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 For application of the EB model, six additional fit parameters were employed as well as several calculated and tabulated values. It was possible to fit both the VGS(λ) and EB models to the com- pressional- and shear-wave speed data with com- parable model-data agreement. Although there was more scatter in the attenuation data, it was also reasonably fit by the models. To further as- sess which of the frequency-dependent models more accurately describe the data, comparison with measurements of other acoustic parameters, such as the bottom reflection coefficient, would help reduce ambiguity.
Although models like EB and VGS(λ) have been
primarily applied to prediction of the geoacous-
tic properties in sandy sediments, less attention
has been given to muddy sediments or sedi-
ments that are mixtures of sand and significant
fractions of smaller particles. For example, new
models will likely need to be developed that take
into account the microscopic physics of the in-
teractions between clay particles. Recent efforts have been undertaken to understand the physics of interparticluate interactions in clay and its impact on shear-wave speed or compressional attenuation (Pierce et al., 2016). However, a unified model capable of predicting all four wave parame- ters in fine-grained sediments remains to be developed.
Biological Effects on Sediment
Acoustic Properties
There have been a number of experimental studies con- cerned with the effects of benthic biology on seabed acous- tic properties. Although some empirical relationships have been established, no predictive theoretical model addresses the acoustic behavior of marine sediment containing organ- ic matter or biological content. In this section, we present two examples of how marine sediment acoustic properties can be affected by the presence of biological organisms by looking at seagrass meadows and benthic infauna.
Seagrass Meadows
Biological processes and physical characteristics associated with seagrass can greatly affect acoustic propagation in coast- al regions. An important acoustical effect is due to bubble production by the plants, which can have a significant impact on both object detection and bottom mapping sonars by in- creasing clutter through reflection, absorption, and scattering of sound (Komatsu et al., 2003). Remote sensing techniques
Figure 4. Comparison of measured wave data from the deployments at the shal- low site with sandy sediment (open diamonds with error bars) with the best- fit sediment models. a: Compressional sound speed ratio; b: shear-wave speed; c: compressional-wave attenuation; d: shear-wave attenuation. M-W, Mallock- Wood equation; VGS(λ), viscous grain-shearing theory; EB; extended Biot model. The VGS(λ) and EB models were both developed for sandy sediments, and they fit well with the measured data from the sandy sediment site. The M-W is equivalent to the low-frequency limit for sound speed in the VGS(λ) model. Adapted from Lee et al. (2016a).
 have been demonstrated to monitor biological markers, such as photosynthetic activity from seagrass, as an assessment of marine ecosystem health (Hermand et al., 1998). Addition- ally, seismo-acoustic survey tools have been investigated to obtain carbon sink estimates for the sediment underneath seagrass beds (Lo Iacono et al., 2008). It has also been shown that gas production by seagrass is temporally variable over both shorter (diurnal) and longer (seasonal and longer) tim- escales, indicating that the potential acoustic effects are also time dependent (Wilson et al., 2012).
Gas generated by seagrass photosynthetic activity can dis- solve directly into the surrounding seawater or form bubbles that cling to the outside of the leaves. In addition to the gas- bearing leaf tissue in the water column, the rhizomes also contain aerenchyma (gas-filled canals), which allow for dif- fusion of oxygen into the surrounding sediment. The density and elastic moduli of the plants themselves can also poten- tially affect long-range acoustic propagation by altering the effective material properties at the water-sediment interface and within the seabed when seagrass meadows are ubiqui- tous in the environment.
In situ measurements of sound speed and attenuation in a bed of Thalassia testudinum (turtlegrass) located in east Corpus Christi Bay near Port Aransas, TX, are shown in Figure 5 (Lee et al., 2017). The acoustic measurements were
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