Special Issue "Geological Seafloor Mapping"

A special issue of Geosciences (ISSN 2076-3263). This special issue belongs to the section "Geophysics".

Deadline for manuscript submissions: closed (31 March 2019).

Special Issue Editors

Dr. Markus Diesing
Website
Guest Editor
Geological Survey of Norway, Leiv Eirikssons vei 39, 7040 Trondheim, Norway
Interests: marine geology; sediments; sediment dynamics; habitats; cold-water coral reefs; organic carbon; acoustic remote sensing; mapping; spatial prediction; supervised classification; GEOBIA
Dr. Peter Feldens
Website
Guest Editor
Leibniz-Institute for Baltic Sea Research Warnemünde, Seestraße 15, 18119 Rostock, Germany
Interests: marine geology; habitat mapping; marine geomorphology; geology of the Baltic Sea; sediment dynamics

Special Issue Information

Dear Colleagues,

The ocean floor is vast, yet largely uncharted. Although an ambitious pledge was made to map the entire ocean floor by the year 2030, this only pertains to the bathymetry of the oceans. Mapping the geological makeup of the seafloor remains one of the great challenges in marine geoscience. Recent advances in data acquisition, processing, analysis and dissemination should, however, put us in a better position to deliver accurate and detailed maps of seafloor sediment and substratum types.

A significant part of the analysis rests on the acoustic backscatter intensity of the seafloor gathered with sidescan sonars and, more recently, multibeam echosounders (MBES). We have witnessed significant advances in this field of technology in recent years, including global efforts to standardise the collection and processing of calibrated backscatter data and the introduction of multispectral MBES for seafloor mapping. Such advances will ultimately lead to better maps of the geology of the seafloor and the distribution of benthic habitats.

Progress has also been made by introducing methods of image analysis, spatial prediction and machine learning, widely utilised in terrestrial mapping applications, to geological seafloor mapping. These methods have several advantages over traditional mapping ‘by eye’, including repeatability, time-savings, cost-effectiveness and the provision of estimates of accuracy. More recently, attempts have been made in spatially predicting quantitative sediment properties (e.g., grain-size composition) rather than sediment classes. Such studies can also shed light on the relationships between sediment properties and the marine environmental drivers that determine the distribution of sediments on the seafloor.

It is generally acknowledged that due to the high costs involved in collecting marine datasets we should ‘collect once, use many times’. Efficient systems for data search and retrieval make it now much easier to search for relevant datasets and download them from databases.

The aim of this Special Issue of Geosciences is to showcase the latest developments in the field of geological seafloor mapping. We specifically invite contributions addressing the following aspects:

  • Studies assessing the potential of multispectral MBES for geological seafloor mapping
  • Systematic and quantitative comparisons of mapping approaches
  • The impact of spatial scale on mapping performance
  • The assessment and communication of mapping uncertainty and confidence
  • Quantification of the relationships between sediments and environmental drivers
  • Quantification of the relationships between sediments, benthic organisms, and backscatter
  • Case studies from local to global scales making innovative use of legacy data from data repositories

Dr. Markus Diesing
Dr. Peter Feldens
Guest Editors

Manuscript Submission Information

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Keywords

  • Marine geology
  • Seafloor mapping
  • Sediment
  • Benthic habitats
  • Multibeam echosounder
  • Acoustic backscatter
  • Spatial prediction
  • Image analysis
  • Machine learning
  • Accuracy
  • Confidence
  • Spatial scale

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Published Papers (26 papers)

