|Title||Maximum entropy derived statistics of sound-speed structure in a fine-grained sediment inferred from sparse broadband acoustic measurements on the New England Continental Shelf|
|Publication Type||Journal Article|
|Year of Publication||2020|
|Authors||Knobles D.P, Wilson P.S, Goff J.A, Wan L., Buckingham MJ, Chaytor J.D, Badiey M.|
|Type of Article||Article|
|Keywords||east china sea; Engineering; frequency-dependence; geoacoustic; information-theory; marine-sediments; Maximum entropy; oceanography; parameters; reflection-loss; Remote sensing; sea-bottom attenuation; seabed acoustics; shallow-water; velocity-gradients; Wave properties|
Marginal probability distributions for parameters representing an effective sound-speed structure of a fine-grained sediment are inferred from a data ensemble maximum entropy method that utilizes a sparse spatially distributed set of received pressure time series resulting from multiple explosive sources in a shallow-water ocean environment possessing significant spatial variability of the seabed. A remote sensing seabed acoustics experiment undertaken in March 2017 off the New England Shelf was designed so that multiple independent analyses could infer the statistical properties of the seabed. The current analysis incorporates the measured horizontal variability from interpretations of a subbottom profiling survey of the experimental area. An idealized range- and azimuth-dependent parameterization of the seabed is derived from identification of horizons within the seabed that define multiple sediment layers. A sparse set of explosive charges were deployed on circular tracks with radii of about 2, 4, and 6.5 km with an acoustic array at the center to correlate a set of random measurements to physical acoustic processes that characterize the seabed. The mean values of a surface sound speed ratio and a linear sound speed gradient for the fine-grained sediment layer derived from 12 data samples processed in the 25-275-Hz band provide an estimate of the effective sound-speed structure in a 130-km$<^>2$ area. The inferred sediment sound speed values are evaluated by predicting measured time series data not used in the statistical inference, and are also compared to historical measurements. Finally, the low-frequency maximum entropy estimate of the sediment sound speed along with physical measurements derived from piston core measurements are utilized to estimate the sediment grain bulk modulus.