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Backscatter data from multibeam echosounders are commonly used to classify seafloor sediment composition. Previously, it was found that the survey azimuth affects backscatter when small organized seafloor structures, such as sand ripples, are present. These sand ripples are too small to be detected in the multibeam bathymetry. Here, we show that such azimuth effects are time dependent and are useful to examine the orientation of sand ripples in relation to the flow direction of the tide. To this end, multibeam echosounder data at four different frequencies were gathered from the area of the Brown Bank in the North Sea. The acoustic results were compared to video and tide-flow data for validation. The sand ripples affected the backscatter at all frequencies, but for the lowest frequencies the effect was spread over more beam angles. Using the acoustic data made it possible to deduce the orientations of the sand ripples over areas of multiple square kilometers. We found that the top centimeter(s) of the seafloor undergoes a complete transformation every six hours, as the orientation of the sand ripples changes with the changing tide. Our methodology allows for morphology change detection at larger scales and higher resolutions than previously achieved.

High-resolution surveying techniques of subtidal soft-bottom seafloor habitats show higher small-scale variation in topography and sediment type than previously thought, but the ecological relevance of this variation remains unclear. In addition, high-resolution surveys of benthic fauna show a large spatial variability in community composition, but this has yet poorly been linked to seafloor morphology and sediment composition. For instance, on soft-bottom coastal shelves, hydrodynamic forces from winds and tidal currents can cause nested multiscale morphological features ranging from metre-scale (mega)ripples, to sand waves and kilometre-scale linear sandbanks. This multiscale habitat heterogeneity is generally disregarded in the ecological assessments of benthic habitats. We therefore developed and tested a novel multiscale assessment toolbox that combines standard bathymetry, multibeam backscatter classification, video surveying of epibenthos and box core samples of sediment and macrobenthos. In a study on the Brown Bank, a sandbank in the southern North Sea, we found that these methods are greatly complementary and allow for more detail in the interpretation of benthic surveys. Acoustic and video data characterised the seafloor surface and subsurface, and macrobenthos communities were found to be structured by both sandbank and sand wave topography. We found indications that acoustic techniques can be used to determine the location of epibenthic reefs. The multiscale assessment toolbox furthermore allows formulating recommendations for conservation management related to the impact of sea floor disturbances through dredging and trawling.

Grain-size sorting in bedforms is well known in river dunes. On continental shelves, however, datasets aimed at grain-size sorting over bedforms, are limited. More extensive observations of sediment sorting over bedforms may help to understand their morphodynamic processes, and are key in habitat mapping, since grain-size is a main control on the composition of benthic fauna. A time series of seven multibeam (MBES) bathymetry and backscatter measurements and box cores were collected for the monitoring of a coastal nourishment in a tidal inlet at Ameland, Netherlands. Prior to the nourishment (April 2017), 10-15 m long and 1.5 m high megaripples occurred. The time series shows the rapid development of high and steep megaripples in the newly replenished sediment, with a wavelength of 40 m and height of 2.5 m within three months (during-nourishment; October 2017), which then grew into 120 m long and 3 m high sand waves in relatively shallow water (10 - 14 m) within 5 months (post-nourishment; March 2018). Relative backscatter (BS) strengths, which are corrected for, among others, transmission losses and bed morphology, represent seabed sediment characteristics. Bed classification of BS strengths, using an unsupervised Bayesian method, resulted in a high-resolution map of 5 acoustic classes (ACs), to which sediment types were assigned using the box cores as ground truthing. These box cores, however, were not taken at the detailed level of sand wave crests and troughs. The acoustic sediment classes (ASCs) exhibit a repetitive pattern, indicating horizontal sediment sorting over bedforms, that shifted and intensified during the growth of the megaripples into sand waves. The ASC megaripple pattern is less consistent, but generally comprises finer sediments (ASC2-3: sand) on the stoss sides and coarser sediments on the lee sides (ASC3-4: sand to slightly gravelly sand). The sand wave pattern is very consistent and comprises coarse sediments on the stoss sides (ASC5: gravel- and shell-containing sands), finer sediments towards the crests (ASC2-3: sand) and even finer sediments (ASC1: sandy mud) in the troughs. In the course of one year, both the morphological and sorting patterns seem to repeat itself. A similar sorting evolution was observed during the growth of megaripples just farther offshore. In a different data set, farther offshore on the Netherlands Continental Shelf and built up over several years, grab samples were collected in transects, specifically at crests and troughs of sand waves and long bed waves, and were analysed for grain size, organic matter and CaCO3 contents. Median grain sizes in the troughs of bedforms are consistently finer than at the crests, and reveal significant signatures between sand wave fields, with crest-trough differences among sites ranging between 10 and 85 micrometer. Unfortunately, MBES-BS data are not available for establishing large-scaled, spatial sorting patterns.This evolution of horizontal sediment-sorting patterns during the growth of marine bedforms may support modelling studies of hydrodynamic responses of flow over undulating beds and may explain the morphodynamic evolution of marine bedforms, as relevant in marine ecology. However, coherent empirical datasets are required.

The noise emissions of a full-scale nose landing gear (NLG), measured in a wind tunnel and obtained from computational simulations, are compared with those of three regional aircraft types recorded in flyover measurements. A comparison is made with the noise prediction models of Fink, Guo, and German Aerospace Center (DLR). A good agreement was found between all the spectra. The noise emissions up to 1.2 kHz were found to scale with the sixth power of the flow velocity, as usual; however, the spectra at higher frequencies collapsed better when scaled to the seventh power, confirming the fact that high-frequency noise is radiated from the turbulent flow surrounding small features of the NLG. Microphone arrays showed that the main noise sources were located in the middle of the wheel axle. For the flyovers and computational simulations, strong tonal peaks (at around 2200 Hz) were found, which are likely to be caused by open cavities in the NLG. This phenomenon is not accounted for in prediction models. Removing these tones would result in noise reductions of up to 2 dB. Thus, it is highly recommended to include cavity-noise estimations in the current prediction models, or to simply eliminate such cavities where possible with the use of cavity caps.