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Research

Open AccessArticle
Impact of Sparse Benthic Life on Seafloor Roughness and High-Frequency Acoustic Scatter
Geosciences 2019, 9(10), 454; https://doi.org/10.3390/geosciences9100454 - 22 Oct 2019
Cited by 1
Abstract
Quantitative acoustic marine habitat mapping needs to consider the impact of macrobenthic organisms on backscatter data. However, the sensitivity of hydroacoustic systems to epibenthic life is poorly constrained. This study explores the impact of a benthic community with sparse abundance on seafloor microroughness [...] Read more.
Quantitative acoustic marine habitat mapping needs to consider the impact of macrobenthic organisms on backscatter data. However, the sensitivity of hydroacoustic systems to epibenthic life is poorly constrained. This study explores the impact of a benthic community with sparse abundance on seafloor microroughness and acoustic backscatter at a sandy seafloor in the German North Sea. A multibeam echo sounder survey was ground-truthed by lander measurements combining a laser line scanner with sub-mm resolution and broad-band acoustic transducers. Biotic and abiotic features and spatial roughness parameters were determined by the laser line scanner. At the same locations, acoustic backscatter was measured and compared with an acoustic scatter model utilizing the small-roughness perturbation approximation. Results of the lander experiments show that a coverage with epibenthic features of 1.6% increases seafloor roughness at spatial wavelengths between 0.005–0.03 m, increasing both spectral slope and intercept. Despite the fact that a strong impact on backscatter was predicted by the acoustic model based on measured roughness parameters, only a minor (1.1 dB) change of backscatter was actually observed during both the lander experiments and the ship-based acoustic survey. The results of this study indicate that benthic coverage of less than 1.6% is insufficient to be detected by current acoustic remote sensing. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle
Bathymetry and Geomorphology of Shelikof Strait and the Western Gulf of Alaska
Geosciences 2019, 9(10), 409; https://doi.org/10.3390/geosciences9100409 - 21 Sep 2019
Cited by 1
Abstract
We defined the bathymetry of Shelikof Strait and the western Gulf of Alaska (WGOA) from the edges of the land masses down to about 7000 m deep in the Aleutian Trench. This map was produced by combining soundings from historical National Ocean Service [...] Read more.
We defined the bathymetry of Shelikof Strait and the western Gulf of Alaska (WGOA) from the edges of the land masses down to about 7000 m deep in the Aleutian Trench. This map was produced by combining soundings from historical National Ocean Service (NOS) smooth sheets (2.7 million soundings); shallow multibeam and LIDAR (light detection and ranging) data sets from the NOS and others (subsampled to 2.6 million soundings); and deep multibeam (subsampled to 3.3 million soundings), single-beam, and underway files from fisheries research cruises (9.1 million soundings). These legacy smooth sheet data, some over a century old, were the best descriptor of much of the shallower and inshore areas, but they are superseded by the newer multibeam and LIDAR, where available. Much of the offshore area is only mapped by non-hydrographic single-beam and underway files. We combined these disparate data sets by proofing them against their source files, where possible, in an attempt to preserve seafloor features for research purposes. We also attempted to minimize bathymetric data errors so that they would not create artificial seafloor features that might impact such analyses. The main result of the bathymetry compilation is that we observe abundant features related to glaciation of the shelf of Alaska during the Last Glacial Maximum including abundant end moraines, some medial moraines, glacial lineations, eskers, iceberg ploughmarks, and two types of pockmarks. We developed an integrated onshore–offshore geomorphic map of the region that includes glacial flow directions, moraines, and iceberg ploughmarks to better define the form and flow of former ice masses. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle
Limitations of Boulder Detection in Shallow Water Habitats Using High-Resolution Sidescan Sonar Images
Geosciences 2019, 9(9), 390; https://doi.org/10.3390/geosciences9090390 - 06 Sep 2019
Cited by 2
Abstract
Stones and boulders in shallow waters (0–10 m water depth) form complex geo-habitats, serving as a hardground for many benthic species, and are important contributors to coastal biodiversity and high benthic production. This study focuses on limitations in stone and boulder detection using [...] Read more.
Stones and boulders in shallow waters (0–10 m water depth) form complex geo-habitats, serving as a hardground for many benthic species, and are important contributors to coastal biodiversity and high benthic production. This study focuses on limitations in stone and boulder detection using high-resolution sidescan sonar images in shallow water environments of the southwestern Baltic Sea. Observations were carried out using sidescan sonars operating with frequencies from 450 kHz up to 1 MHz to identify individual stones and boulders within different levels of resolution. In addition, sidescan sonar images were generated using varying survey directions for an assessment of range effects. The comparison of images of different resolutions reveals considerable discrepancies in the numbers of detectable stones and boulders, and in their distribution patterns. Results on the detection of individual stones and boulders at approximately 0.04 m/pixel resolution were compared to common discretizations: it was shown that image resolutions of 0.2 m/pixel may underestimate available hard-ground settlement space by up to 42%. If methodological constraints are known and considered, detailed information about individual stones and boulders, and potential settlement space for marine organisms, can be derived. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle
Seafloor Characterization Using Multibeam Echosounder Backscatter Data: Methodology and Results in the North Sea
Geosciences 2019, 9(7), 292; https://doi.org/10.3390/geosciences9070292 - 30 Jun 2019
Cited by 4
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle
A Spatially Explicit Comparison of Quantitative and Categorical Modelling Approaches for Mapping Seabed Sediments Using Random Forest
Geosciences 2019, 9(6), 254; https://doi.org/10.3390/geosciences9060254 - 06 Jun 2019
Cited by 4
Abstract
Seabed sediment composition is an important component of benthic habitat and there are many approaches for producing maps that convey sediment information to marine managers. Random Forest is a popular statistical method for thematic seabed sediment mapping using both categorical and quantitative supervised [...] Read more.
Seabed sediment composition is an important component of benthic habitat and there are many approaches for producing maps that convey sediment information to marine managers. Random Forest is a popular statistical method for thematic seabed sediment mapping using both categorical and quantitative supervised modelling approaches. This study compares the performance and qualities of these Random Forest approaches to predict the distribution of fine-grained sediments from grab samples as one component of a multi-model map of sediment classes in Frobisher Bay, Nunavut, Canada. The second component predicts the presence of coarse substrates from underwater video. Spatial and non-spatial cross-validations were conducted to evaluate the performance of categorical and quantitative Random Forest models and maps were compared to determine differences in predictions. While both approaches seemed highly accurate, the non-spatial cross-validation suggested greater accuracy using the categorical approach. Using a spatial cross-validation, there was little difference between approaches—both showed poor extrapolative performance. Spatial cross-validation methods also suggested evidence of overfitting in the coarse sediment model caused by the spatial dependence of transect samples. The quantitative modelling approach was able to predict rare and unsampled sediment classes but the flexibility of probabilistic predictions from the categorical approach allowed for tuning to maximize extrapolative performance. Results demonstrate that the apparent accuracies of these models failed to convey important differences between map predictions and that spatially explicit evaluation strategies may be necessary for evaluating extrapolative performance. Differentiating extrapolative from interpolative prediction can aid in selecting appropriate modelling methods. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle
High-Silica Lava Morphology at Ocean Spreading Ridges: Machine-Learning Seafloor Classification at Alarcon Rise
Geosciences 2019, 9(6), 245; https://doi.org/10.3390/geosciences9060245 - 01 Jun 2019
Cited by 2
Abstract
The oceanic crust consists mostly of basalt, but more evolved compositions may be far more common than previously thought. To aid in distinguishing rhyolite from basaltic lava and help guide sampling and understand spatial distribution, we constructed a classifier using neural networks and [...] Read more.
The oceanic crust consists mostly of basalt, but more evolved compositions may be far more common than previously thought. To aid in distinguishing rhyolite from basaltic lava and help guide sampling and understand spatial distribution, we constructed a classifier using neural networks and fuzzy inference to recognize rhyolite from its lava morphology in sonar data. The Alarcon Rise is ideal to study the relationship between lava flow morphology and composition, because it exhibits a full range of lava compositions in a well-mapped ocean ridge segment. This study shows that the most dramatic geomorphic threshold in submarine lava separates rhyolitic lava from lower-silica compositions. Extremely viscous rhyolite erupts as jagged lobes and lava branches in submarine environments. An automated classification of sonar data is a useful first-order tool to differentiate submarine rhyolite flows from widespread basalts, yielding insights into eruption, emplacement, and architecture of the ocean crust. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle
How Do Continuous High-Resolution Models of Patchy Seabed Habitats Enhance Classification Schemes?
Geosciences 2019, 9(5), 237; https://doi.org/10.3390/geosciences9050237 - 23 May 2019
Cited by 3
Abstract
Predefined classification schemes and fixed geographic scales are often used to simplify and cost-effectively map the spatial complexity of nature. These simplifications can however limit the usefulness of the mapping effort for users who need information across a different range of thematic and [...] Read more.
