-acoustic relationship has been investigated previously through using physical geoacoustic models and empirical methods and found to be complex and nonlinear. Here we employ a robust machine-learning statistical model (random forest decision tree) to investigate the most likely nonlinear sediment-backscatter relationships. The analysis uses colocated sediment and acoustic backscatter data (collected from a 300-kHz multibeam sonar system) for 564 locations in four different areas on the Australian margin. Seven sediment grain size properties (%gravel, %sand, %mud, mean grain size, sorting, skewness, and kurtosis) were used to predict the acoustic backscatter responses at individual incidence angles. The modeling results demonstrate the effectiveness of this multivariate predictive approach for the investigation of sediment-acoustic relationship. Thus, we find that for incidence angles between 1° and 41°, the sediment variables explain around 70% of variance in the backscatter intensity. Sediment mud content was found to be the most important sediment variable in the model and has a significant negative relationship with backscatter intensity. Mean grain size was the second ranked sediment variable and found to have a positive relationship with backscatter intensity. The results also show that sediment mud content plays a key role in sorting-backscatter and sand-backscatter relationships. Using only two sediment properties, mud content and mean grain size, together it was possible to largely explain the sediment-acoustic relationship. The strongest backscatter return occurred with medium sediment mud content and large mean grain sizes (or muddy coarse sand).