Realistic predictions of the contribution of the various sources affecting the quality of the bathymetric measurements prior to a survey are of importance to ensure sufficient accuracy of the soundings. To this end, models predicting these contributions have been developed. The objective of the present paper is to assess the performance of the bathymetric uncertainty prediction model for modern Multi-Beam Echo-Sounder (MBES) systems. Two datasets were acquired at water depths of 10 m and 30 m with three pulse lengths equaling 27 s , 54 s , and 134 s in the Oosterschelde estuary (The Netherlands). The comparison between the bathymetric uncertainties derived from the measurements and those predicted using the current model indicated a relatively good agreement except for the most outer beams. The performance of the uncertainty prediction model improved by accounting for the most recent insights into the contributors to the MBES depth uncertainties, i.e., the Doppler effect, baseline decorrelation (accounting for the pulse shape), and the signal-to-noise ratio.

Multi-frequency multibeam backscatter (BS) has indicated, in particular for fine sediments, the potential for increasing the discrimination between different seabed environments. Fine sediments are expected to have a varying signal penetration within the frequency range of modern multibeam echosounders (MBES). Therefore, it is unknown to what extent the multispectral MBES data represent the surface of the seabed or different parts of the subsurface. Here, the effect of signal penetration on the measured multi-frequency BS and bathymetry is investigated. To this end, two multi-frequency datasets (90 to 450 kHz) were acquired with an R2Sonic 2026 MBES, supported by ground-truthing, in the Vlietland Lake and Port of Rotterdam (The Netherlands). In addition, a model to simulate the MBES bathymetric measurements in a layered medium is developed. The measured bathymetry difference between the lowest ( 90 kHz) and highest frequency ( 450 kHz) in areas with muddy sediments reaches values up to 60 cm dependent on the location and incident angle. In spatial correspondence with the variation in the depth difference, the BS level at the lowest frequency varies by up to 15 dB for the muddy sediments while the BS at the highest frequency shows only small variations. A comparison of the acoustic results with ground-truthing, geological setting and model indicates that the measured bathymetry and BS at the different frequencies correspond to different parts of the seabed. However, the low-frequency BS cannot be directly related to a subsurface layer because of a significant sound attenuation in the upper layer. The simulation of the MBES bottom detection indicates that the bathymetry measured at the highest and lowest frequency can be used to determine the thickness of thin layers (&sim, 20 cm). However, with an increasing layer thickness, the bottom detection becomes more sensitive to the incident angle and small variations in the sediment properties. Consequently, an accurate determination of the layer thickness is hampered. Based on this study, it is highly recommended to analyze multi-frequency BS in combination with the inter-frequency bathymetry difference to ensure a correct interpretation and classification of multi-frequency BS data.

In this contribution bathymetric uncertainties induced by the use of frequency modulated (FM) signals for multi-beam-echo-sounder (MBES) measurements are quantified and their relevance for MBES bathymetric uncertainty predictions is assessed. When switching to FM, the quality of depth measurements can get deteriorated due to the Doppler effect and baseline decorrelation. The uncertainty due to the former is divided into second-order (imperfectness of the Doppler-range correction) and first-order (effect on beamsteering) effects. The latter also holds for continuous wave (CW) signals. Here, situations of relevance for measurements in the continental shelf and ship dynamics associated to rough and calm sea-states are considered, and the vertical uncertainty induced by the above sources is quantified. The influence of the Doppler effect depends on the sea state, but is found to potentially have a significant contribution to the MBES error budget for both FM and CW [nearly 82% (rough) and 68% (calm) of the total uncertainty]. The effect of baseline decorrelation depends on the actual pulse shape. For the specifications investigated, vertical uncertainties induced by this source are predicted to be larger for FM than that of CW. This is confirmed by a comparison between the modelled and measured effect on depth uncertainties when switching to FM.

This study applies three classification methods exploiting the angular dependence of acoustic seafloor backscatter along with high resolution sub-bottom profiling for seafloor sediment characterization in the Eckernforde Bay, Baltic Sea Germany. This area is well suited for acoustic backscatter studies due to its shallowness, its smooth bathymetry and the presence of a wide range of sediment types. Backscatter data were acquired using a Seabeam1180 (180 kHz) multibeam echosounder and sub-bottom profiler data were recorded using a SES-2000 parametric sonar transmitting 6 and 12 kHz. The high density of seafloor soundings allowed extracting backscatter layers for five beam angles over a large part of the surveyed area. A Bayesian probability method was employed for sediment classification based on the backscatter variability at a single incidence angle, whereas Maximum Likelihood Classification (MLC) and Principal Components Analysis (PCA) were applied to the multi-angle layers. The Bayesian approach was used for identifying the optimum number of acoustic classes because cluster validation is carried out prior to class assignment and class outputs are ordinal categorical values. The method is based on the principle that backscatter values from a single incidence angle express a normal distribution for a particular sediment type. The resulting Bayesian classes were well correlated to median grain sizes and the percentage of coarse material. The MLC method uses angular response information from five layers of training areas extracted from the Bayesian classification map. The subsequent PCA analysis is based on the transformation of these five layers into two principal components that comprise most of the data variability. These principal components were clustered in five classes after running an external cluster validation test. In general both methods MLC and PCA, separated the various sediment types effectively, showing good agreement (kappa >0.7) with the Bayesian approach which also correlates well with ground truth data (r2 > 0.7). In addition, sub-bottom data were used in conjunction with the Bayesian classification results to characterize acoustic classes with respect to their geological and stratigraphic interpretation. The joined interpretation of seafloor and sub-seafloor data sets proved to be an efficient approach for a better understanding of seafloor backscatter patchiness and to discriminate acoustically similar classes in different geological/bathymetric settings.

