Model-Based Acoustic Remote Sensing of Seafloor Characteristics

Stochastic formalism-based seafloor feature discrimination using multifractality of time-dependent acoustic backscatter

“…The scatter diagram (Fig. 3b of De and Chakraborty, 2011) between the estimated mean values of M ϕ and γ 2 at 210 kHz indicated that the values of γ 2 were confined within 3.21-3.4 in fine sediments and within 3.0-3.21 in coarse sediments. In contrast, the estimated mean values of γ 2 at 33 kHz inversions did not exhibit any apparent trend to discriminate between the fine and coarse sediment provinces.…”

“…The numerical approach employed for extracting information from the data is commonly referred to as "inversion modeling". The inversion modeling primarily involves physics-based inversion of echosounding data to obtain the upper-layer seafloor roughness parameters, namely the sediment mean grain size (M ϕ ); spectral parameters at the water-seafloor interface (γ 2 , w 2 ); and sediment volume parameter (σ 2 ), which can be used to examine the fine scale seafloor processes (Sternlicht and de Moustier, 2003a, b;De and Chakraborty, 2011;Haris et al, 2011).…”

“…The differences in H principally reflect variations in the spectral slopes (Lovejoy et al, 2001) (and thereby the corresponding γ 2 values). With reference to the inversion modeling study carried out by De and Chakraborty (2011), in the coarse sediment region, the average γ 2 values were restricted to values around 3.23 ± 0.071 and 3.16 ± 0.047, respectively, at 33 and 210 kHz. In the fine sediment region, the average γ 2 values were found to be within 3.22 ± 0.074 and 3.30 ± 0.037, respectively, for 33 and 210 kHz.…”

“…The scatter diagram between the estimated mean values of M ϕ and w 2 (Fig. 3a of De and Chakraborty, 2011) revealed that, in the coarse sediment region, the average w 2 values were restricted to values around 0.00356 ± 0.00047 and 0.00365 ± 0.00101, respectively, at 33 and 210 kHz, but that in fine sediments, the average w 2 values were found to vary between 0.000461 ± 0.00013 and 0.000605 ± 0.000042, respectively, at 33 and 210 kHz. The computed w 2 (or intercept) values were well clustered at both the acoustic frequencies, having fewer fluctuations for the fine sediment as compared with the coarse sediment region.…”

“…The seafloor characterization and classification methodologies available in the literature using SBES can be traditionally grouped into two categories, namely modelbased techniques and empirical methods. The model-based techniques often utilize physics-based acoustic backscatter models to characterize the seafloor sediments by optimizing the match between the measured and the modeled signals (Sternlicht and de Moustier, 2003a, b;van Walree et al, 2006;De and Chakraborty, 2011;Snellen et al, 2011). The empirical methods however rely on the statistical and artificial neural network-based approaches to correlate the features of echo signals with the sediment type (van Walree et al, 2005;Chakraborty et al, 2004;De and Chakraborty, 2009;Amiri-Simkooei et al, 2011;Madricardo et al, 2012).…”