This paper presents single receiver geoacoustic inversion of a combustive sound source signal, recorded during the 2017 Seabed Characterization Experiment on the New England Mud Patch, in an area where water depth is around 70 m. There are two important features in this study. First, it is shown that high-order modes can be resolved and estimated using warping (up to mode number 18 over the frequency band 20–440 Hz). However, it is not possible to determine mode numbers from the data, so that classical inversion methods that require mode identification cannot be applied. To solve this issue, an inversion algorithm that jointly estimates geoacoustic properties and identifies mode number is proposed. It is successfully applied on a range-dependent track, and provides a reliable range-average estimation of geoacoustic properties of the mud layer, an important feature of the seabed on the experimental area.

A three-dimensional underwater sound propagation model with realistic ocean environmental conditions has been created for assessing the impacts of noise from offshore wind farm construction and operation. This model utilizes an existing accurate numerical solution scheme to solve the three-dimensional Helmholtz wave equation, and it is compared and validated with acoustic transmission data between 750 and 1250 Hz collected during the development of the Block Island Wind Farm (BIWF), Rhode Island. The variability of underwater sound propagation conditions has been investigated in the BIWF area on a temporal scale of months and a spatial scale of kilometers. This study suggests that future offshore wind farm developments can exploit the seasonal variability of underwater sound propagation for mitigating noise impact by scheduling wind farm construction during periods of high acoustic transmission loss. Discussions on other applications of soundscape prediction, planning, and management are provided.

The sea bottom is generally modeled as a fluid for many of the shallow water acoustic propagation modeling applications. The inherent assumption is that the bottom does not support any shear wave propagation. This study explores the impact of this assumption on the dispersion behavior of acoustic normal modes. A sensitivity study is performed to investigate the effect of density, shear, and compressional wave speeds on modal dispersion. This study focuses on low-frequency (less than 100 Hz) and lower order modes (mode four and lower) in shallow water (70–90-m water depth). These modes and frequency bands are characterized by deeper penetration and larger depth averaging scales. This enabled the use of a simple half-space elastic model of the sea bottom for most of the analyses in this study. A depth-dependent bottom model was used to investigate the effect of a mud layer on modal dispersion. Sediment shear wave speeds were also estimated using broadband data from two shallow water experiments: Shelf Break Primer (1996) and Seabed Characterization Experiment (2017). The inversion results from these two experiments are compared and they are also compared with deep core data and prior inversions.

This article proposes a transdimensional (trans-D) geoacoustic inversion method adapted to range-dependent (RD) propagation tracks based on prior information from a high-resolution seismic survey. Most trans-D inversions to date model the seabed as a stack of range-independent homogeneous layers, with unknown geoacoustic parameters and an unknown number of layers. The proposed method models the seabed as an unknown number of homogeneous sediment layers with an RD thickness structure and applies an adiabatic normal-mode model to predict acoustic propagation. To do so, the method extrapolates trans-D seabed models proposed at the receiver position over the range of the propagation track using reflector-interface information from a seismic survey. The method is applied successfully to modal time–frequency dispersion data collected over an RD track during the 2017 Seabed Characterization Experiment (SBCEX).

Fibre-optic distributed acoustic Sensing (DAS) provide information on vibration response comparable with geophones and seismometers and may become widely used for infrastructure monitoring. DAS can be used to monitor earthquake activity, carbon dioxide sequestration, pipelines and roadway/railway subgrade integrity. Little is known about the effect of soil type and burial method on DAS response. The objective of this paper is to present the results of a field study in which DAS was installed in different soil types (silty sand, clean sand, gravel and a flowable fill) adjacent to an existing, decade-old DAS array. Impact tests were performed to evaluate DAS response in the different soil types and a portion of DAS array installed a decade prior. Signal-to-noise ratio (SNR) was used to compare performance of DAS response. Results of the monitoring programme indicate that portions of the array in sand, gravel and silty sand had a good response with comparable SNR. A newer portion of the array showed ∼5 dB better than the decade-old portion of DAS array, both in silty sand, with the old portion still performing well. These results may help build confidence with the geotechnical community regarding the longevity performance of DAS for infrastructure vibration monitoring.

Horizontal to vertical spectral ratio (HVSR) of the Scholte wave particle motion can be used to estimate ocean bottom shear properties. This study uses data from two ocean bottom recorders deployed during the Seabed Characterization Experiment in 2022 (SBCEX-2022) in the New England Mud Patch. Sediment thickness data from seismic surveys and core data from the experimental area were used to constrain the shear speed estimation approach. The bottom model estimated by matching the HVSR and ellipticity angle from a model with the data compares well with two previous inversions from the same location. The current inversion estimates shear speed in the sublayers within the mud layer, which were identified by the seismic surveys and core data. The HVSR-based method is a fast and robust inversion approach for estimating the thickness of the sediment layers and the shear wave speeds. The data used for this study consist primarily of ambient seismic field mixed with potential far-field anthropogenic noise sources (e.g., ships, sources deployed as part of the experiment).