Methods for gridding multibeam echo sounder (MBES) measurements to equidistant grids are proposed as alternatives to the shallowest measured depth, which is affected by outliers. The approaches considered use a combination of mean and standard deviation of soundings and the regression coefficient from the best fitted plane. These methods along with mean and shallowest depths were applied to two surveyed areas. Two issues were found to be of importance, that is, a proper distribution of soundings and low uncertainties in the depth measurements. Improper sampling excludes using the method employing regression coefficients. For flat areas, the shallowest measured depth was found to be highly influenced by measurement uncertainties, counteracted when using the mean depth. However, the mean depth underestimates the shallowest depth for areas with slopes. When correcting the mean depth for standard deviation, the effect of slopes is accounted for while the influence of measurement uncertainties is decreased compared to shallowest measured depth. Since the uncertainties are dependent on beam angle, depth, and measurement equipment, these issues need to be accounted for in survey planning.

Continuous long-term sound sources are recorded at hydroacoustic station H03S, a threeelement hydrophone array south of Robinson Crusoe Island between 2014 April 23 and 2017 August 20. The origin of the signal between 3 and 20 Hz is investigated by using cross-correlation, array processing using plane wave beamforming and spectral analysis. Onebit normalization is successfully applied as a cross-correlation pre-processing step in order to suppress undesired earthquake events in the data. Traveltimes retrieved from averaged cross-correlations do not yield a coherent array direction of arrival. Averaged envelopes of the cross-correlations, however, indicate a coherent signal approaching H03S from a south. southwest direction. Beamforming indicates two dominant backazimuth directions: 172°-224° (Antarctica) and 242° (Monowai Volcanic Seamount). This continuous source field creates possibilities to investigate the applicability of acoustic thermometry at hydrophones H03 S1. S2. Cross-correlation and array processing indicate significant directional variation in local propagation, most likely related to the steep slope in the bathymetry near H03S. In addition, it is demonstrated that the ambient noise field is not sufficiently equipartitioned. It is shown that this causes a large error in the estimated temperature, primarily due to the short receiver spacing. These large errors have not been addressed in previous studies on deep-ocean acoustic thermometry. Hence, it is shown that acoustic thermometry does not perform well on small arrays such as H03S. The power spectral density yields a strong broadband signal in January. March, most likely related to iceberg noise. A narrow banded signal around 17 Hz between April and September corresponds to whale calls. The best-beam sound pressure levels towards Antarctica are compared to ERA5 climatologies for sea ice cover and normalized stress into the ocean, supporting the hypothesis of iceberg noise.

In this contribution a method for correcting bathymetric measurements affected by inaccurate water column sound speed profiles (SSPs) is presented. The method exploits the redundancy in the multibeam echosounder measurements obtained from the overlap of adjacent swaths by minimizing the difference between depths along overlapping swaths. Two optimization methods are used, i.e., Differential Evolution (DE) and Gauss-Newton (GN). While DE inverts for the sound speed by minimizing the depth variation, GN inverts for both bathymetry and sound speed by minimizing the squared sum of the differences between the modeled and measured travel times. The inversion method assumes a constant SSP in the water column. Applying the method to a salt wedge survey area with large variations in the water column sound speed indicates a good agreement between the original depth measurements and those derived after the inversion with the mean and standard deviation of the depth differences equaling 0.009m and 0.024m, respectively. This indicates that even with a simple parametrization of the sound speed in the water column, the correct bathymetry can be derived from the inversion. The SSP inversion method is also applied to an area with existing refraction artefacts. It corrects the bathymetry and reduces the mean and standard deviation of the depth standard deviation by a factor of around 2.75 compared to the case where the measured SSPs were used. Furthermore, the SSP inversion method neither manipulates the existing morphology nor introduces artificial bathymetric features in the areas where such refraction artefacts are not present. Considering constant SSPs, both DE and GN give almost identical results with GN being faster. However, GN is less flexible with regards to varying sound speed parameterizations.

(2) the use of multiple frequencies allows for a better acoustic discrimination of seabed sediments than single-frequency data, Multi-frequency backscatter data collected from multibeam echosounders (MBESs) is increasingly becoming available. The ability to collect data at multiple frequencies at the same time is expected to allow for better discrimination between seabed sediments. We propose an extension of the Bayesian method for seabed classification to multi-frequency backscatter. By combining the information retrieved at single frequencies we produce a multispectral acoustic classification map, which allows us to distinguish more seabed environments. In this study we use three triple-frequency (100, 200, and 400 kHz) backscatter datasets acquired with an R2Sonic 2026 in the Bedford Basin, Canada in 2016 and 2017 and in the Patricia Bay, Canada in 2016. The results are threefold: (1) combining 100 and 400 kHz, in general, reveals the most additional information about the seabed, and (3) the optimal frequency selection for acoustic sediment classification depends on the local seabed. However, a quantification of the benefit using multiple frequencies cannot clearly be determined based on the existing ground-truth data. Still, a qualitative comparison and a geological interpretation indicate an improved discrimination between different seabed environments using multi-frequency backscatter.

