Sensors to Increase the Security of Underwater Communication Cables: A Review of Underwater Monitoring Sensors.

Underwater communication cables transport large amounts of sensitive information between countries. This fact converts these cables into a critical infrastructure that must be protected. Monitoring the underwater cable environment is rare and any intervention is usually driven by cable faults. In the last few years, several reports raised issues about possible future malicious attacks on such cables. The main objective of this operational research and analysis (ORA) paper is to present an overview of different commercial and already available marine sensor technologies (acoustic, optic, magnetic and oceanographic) that could be used for autonomous monitoring of the underwater cable environment. These sensors could be mounted on different autonomous platforms, such as unmanned surface vehicles (USVs) or autonomous underwater vehicles (AUVs). This paper analyses a multi-threat sabotage scenario where surveying a transatlantic cable of 13,000 km, (reaching water depths up to 4000 m) is necessary. The potential underwater threats identified for such a scenario are: divers, anchors, fishing trawls, submarines, remotely operated vehicles (ROVs) and AUVs. The paper discusses the capabilities of the identified sensors to detect such identified threats for the scenario under study. It also presents ideas on the construction of periodic and permanent surveillance networks. Research study and results are focused on providing useful information to decision-makers in charge of designing surveillance capabilities to secure underwater communication cables.

Geoacoustic inversion on the New England Mud Patch using warping and dispersion curves of high-order modes.

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.

Observations regarding coarse sediment classification based on multi-beam echo-sounder's backscatter strength and depth residuals in Dutch rivers.

This contribution investigates the behavior of two important riverbed sediment classifiers, derived from multi-beam echo-sounder (MBES)-operating at 300 kHz-data, in very coarse sediment environments. These are the backscatter strength and the depth residuals. Four MBES data sets collected at different parts of rivers in the Netherlands are employed. From previous research the backscatter strength was found to increase for increasing mean grain sizes. Depth residuals, however, are often found to have lower values for coarser sediments. Investigation of the four data sets indicates that these statements are valid only for moderately coarse sediment such as sand. For very coarse sediments (e.g., coarse gravel) the backscatter strength is found to decrease and the depth residuals increase for increasing mean grain sizes. This is observed when the sediment mean grain size becomes significantly larger than the acoustic wavelength of the MBES (5 mm). Knowledge regarding this behavior is of high importance when using backscatter strength and depth residuals for sediment classification purposes as the reverse in behavior can induce ambiguity in the classification.

An inter-comparison of sediment classification methods based on multi-beam echo-sounder backscatter and sediment natural radioactivity data.

This contribution presents sediment classification results derived from different sources of data collected at the Dordtse Kil river, the Netherlands. The first source is a multi-beam echo-sounder (MBES). The second source is measurements taken with a gamma-ray scintillation detector, i.e., the Multi-Element Detection System for Underwater Sediment Activity (Medusa), towed over the sediments and measuring sediment natural radioactivity. Two analysis methods are employed for sediment classification based on the MBES data. The first is a Bayesian estimation method that uses the average backscatter data per beam and, therefore, is independent of the quality of the MBES calibration. The second is a model-based method that matches the measured backscatter curves to theoretical curves, predicted by a physics-based model. Medusa provides estimates for the concentrations of potassium, uranium, thorium, and cesium, known to be indicative for sediment properties, viz. mean grain size, silt content, and the presence of organic matter. In addition, a hydrophone attached to the Medusa system provides information regarding the sediment roughness. This paper presents an inter-comparison between the sediment classification results using the above-mentioned methods. It is shown that although originating from completely different sources, the MBES and Medusa provide similar information, revealing the same sediment distribution.

Improving riverbed sediment classification using backscatter and depth residual features of multi-beam echo-sounder systems.

Riverbed and seafloor sediment classification using acoustic remote sensing techniques is of high interest due to their high coverage capabilities at limited cost. This contribution presents the results of riverbed sediment classification using multi-beam echo-sounder data based on an empirical method. Two data sets are considered, both taken at the Waal River, namely Sint Andries and Nijmegen. This work is a follow-up to the work carried out by Amiri-Simkooei et al. [J. Acoust. Soc. Am. 126(4), 1724-1738 (2009)]. The empirical method bases the classification on features of the backscatter strength and depth residuals. A principal component analysis is used to identify the most appropriate and informative features. Clustering is then applied to the principal components resulting from this set of features to assign a sediment class to each measurement. The results show that the backscatter strength features discriminate between different classes based on the sediment properties, whereas the depth residual features discriminate classes based on riverbed forms such as the "fixed layer" (stone having riprap structure) and riverbed ripples. Combination of these two sets of features is highly recommended because they provide complementary information on both the composition and the structure of the riverbed.