Predefined classification schemes and fixed geographic scales are often used to simplify and cost-effectively map the spatial complexity of nature. These simplifications can however limit the usefulness of the mapping effort for users who need information across a different range of thematic and spatial resolutions. We demonstrate how substrate and biological information from point samples and photos, combined with continuous multibeam data, can be modeled to predictively map percentage cover conforming with multiple existing classification schemes (i.e., HELCOM HUB; Natura 2000), while also providing high-resolution (5 m) maps of individual substrate and biological components across a 1344 km2 offshore bank in the Baltic Sea. Data for substrate and epibenthic organisms were obtained from high-resolution photo mosaics, sediment grab samples, legacy data and expert annotations. Environmental variables included pixel and object based metrics at multiple scales (0.5 m–2 km), which improved the accuracy of models. We found that using Boosted Regression Trees (BRTs) to predict continuous models of substrate and biological components provided additional detail for each component without losing accuracy in the classified maps, compared with a thematic model. Results demonstrate the sensitivity of habitat maps to the effects of spatial and thematic resolution and the importance of high-resolution maps to management applications. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle
New Feature Classes for Acoustic Habitat Mapping—A Multibeam Echosounder Point Cloud Analysis for Mapping Submerged Aquatic Vegetation (SAV)
Geosciences 2019, 9(5), 235; https://doi.org/10.3390/geosciences9050235 - 23 May 2019
Cited by 4
Abstract
A new method for multibeam echosounder (MBES) data analysis is presented with the aim of improving habitat mapping, especially when considering submerged aquatic vegetation (SAV). MBES data were acquired with 400 kHz in 1–8 m water depth with a spatial resolution in the [...] Read more.
A new method for multibeam echosounder (MBES) data analysis is presented with the aim of improving habitat mapping, especially when considering submerged aquatic vegetation (SAV). MBES data were acquired with 400 kHz in 1–8 m water depth with a spatial resolution in the decimeter scale. The survey area was known to be populated with the seagrass Zostera marina and the bathymetric soundings were highly influenced by this habitat. The depth values often coincide with the canopy of the seagrass. Instead of classifying the data with a digital terrain model and the given derivatives, we derive predictive features from the native point cloud of the MBES soundings in a similar way to terrestrial LiDAR data analysis. We calculated the eigenvalues to derive nine characteristic features, which include linearity, planarity, and sphericity. The features were calculated for each sounding within a cylindrical neighborhood of 0.5 m radius and holding 88 neighboring soundings, on average, during our survey. The occurrence of seagrass was ground-truthed by divers and aerial photography. A data model was constructed and we applied a random forest machine learning supervised classification to predict between the two cases of “seafloor” and “vegetation”. Prediction by linearity, planarity, and sphericity resulted in 88.5% prediction accuracy. After constructing the higher-order eigenvalue derivatives and having the nine features available, the model resulted in 96% prediction accuracy. This study outlines for the first time that valuable feature classes can be derived from MBES point clouds—an approach that could substantially improve bathymetric measurements and habitat mapping. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle
Optimizing an Inner-Continental Shelf Geologic Framework Investigation through Data Repurposing and Machine Learning
Geosciences 2019, 9(5), 231; https://doi.org/10.3390/geosciences9050231 - 21 May 2019
Cited by 2
Abstract
The U.S. Geological Survey (USGS) and the National Oceanic Atmospheric Administration (NOAA) have collected approximately 5400 km2 of geophysical and hydrographic data on the Atlantic continental shelf between Delaware and Virginia over the past decade and a half. Although originally acquired for [...] Read more.
The U.S. Geological Survey (USGS) and the National Oceanic Atmospheric Administration (NOAA) have collected approximately 5400 km2 of geophysical and hydrographic data on the Atlantic continental shelf between Delaware and Virginia over the past decade and a half. Although originally acquired for different objectives, the comprehensive coverage and variety of data (bathymetry, backscatter, imagery and physical samples) presents an opportunity to merge collections and create high-resolution, broad-scale geologic maps of the seafloor. This compilation of data repurposes hydrographic data, expands the area of geologic investigation, highlights the versatility of mapping data, and creates new geologic products that would not have been independently possible. The data are classified using a variety of machine learning algorithms, including unsupervised and supervised methods. Four unique classes were targeted for classification, and source data include bathymetry, backscatter, slope, curvature, and shaded-relief. A random forest classifier used on all five source data layers was found to be the most accurate method for these data. Geomorphologic and sediment texture maps are derived from the classified acoustic data using over 200 ground truth samples. The geologic data products can be used to identify sediment sources, inform resource management, link seafloor environments to sediment texture, improve our understanding of the seafloor structure and sediment pathways, and demonstrate how ocean mapping resources can be useful beyond their original intent to maximize the footprint and scientific impact of a study. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle
Automated Stone Detection on Side-Scan Sonar Mosaics Using Haar-Like Features
Geosciences 2019, 9(5), 216; https://doi.org/10.3390/geosciences9050216 - 11 May 2019
Cited by 3
Abstract
Stony grounds form important habitats in the marine environment, especially for sessile benthic organisms. For the purpose of habitat demarcation and monitoring, knowledge of the position and abundance of individual stones is necessary. This is especially the case in areas with a scattered [...] Read more.
Stony grounds form important habitats in the marine environment, especially for sessile benthic organisms. For the purpose of habitat demarcation and monitoring, knowledge of the position and abundance of individual stones is necessary. This is especially the case in areas with a scattered occurrence of stones in an environment which is otherwise characterized by relatively mobile sandy sediments. Exposed stones can be detected using side-scan sonar (SSS) data. However, apart from laborious manual identification, there is as yet no automated or semi-automated method available for a fast and spatially resolved detection of stones. In this study, a Haar-like feature detector was trained to identify individual stones on an SSS mosaic (~12 km2) showing heterogeneous sediment distribution. The results of this method were compared with those of manually derived stones. Our study shows that the Haar-like feature detector was able to detect up to 62% of the overall occurrence of stones within the study area. Even though the sheer number of correctly identified stones was influenced by, e.g., the type of sediments and the number of grey values of the mosaic, Haar-like feature detectors provide a relatively easy and fast method to identify stones on SSS mosaics when compared to the manual investigation. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle
Sources and Impacts of Bottom Slope Uncertainty on Estimation of Seafloor Backscatter from Swath Sonars
Geosciences 2019, 9(4), 183; https://doi.org/10.3390/geosciences9040183 - 20 Apr 2019
Cited by 3
Abstract
Seafloor backscatter data from multibeam echosounders are now widely used in seafloor characterization studies. Accurate and repeatable measurements are essential for advancing the success of these techniques. This paper explores the impact of uncertainty in our knowledge of the local seafloor slope on [...] Read more.
Seafloor backscatter data from multibeam echosounders are now widely used in seafloor characterization studies. Accurate and repeatable measurements are essential for advancing the success of these techniques. This paper explores the impact of uncertainty in our knowledge of the local seafloor slope on the overall accuracy of the backscatter measurement. Amongst the various sources of slope uncertainty studied, the impact of bathymetric uncertainty and scale were identified as the major sources of slope uncertainty. The bottom slope affects two important corrections needed for estimating seafloor backscatter: (1) The insonified area and; (2) the seafloor incidence angle. The impacts of these slope-related uncertainty sources were quantified for a shallow water multibeam survey. The results show that the most significant uncertainty in backscatter data arises when seafloor slope is not accounted for or when low-resolution bathymetry is used to estimate seafloor slope. This effect is enhanced in rough seafloors. A standard method of seafloor slope correction is proposed to achieve repeatable and accurate backscatter results. Additionally, a standard data package, including metadata describing the slope corrections applied, needs to accompany backscatter results and should include details of the slope estimation method and resolution of the bathymetry used. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle
Legacy Data: How Decades of Seabed Sampling Can Produce Robust Predictions and Versatile Products
Geosciences 2019, 9(4), 182; https://doi.org/10.3390/geosciences9040182 - 19 Apr 2019
Cited by 3
Abstract
Sediment maps developed from categorical data are widely applied to support marine spatial planning across various fields. However, deriving maps independently of sediment classification potentially improves our understanding of environmental gradients and reduces issues of harmonising data across jurisdictional boundaries. As the groundtruth [...] Read more.