Acoustic remote sensing techniques are an attractive means for obtaining information on the composition of marine sediments since they have high coverage capabilities and thus allow for efficient surveying. Operating at a wide range of specific frequencies, the acoustic sensors provide insight into the sediment body at different depths. This article suggests an efficient manner to combine high- and low-frequency acoustics for obtaining a comprehensive description of fine-grained sediments in a shallow water environment, as was aimed at by the Maritime Rapid Environmental Assessment/Blue Planet trial (MREA/BP'07). This trial was carried out in the Mediterranean Sea in 2007, employing a variety of acoustic sensors, including echosounders, seismic systems, and dedicated array configurations. In a previous paper (Siemes , IEEE J. Ocean. Eng. vol. 35, no. 4, pp. 766–778), we established a three-dimensional picture of the sediment distribution in the MREA/BP'07 area by associating high-frequency echosounder data with low-frequency seismics. A classification based solely on these hydrographic measurements, however, could not provide the physical properties of the near-surface sediments. In the current article we complement this environmental picture with results from a geoacoustic inversion effort, which do provide information on the actual physical properties, such as sound speed, density, attenuation, and layer thickness. In contrast to carrying out the inversion over the complete area, only a limited number of locations was selected for inversion, to limit the computational efforts. This selection was based on the hydrographic environmental picture obtained in (Siemes , IEEE J. Ocean. Eng. vol. 35, no. 4, pp. 766–778). Inverted sediment properties obtained within similar hydrographic regions confirm the similarity in sediment type among these regions, whereas differences in sediment properties between different hydrographic regions are confirmed as well. Variations in the inversion results within an area with a single sediment type could be attributed to the presence of gas. These results show the suitability of the proposed approach, where backscatter and seismic data discern areas that a priori would differ in their near-surface sediment properties and where geoacoustic inversion assigns actual sediment parameters to these different areas.

Highlights: • Acoustically meaningful clustering correlating with ground truth data • Seascape scale acoustic mapping through classification per beam footprint • Bayesian approach for discriminating sedimentary units with low heterogeneity • Geo-acoustic resolution: a new measure of sedimentary acoustic class separability Modern seafloor mapping is based on high resolution MBES systems that provide detailed bathymetric and acoustic intensity (backscatter) information. We examine and validate the performance of two unsupervised MBES classification techniques for discriminating acoustic classes of sedimentary units with small grain size variability. The first technique, based on a principal components analysis (PCA), is commonly used in literature and has been applied for comparison with the more recent approach of Bayesian statistics. By applying these techniques to a MBES dataset from an estuarine area in The Netherlands, we tested their ability to discriminate fine grained sediments (at least 70% silt) holding small percentages of coarser material such as sand, shell hash or shells. We focus on the Bayesian technique as it outputs acoustically significant classes related to backscatter values. This technique utilizes backscatter values averaged over scatter pixels (projected pulse lengths) inside the footprint of each beam. The originality of our application lies in the fact that, the optimal number of classes is derived by utilizing a number of beams simultaneously. It is assumed that the backscatter values per beam vary relatively to the varying seafloor types. By treating the beams separately, across track variation in the seafloor type can also be accounted for. Thereby the classification is guided by outer, more discriminative beams. Additionally we control the optimal number of classes by employing the quantitative criterion of goodness of fit (χ2). The Bayesian acoustic classes show correlation with grain size parameters such as coarse fraction (>500μm) percentage and mean of the grain size (

Natural and man-induced coastal erosion endanger life and environment in coastal areas worldwide. For sedimentary barrier coasts, beach and underwater nourishments are an efficient coastal protection strategy. To optimize nourishments and to understand their impact on the marine environment, monitoring strategies are required. In this study, we investigate the potential of multibeam echosounder (MBES) data, providing both bathymetry and backscatter (BS), for monitoring the evolution of the nourished sediment and morphodynamics over time. A time series of seven MBES measurements, as well as two sets of box cores, vibrocores and seismic data were acquired of a channel-side nourishment near the Wadden Sea island Ameland (The Netherlands), between April 2017 and May 2019. In general, a high confidence of the acoustic reliability of the BS time series measurements is demonstrated. The unsupervised Bayesian classification method, supported by ground-truthing, is employed to produce a time series of sediment maps, revealing sediments ranging from sandy mud to sand with varying amounts of shell fragments. Based on the sediment maps, the nourished sediment could be distinguished from the natural sediment. Within one year, the shell-rich pre-nourishment seabed is recreated by washing out finer sediments, which are deposited towards the main tidal channel. Using the seismic data and vibrocores, the shell-rich pre-nourishment seabed could be identified in the subsurface after being buried by the nourishments, supporting the general findings. Furthermore, a rapid development of steep bedforms with increasing sediment sorting is observed in parts of the nourished areas. This study shows that high-resolution sediment maps obtained from a time series of MBES BS together with bathymetry reveal morphodynamic and sedimentary processes of nourishment evolution and can advance underwater nourishment strategies.

Obtaining an overview of the spatial and temporal distribution of seabed sediments is of high interest for multiple research disciplines. Multibeam echosounders allow for the mapping of seabed sediments with high area coverage. In this paper, the repeatability of acoustic classification derived from multibeam echosounder backscatter is addressed. To this end, multibeam echosounder backscatter data acquired on the Cleaver Bank (North Sea) during five different surveys is employed using two different classification methods, i.e., a method based on the principal component analyses and the Bayesian technique. Different vessels were used for the different surveys. The comparison of the classification results between the different surveys indicates good repeatability. This repeatability demonstrates the potential of using backscatter for long-term environmental monitoring. However, the use of different classification methods results in somewhat different classification maps. Monitoring, therefore, requires the consistent use of a single method. Furthermore, it is found that the statistical characteristics of backscatter is such that clustering algorithms are less suited to discern the number of sediment types present in the study area. The Bayesian technique accounting for backscatter statistics is therefore recommended. A strong positive correlation between backscatter and median grain size for finer sediments (0.5 mm. Consequently, for the situation considered a unique assignment of sediment type to acoustic class is not possible for these coarser sediments.

