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Seafloor characterization using multibeam echosounder (MBES) backscatter data is an active field of research. The observed backscatter curve (OBC) is used in an inversion algorithm with available physics-based models to determine the seafloor geoacoustic parameters. A complication is that the OBC cannot directly be coupled to the modeled backscatter curve (MBC) due to the correction of uncalibrated sonars. Grab samples at reference areas are usually required to estimate the angular calibration curve (ACC) prior to the inversion. We first attempt to estimate the MBES ACC without grab sampling by using the least squares cubic spline approximation method implemented in a differential evolution optimization algorithm. The geoacoustic parameters are then inverted over the entire area using the OBCs corrected for the estimated ACC. The results indicate that a search for at least three geoacoustic parameters is required, which includes the sediment mean grain size, roughness parameter, and volume scattering parameter. The inverted mean grain sizes are in agreement with grab samples, indicating reliability and stability of the proposed method. Furthermore, the interaction between the geoacoustic parameters and Bayesian acoustic classes is investigated. It is observed that higher backscatter values, and thereby higher acoustic classes, should not only be attributed to (slightly) coarser sediment, especially in a homogeneous sedimentary environment such as the Brown Bank, North Sea. Higher acoustic classes should also be attributed to larger seafloor roughness and volume scattering parameters, which are not likely intrinsic to only sediment characteristics but also to other contributing factors.

Original languageEnglish
Article number292
Number of pages23
JournalGeosciences (Switzerland)
Volume9
Issue number7
DOIs
Publication statusPublished - 1 Jul 2019

    Research areas

  • Angular calibration curve, Geoacoustic inversion, Least squares cubic spline approximation, Multibeam echosounder, Seafloor sediment classification

ID: 55587869