Sediment maps developed from categorical data are widely applied to support marine spatial planning across various fields. However, deriving maps independently of sediment classification potentially improves our understanding of environmental gradients and reduces issues of harmonising data across jurisdictional boundaries. As the groundtruth samples are often measured for the fractions of mud, sand and gravel, this data can be utilised more effectively to produce quantitative maps of sediment composition. Using harmonised data products from a range of sources including the European Marine Observation and Data Network (EMODnet), spatial predictions of these three sediment fractions were generated for the north-west European continental shelf using the random forest algorithm. Once modelled these sediment fraction maps were classified using a range of schemes to show the versatility of such an approach, and spatial accuracy maps were generated to support their interpretation. The maps produced in this study are to date the highest resolution quantitative sediment composition maps that have been produced for a study area of this extent and are likely to be of interest for a wide range of applications such as ecological and biophysical studies. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle
Developing an Optimal Spatial Predictive Model for Seabed Sand Content Using Machine Learning, Geostatistics, and Their Hybrid Methods
Geosciences 2019, 9(4), 180; https://doi.org/10.3390/geosciences9040180 - 17 Apr 2019
Cited by 2
Abstract
Seabed sediment predictions at regional and national scales in Australia are mainly based on bathymetry-related variables due to the lack of backscatter-derived data. In this study, we applied random forests (RFs), hybrid methods of RF and geostatistics, and generalized boosted regression modelling (GBM), [...] Read more.
Seabed sediment predictions at regional and national scales in Australia are mainly based on bathymetry-related variables due to the lack of backscatter-derived data. In this study, we applied random forests (RFs), hybrid methods of RF and geostatistics, and generalized boosted regression modelling (GBM), to seabed sand content point data and acoustic multibeam data and their derived variables, to develop an accurate model to predict seabed sand content at a local scale. We also addressed relevant issues with variable selection. It was found that: (1) backscatter-related variables are more important than bathymetry-related variables for sand predictive modelling; (2) the inclusion of highly correlated predictors can improve predictive accuracy; (3) the rank orders of averaged variable importance (AVI) and accuracy contribution change with input predictors for RF and are not necessarily matched; (4) a knowledge-informed AVI method (KIAVI2) is recommended for RF; (5) the hybrid methods and their averaging can significantly improve predictive accuracy and are recommended; (6) relationships between sand and predictors are non-linear; and (7) variable selection methods for GBM need further study. Accuracy-improved predictions of sand content are generated at high resolution, which provide important baseline information for environmental management and conservation. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle
Detection of Boulders in Side Scan Sonar Mosaics by a Neural Network
Geosciences 2019, 9(4), 159; https://doi.org/10.3390/geosciences9040159 - 03 Apr 2019
Cited by 5
Abstract
Boulders provide ecologically important hard grounds in shelf seas, and form protected habitats under the European Habitats Directive. Boulders on the seafloor can usually be recognized in backscatter mosaics due to a characteristic pattern of high backscatter intensity followed by an acoustic shadow. [...] Read more.
Boulders provide ecologically important hard grounds in shelf seas, and form protected habitats under the European Habitats Directive. Boulders on the seafloor can usually be recognized in backscatter mosaics due to a characteristic pattern of high backscatter intensity followed by an acoustic shadow. The manual identification of boulders on mosaics is tedious and subjective, and thus could benefit from automation. In this study, we train an object detection framework, RetinaNet, based on a neural network backbone, ResNet, to detect boulders in backscatter mosaics derived from a sidescan-sonar operating at 384 kHz. A training dataset comprising 4617 boulders and 2005 negative examples similar to boulders was used to train RetinaNet. The trained model was applied to a test area located in the Kriegers Flak area (Baltic Sea), and the results compared to mosaic interpretation by expert analysis. Some misclassification of water column noise and boundaries of artificial plough marks occurs, but the results of the trained model are comparable to the human interpretation. While the trained model correctly identified a higher number of boulders, the human interpreter had an advantage at recognizing smaller objects comprising a bounding box of less than 7 × 7 pixels. Almost identical performance between the best model and expert analysis was found when classifying boulder density into three classes (0, 1–5, more than 5) over 10,000 m2 areas, with the best performing model reaching an agreement with the human interpretation of 90%. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle
Seafloor Classification in a Sand Wave Environment on the Dutch Continental Shelf Using Multibeam Echosounder Backscatter Data
Geosciences 2019, 9(3), 142; https://doi.org/10.3390/geosciences9030142 - 23 Mar 2019
Cited by 4
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle
Techniques for Classifying Seabed Morphology and Composition on a Subtropical-Temperate Continental Shelf
Geosciences 2019, 9(3), 141; https://doi.org/10.3390/geosciences9030141 - 22 Mar 2019
Cited by 4
Abstract
In 2017, the New South Wales (NSW) Office of Environment and Heritage (OEH) initiated a state-wide mapping program, SeaBed NSW, which systematically acquires high-resolution (2–5 m cell size) multibeam echosounder (MBES) and marine LiDAR data along more than 2000 km of the subtropical-to-temperate [...] Read more.