Extensive seabed sediment mapping is highly relevant to describe marine ecosystems and to quantify the distribution and extent of benthic habitats. Compared to traditional mapping methods, primarily based on bed sampling, multibeam echo sounding (MBES) is a time-efficient tool to acquire high-resolution bathymetric and backscatter data over large areas. We use a Bayesian method for unsupervised acoustic sediment classification (ASC) of MBES backscatter data of the Cleaver Bank, Netherlands Continental Shelf. On these sparsely distributed backscatter datasets, we tested and evaluated different Kriging algorithms, showing that Ordinary Kriging results in a reliable map. We introduce a new approach to classify interpolated MBES backscatter based on the Bayesian method for producing full-coverage sediment maps. Comparison to a traditional sediment map and in situ measurements shows that this approach resolves lateral heterogeneities (kilometers). When evaluating the high-resolution sediment map obtained from the Bayesian method, based on the actual backscatter, mapping laterally heterogeneous sediments significantly improved (meters). In order to create the optimal sediment map, we aimed to integrate ASC into existing maps, which, however, requires quantified spatial uncertainties of both considered maps. Finally, the low discrimination power of MBES backscatter for coarse sediments is highlighted as a shortcoming of current ASC mapping.

The noise emissions of a full-scale nose landing gear, measured in a wind tunnel and obtained from computational simulations, are compared with those of three regional aircraft types recorded in flyover measurements. The results from these three approaches are also compared with the predictions of two airframe noise models (Fink and Guo). The geometries of the nose landing gears in all cases were similar. Microphone arrays and acoustic imaging algorithms were employed to estimate the sound emissions of the nose landing gears. A good agreement was found between the overall trends of the frequency spectra in all cases. Moreover, the expected 6th power law with the flow velocity was confirmed. On the other hand, strong tonal peaks (at around 2200 Hz) were only found for the flyover tests and computational simulations and are not present in typical noise prediction models. As the frequencies of the tones did not depend on the flow velocity, they are likely to be caused by cavities found in structural components of the nose landing gear. Removing these tones would cause overall noise reductions up to 2 dB in the frequency range examined. The noise emissions in the side direction did not present tonal peaks. The acoustic source maps showed that the dominant noise sources were located in the middle of the wheel axle, followed by the main strut and the bay doors. It is, therefore, recommended to further investigate this phenomenon, to include cavity-noise estimations in the current noise prediction models, and to eliminate such cavities where possible with the use of cavity caps, for example.

High resolution maps of sandy seafloors are valuable to understand seafloor dynamics, plan engineering projects, and create detailed benthic habitat maps. This paper presents multibeam echosounder backscatter classification results of the Brown Bank area of the North Sea. We apply the Bayesian classification method in a megaripple and sand wave area with significant slopes. Prior to the classification, corrections are implemented to account for the slopes. This includes corrections on the backscatter value and its corresponding incident angle. A trade-off in classification resolutions is found. A higher geo-acoustic resolution is obtained at the price of losing spatial resolution, however, the Bayesian classification method remains robust with respect to these trade-off decisions. The classification results are compared to grab sample particle size analysis and classified video footage. In non-distinctive sedimentary environments, the acoustic classes are not attributed to only the mean grain size of the grab samples but to the full spectrum of the grain sizes. Finally, we show the Bayesian classification results can be used to characterize the sedimentary composition of megaripples. Coarser sediments were found in the troughs and on the crests, finer sediments on the stoss slopes and a mixture of sediments on the lee slopes.

Explosive submarine volcanic processes are poorly understood, due to the difficulties associated with both direct observation and continuous monitoring. In this study hydroacoustic, infrasound, and seismic signals recorded during the May 2010 submarine eruption of South Sarigan seamount, Marianas Arc, are used to construct a detailed event chronology. The signals were recorded on stations of the International Monitoring System, which is a component of the verification measures for the Comprehensive Nuclear-Test-Ban Treaty. Numerical hydroacoustic and infrasound propagation modelling confirms that viable propagation paths from the source to receivers exist, and provide traveltimes allowing signals recorded on the different technologies to be associated. The eruption occurred in three stages, separated by three-hour periods of quiescence. 1) A 46 h period during which broadband impulsive hydroacoustic signals were generated in clusters lasting between 2 and 13 min. 95% of the 7602 identified events could be classified into 4 groups based on their waveform similarity. The time interval between clusters decreased steadily from 80 to 25 min during this period. 2) A five-hour period of 10 Hz hydroacoustic tremor, interspersed with large-amplitude, broadband signals. Associated infrasound signals were also recorded at this time. 3) An hour-long period of transient broadband events culminated in two large-amplitude hydroacoustic events and one broadband infrasound signal. A speculative interpretation, consistent with the data, suggests that during phase (1) transitions between endogenous dome growth and phreatomagmatic explosions occurred with the magma ascent rate accelerating throughout the period; during phase (2) continuous venting of fragmented magma occurred, and was powerful enough to breach the sea surface. During the climactic phase (3) discrete powerful explosions occurred, and sufficient seawater was vaporised to produce the contemporaneous 12 km altitude steam plume.

Acoustic remote sensing is a useful tool for seafloor characterization. This contribution presents the results of seafloor sediment classification using single-beam echo-sounder (SBES) data based on a phenomenological method. Basic concepts of principal component analysis (PCA) and its applicability to the sediment classification using acoustical data are studied. This mathematical tool, which retains most of the variation of the data, is applied to the SBES echo shape parameters such as total energy, time-spread, skewness and flatness on three low (12 kHz), moderate (38 kHz), and high (200 kHz) frequencies, making 12 features in total. These parameters are dependent on sediment types and can therefore be used as attributes for seafloor classification. To decrease the statistical fluctuations of the extracted features, an averaging over a sufficiently large number of consecutive pings have been applied to the features. The SBES classification results based on the PCA and K-means clustering approach can clearly discriminate between different sediment classes. The signal at 12 kHz contains information on sediment layers (5 m depth). The performance of the method and the results obtained are assessed using the following independent criteria: 1) inspection of the track crossings indicates stable feature extraction and processing strategy; 2) comparison between the class numbers of the classification results and of the grab samples shows a significant correlation coefficient of 0.90; and 3) an error matrix verifies the stability and independence of the classification results from the features considered.

Seafloor classification using acoustic remote sensing techniques is an attractive approach due to its high-coverage capabilities and limited costs. The multi-beam echo-sounder (MBES) system provides high-resolution bathymetry and backscatter information with 100% coverage. In this paper, we present a seafloor classification method that employs the MBES backscatter data. The method uses the averaged backscatter data per beam. It, therefore, is independent on the quality of the MBES calibration. Also, its performance is insensitive to seafloor type variation along the MBES swathe and corrections for the angular dependence of the backscatter are not needed. The method accounts for the ping-to-ping variability of the backscatter intensity. It estimates both the number of seafloor types present in the survey area and the probability density function for the backscatter strength at a certain angle for each of the seafloor types. Application of the method to MBES backscatter data acquired in a well-known test area in the North Sea shows very good agreement with available ground truth. The method’s discriminatory performance for this area is demonstrated to be comparable to that of taking samples of the sediment. All seafloor types known to be present in the area are resolved for. Application of the method to the Stanton bank data set shows clearly separable areas that differ in seafloor composition.

Features extracted from echosounder bottom returns are compared with the ground truth in a North Sea survey area. The ground truth consists of 50 grab samples for which the grain size distribution, and the gravel and shell contents were determined. Echo envelopes are analysed for two single-beam echosounders, operated at frequencies of 66 and 150 kHz. It is shown that a set of six energetic, statistical, spectral and fractal parameters carries useful information that can be exploited for seafloor characterization and classification purposes. A quantitative comparison of the individual features with the grab sample mean grain size reveals significant correlations. The echo features are also subjected to a principal component analysis in tandem with a cluster analysis. Four sediment classes with different geo-acoustic properties are examined and compared with the grab samples and existing geological information. A subtle difference between the two sounder frequencies is observed in the rendition of an isolated trench with a soft infill of clay and Holocene channel deposits. The acoustic transition from the valley to neighbouring sand and gravel fields occurs more rapidly at the lower of the two examined frequencies. A direct comparison of the acoustic sediment classes with the ground truth reveals that the main sediment types mud, sand, and gravel are more clearly separated at 150 kHz. The acoustic bottom classification scheme also appears particularly sensitive to the presence of gravel at this sounder frequency. © 2005 Elsevier Ltd. All rights reserved.

Acoustic data measured in the ocean fluctuate due to the complex time-varying properties of the channel. When measured data are used for model-based, geo-acoustic inversion, how do acoustic fluctuations impact estimates for the seabed properties? In May 1999 SACLANT Undersea Research Center and TNO-Physics and Electronics Laboratory ~FEL!, conducted a shallow-water experiment on the Adventure Bank off the southwest coast of Sicily, Italy to assess the effects of a time-varying ocean on acoustic propagation and geo-acoustic inversion. A favorable area for acoustic propagation was identified which had slight internal wave activity and a weakly range-dependent bathymetry with sand-like bottom properties. Oceanographic and acoustic measurements were performed continuously over a 3-day period. Broadband ~0.2–3.8 kHz! acoustic signals from a bottom-moored source were transmitted over fixed paths and received on a moored vertical hydrophone array. During the transmissions extensive environmental measurements ~e.g., sound speed, current, sea-surface waveheight, etc.! were made to correlate the time-varying environmental and acoustic data. Modeled acoustic data show time variability which agrees with the measurements. Results illustrate severe problems when modeling shallow-water acoustic propagation at ranges beyond a few kilometers in the frequency band considered. Further, the acoustic fluctuations in time caused erroneous time variability in inverted seabed properties.

Detailed information about the oceanic environment is essential for many applications in the field of marine geology, marine biology, coastal engineering, and marine operations. Especially, knowledge of the sediment properties is often required. Acoustic remote sensing techniques have become highly attractive for classifying the sea bottom and mapping the distribution of sediment properties, due to their high coverage capabilities and low costs compared to common sampling methods. In 2007, the Maritime Rapid Environmental Assessment / Blue Planet trial (MREA/BP'07) was carried out in a shallow water environment with fine-grained sediments in the Mediterranean Sea, in order to investigate means of efficiently obtaining a detailed picture of the underwater environment [1]. Measurements were taken by a large set of sensors, including echosounders, seismic systems, and dedicated array configurations, thereby providing all information required to fully describe the environment. Since these systems operate at different frequencies, they provide information on the sea bottom at different depths. This gave reason for a combination of the acoustic data as taken by the different sensors. Especially, the combination of the classification of the upper sediment layer achieved by the hydrographic surveying and the sediment layering obtained from the seismic systems resulted in a well-described three-dimensional picture of the distribution of the underwater sediments [2]. In this paper we present this environmental picture in the context of geoacoustic inversion. Geoacoustic inversion techniques are an attractive means for estimating a variety of physical properties of underwater environments, such as sound speed, density, and attenuation in each sediment layer, as well as layer thicknesses. Geoacoustic inversion, however, comes at the price of high computational efforts. Especially, in cases in which large numbers of parameters need to be inverted for, finding those parameters that provide the best fit between the measurements and model predictions requires hundreds of iterations. Efficient global optimization tools can reduce these efforts. We propose that both the surveying and inversion efforts can be reduced further by carrying out geoacoustic inversion at a small number of selected sites, only. In this paper it is investigated whether a combination of hydrographic and seismic surveying is suitable for selecting areas for sub-seafloor sediment classification by geoacoustic inversion techniques.

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.

Seafloor classification using acoustic remote sensing techniques is an attractive approach due to its high coverage capabilities and limited costs compared to taking samples of the seafloor. This paper focuses on the characterization of sediments in a coastal environment by combining different hydrographic systems, which are a multibeam echosounder (MBES), a single-beam echosounder (SBES), and seismic systems. The area is located close to the west coast of Italy, southeast of Elba Island, which is known to be composed of very fine-grained material. Both MBES and SBES are, in general, high-frequency systems (> 100 kHz), providing bathymetry and backscatter information of the upper part of the sea bottom. MBES systems provide this information with a high resolution, due to the beam opening angle of typically 1°–3°, and high coverage. An SBES provides measurements directly underneath the ship only, but is widespread. For the classification by means of MBES data, we use the Bayesian approach, employing backscatter measurements per beam. For the SBES, echo shape parameters are determined and are combined in a principal component analysis (PCA). Both approaches give results that are in very good agreement with respect to the distribution of different surficial sediment types. Complementary, low-frequency seismic systems (

Shallow water naval operations require detailed knowledge of the environmental characteristics. In this context, the BP’07 experiment was carried out in the Mediterranean Sea, south-east of Elba Island, in 2007. Measurements that were taken during this experiment employ a large set of sensors, thereby providing all information required to fully describe the environment. Water depths as measured by multi beam echo sounders (MBES) are found to range from 0 to about 130 meter. The finescale topography reveals that areas of different bottom morphology are present. Information about the physical sediment properties is obtained by bottom grab samples. They indicate the seafloor in the area to be composed of very fine-grained sediments with mean grain sizes ranging from 0.5 to 8 micrometer. In addition, the MBES also allows for classifying the seafloor. The MBES classification approach taken discriminates between sediments in the most optimal way by applying the Bayes decision rule for multiple hypotheses. It employs the MBES backscatter data, averaged per beam, which are assumed to be normally distributed. For shallow water situations, this assumption no longer holds due to the limited number of independent scatter pixels in the beam footprint. Averaging over a series of pings has been applied to restore this assumption. The application of the method results in a map of the acoustic classes in this area, indicating the presence of four different seafloor types. A comparison with other results indicates a correlation between seafloor type and the presence of specific bottom features.

We present a method for accurately estimating the bathymetry from multi-beam echo-sounder (MBES) travel-time measurements in environments with large variations in the water column sound speeds (both temporally and spatially). In this type of environments the water column sound speeds at the time of the MBES transmission are often not known, preventing a reliable conversion from the measured travel-times to bathymetry. In addition, accurate sound speed information is required for the MBES beamsteering process. Situations where information regarding the prevailing sound speeds is insufficient occur, for example, in estuaries where fresh river water mixes with seawater. The method fully exploits the redundancy in the MBES measurements obtained from the overlap of adjacent swathes. It searches for those water column sound speeds that result in a maximum agreement in the bathymetry along the overlapping swathes. The Gauss-Newton method is employed for the optimization. This method is very efficient, requiring a few iterations only and therefore very well suited for processing the large datasets as typically obtained by the MBES. From simulations it is shown that the method performs well and allows for an almost exact determination of the bathymetry and sound speeds. In principle, the method eliminates the need for sound speed measurements, as long as sufficient overlap exists.

We present a method to accurately estimate the bathymetry and water sound speed in shallow waters using overlapping swaths obtained from a Multi‐Beam Echo Sounder (MBES). The method is designed to correct the errors in the bathymetry manifested mainly in the outer beams of the MBES. The errors are caused by deviations in sound speed, which occur, for example, in estuaries where fresh river water mixes with seawater. Simulations show that we are able to simultaneously estimate the mean sound speed and the bathymetry. This is accomplished by minimizing the differences between the MBES measurements in the overlap region using a Levenberg‐Marquardt optimization routine. We also present an application of the method to real data obtained at the fairway to the harbour of Rotterdam, The Netherlands. Analysis shows that the inverted sound speed agrees well with the measured sound speed at the transducer. However, the uncertainties are larger when the track‐to‐track distance is 2 times the depth. Therefore, we also discuss the optimal distance between sailed tracks. The method appears to be a promising tool for the accurate mapping of sea floors in shallow‐water areas with a complex sound‐speed profile.

For keeping the Dutch rivers suitable for commercial activities measures are required. For example, the bottom of the river Waal, connecting Rotterdam with German industrial areas, is subsiding. Since the subsidence varies along the river, dangerous shoals occur. Sediment suppletions are planned to counteract the subsidence. Appropriate suppletion material is expected to keep the bottom more stable. To monitor the suppletion effectiveness, multibeam‐echosounder (MBES) measurements are planned, allowing for simultaneous estimation of bathymetry and sediment composition. For the latter, we apply a method employing the MBES backscatter data. It estimates the number of sediment types present in the survey area and discriminates between them by applying the Bayes decision rule for multiple hypotheses, implicitly accounting for the backscatter strength ping‐to‐ping variability. The method's applicability was demonstrated in a well‐surveyed test area (North Sea). In 2007, MBES measurements were acquired at the Waal, accompanied with extensive sediment grabbing. Contrary to the test area, water depths are very shallow and significant bottom slopes exist, requiring corrections. The lower water depths correspond to smaller beam‐footprints, resulting in a higher ping‐to‐ping variability. Consequently the discriminating power between sediments will decrease. The performance of the classification method for this river environment is assessed.

For many years, waves converted from compressional to shear mode have been successfully applied in hydrocarbon exploration to image and characterize the subsurface. Since shear‐waves propagate with a velocity that is dependent on the shear modulus and are thus directly related to the shear strength of the sediment, they are very useful for geotechnical purposes. Generally, P‐wave reflection amplitudes contain S‐wave information but Riedel et al. (2001) showed that these reflections are not very sensitive to this. An alternative approach is to obtain S‐wave information directly from converted waves. However, it is not clear whether these waves can be applied for geotechnical aims. The main focus will be on understanding the dependence of mode conversion on the seismic properties. In this study, we investigate the possibility of acquiring converted waves in marine unconsolidated sediments. From our numerical experiments we found that the conversion is maximal at two angles where the smallest angle appears to be more favorable in the environment of interest. Furthermore, we show how and where converted waves can be observed on a seismogram and the optimum field configuration to acquire them.

In the paper published in the IEEE Journal of Oceanic Engineering, vol. 36, no. 2, pp. 259-271, Apr. 2011, results of applying a feature-based classification technique to single-beam echo-sounder data were presented. Comments on this contribution were presented in a correspondence paper published in the IEEE, Journal of Oceanic Engineering, vol. 37, no. 4, pp. 764-765, Oct. 2012). In this reply, the items addressed in the comments are reflected upon.

It has been theoretically shown by Wapenaar (2006) that the non-reciprocal Green's function can be retrieved with cross-correlation. Thus, interferometric techniques can be applied to a moving medium such as the atmosphere. Numerical experiments have shown that the delay times of stratospherically refracted infrasound can be obtained from cross-correlation between pairs of microbarometers. Doing so, information about the wind and temperature around the stratopause can be passively gathered from the stationary phase with, for example, the continuous noise of microbaroms. Actual measurements have been used from the Large Aperture Infrasound Array (LAIA) in the Netherlands. LAIA consists of several microbarometers with inter-station distances ranging from a few kilometers to tens of kilometers and is ideally suited to assess the results from theoretical and numerical experiments in practice. Results will be shown on the correlation length of infrasound from microbaroms and the effect of wind and temperature ...

A method has recently been developed that employs multi-beam echo-sounder backscatter data to both obtain the number of sediment classes and discriminate between them by applying the Bayes decision rule to multiple hypotheses [ Simons and Snellen, Appl. Acoust. 70, 1258–1268 (2009) ]. In deep water, the number of scatter pixels within the beam footprint is large enough to ensure Gaussian distributions for the backscatter strengths and to increase the discriminative power between acoustic classes. In very shallow water (

Compared to taking many sediment grabs, classification techniques based on acoustic remote sensing allow for significant cost reductions, providing the required information at much denser spatial scale. An attractive approach hereto is to use the multibeam‐echosounder (MBES) which measures sediment backscatter strengths as a function of angle. By maximizing the match between this backscatter strength and model results, sediment properties can be derived. The backscatter model used here accounts for both surface roughness and volume scattering. MBES measurements (300 kHz) were taken in the Cleaver‐Bank area (North Sea), characterized by a wide variety of sediment types for which a large number of grabs is collected. For the majority of the sediment types occurring, well‐defined single‐sediment‐type areas can be discerned. Based on MBES measurements in these areas and corresponding geotechnical sediment parameters provided by grab grain size analysis, calibration curves are derived, representing the mismatc...

For investigating the dispersion behavior of sediment sound speeds, broadband sound speed measurements are required. Sediment sound speed dispersion models predict the largest sound speed variations typically at low frequencies. Aircraft fly‐over noise acquired on receivers placed in sediment proved to be a promising approach towards broadband low‐frequency measurements. Since the magnitude of the Doppler‐shift depends on sound speed at the receiver position, the observed Doppler‐shift can be used to derive the sound speed. We propose a new approach using the entire time series instead of employing only the two observed frequencies at approaching and departing. It is based on a model for received signals, accounting for the Doppler‐shifted frequency. By maximizing the match between modeled and measured time series as a function of sound speed and frequency in a least‐squares framework, estimates of the frequency‐dependent sound speed are obtained. The method is applied to data acquired at receivers placed...

Several approaches exist towards seafloor classification using high‐frequency backscattering measurements. The classification approach taken in this paper is a model‐based classification employing backscatter data measured by a multibeam‐echosounder (MBES) system. The method discriminates between sediments in the most optimal way by applying the Bayes decision rule for multiple hypotheses, implicitly accounting for the backscatter strength ping‐to‐ping variability. The method's applicability for seafloor classification has been demonstrated by using 300 kHz MBES data collected in the Cleaver‐Bank area (North Sea). The area is well‐known from a geological point of view due to extensive sampling campaigns and is characterized by a wide variety of seafloor types. Here we apply the classification method to MBES data acquired in the Oosterschelde estuary (the Netherlands) which is known to contain mussel culture spots. Also recordings using a video camera towed close to the seafloor and core measurements have ...

For shallow‐water naval operations, obtaining rapidly an accurate picture of the environmental circumstances often is of high importance. Hereto a multi‐sensor approach is required. In this context, the MREA/BP'07 experiment has been carried out south of Elba (Mediterranean Sea), where several techniques of environmental characterization covering the fields of underwater acoustics, physical oceanography and geophysics have been combined [Le Gac&Hermand, 2007]. The required information typically concerns water‐column properties, sea surface roughness, and sediment geo‐acoustic properties. Estimating these geo‐acoustic parameters from inversion of acoustic data received on drifting sparse arrays has proved to be a promising approach. Part of MREA/BP'07 was therefore dedicated to this type of measurement. For validating the resulting geo‐acoustic estimates sediment samples were collected. Additionally, measurements were carried out using a multibeam‐echosounder. This system provides depth information, but al...

In October 1997, shallow‐water experiments were carried out on the Bank of Adventure southwest of Sicily. The purpose of this trial was to obtain acoustic data in range‐dependent environments for testing and further developing matched‐field inversion methods to determine geoacoustic properties of the sea floor. On one track of the experiments, an acoustic source was towed over a sloping bottom with a water depth varying from 100 m at the vertical receiving array to 75 m at the end of the 7‐km track. The array comprises 64 hydrophones and covered the central 62 m of the water column. At several source‐receiver ranges, the source (towed below the thermocline) transmitted a multitone signal in the band 50–300 Hz. Detailed sound‐speed profiling of the water column and seismic profiling of the bottom were performed along the track. Broadband inversion of the acoustic data was done using a genetic algorithm as the global search method. The forward model uses standard adiabatic mode theory. Inversion results are shown for different geoacoustic models with different complexity concerning sediment layering. The bottom parameters obtained agree well with the results of the seismic analysis and its geological interpretation.