In 2017, the New South Wales (NSW) Office of Environment and Heritage (OEH) initiated a state-wide mapping program, SeaBed NSW, which systematically acquires high-resolution (2–5 m cell size) multibeam echosounder (MBES) and marine LiDAR data along more than 2000 km of the subtropical-to-temperate southeast Australian continental shelf. This program considerably expands upon existing efforts by OEH to date, which have mapped approximately 15% of NSW waters with these technologies. The delivery of high volumes of new data, together with the vast repository of existing data, highlights the need for a standardised, automated approach to classify seabed data. Here we present a methodological approach with new procedures to semi-automate the classification of high-resolution bathymetry and intensity (backscatter and reflectivity) data into a suite of data products including classifications of seabed morphology (landforms) and composition (substrates, habitats, geomorphology). These methodologies are applied to two case study areas representing newer (Wollongong, NSW) and older (South Solitary Islands, NSW) MBES datasets to assess the transferability of classification techniques across input data of varied quality. The suite of seabed classifications produced by this study provide fundamental baseline data on seabed shape, complexity, and composition which will inform regional risk assessments and provide insights into biodiversity and geodiversity. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle
Multispectral Multibeam Echo Sounder Backscatter as a Tool for Improved Seafloor Characterization
Geosciences 2019, 9(3), 126; https://doi.org/10.3390/geosciences9030126 - 12 Mar 2019
Cited by 10
Abstract
The establishment of multibeam echosounders (MBES), as a mainstream tool in ocean mapping, has facilitated integrative approaches towards nautical charting, benthic habitat mapping, and seafloor geotechnical surveys. The combined acoustic response of the seabed and the subsurface can vary with MBES operating frequency. [...] Read more.
The establishment of multibeam echosounders (MBES), as a mainstream tool in ocean mapping, has facilitated integrative approaches towards nautical charting, benthic habitat mapping, and seafloor geotechnical surveys. The combined acoustic response of the seabed and the subsurface can vary with MBES operating frequency. At worst, this can make for difficulties in merging the results from different mapping systems or mapping campaigns. However, at best, having observations of the same seafloor at different acoustic wavelengths allows for increased discriminatory power in seabed classification and characterization efforts. Here, we present the results from trials of a multispectral multibeam system (R2Sonic 2026 MBES, manufactured by R2Sonic, LLC, Austin, TX, USA) in the Bedford Basin, Nova Scotia. In this system, the frequency can be modified on a ping-by-ping basis, which can provide multi-spectral acoustic measurements with a single pass of the survey platform. The surveys were conducted at three operating frequencies (100, 200, and 400 kHz), and the resulting backscatter mosaics revealed differences in parts of the survey area between the frequencies. Ground validation surveys using a combination of underwater video transects and benthic grab and core sampling confirmed that these differences were due to coarse, dredge spoil material underlying a surface cover of mud. These innovations offer tremendous potential for application in the area of seafloor geological and benthic habitat mapping. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle
Picking Up the Pieces—Harmonising and Collating Seabed Substrate Data for European Maritime Areas
Geosciences 2019, 9(2), 84; https://doi.org/10.3390/geosciences9020084 - 13 Feb 2019
Cited by 6
Abstract
The poor access to data on the marine environment is a handicap to government decision-making, a barrier to scientific understanding and an obstacle to economic growth. In this light, the European Commission initiated the European Marine Observation and Data Network (EMODnet) in 2009 [...] Read more.
The poor access to data on the marine environment is a handicap to government decision-making, a barrier to scientific understanding and an obstacle to economic growth. In this light, the European Commission initiated the European Marine Observation and Data Network (EMODnet) in 2009 to assemble and disseminate hitherto dispersed marine data. In the ten years since then, EMODnet has become a key producer of publicly available, harmonised datasets covering broad areas. This paper describes the methodologies applied in EMODnet Geology project to produce fully populated GIS layers of seabed substrate distribution for the European marine areas. We describe steps involved in translating national seabed substrate data, conforming to various standards, into a uniform EMODnet substrate classification scheme (i.e., the Folk sediment classification). Rock and boulders form an additional substrate class. Seabed substrate data products at scales of 1:250,000 and 1:1 million, compiled using descriptions and analyses of seabed samples as well as interpreted acoustic images, cover about 20% and 65% of the European maritime areas, respectively. A simple confidence assessment, based on sample and acoustic coverage, is helpful in identifying data gaps. The harmonised seabed substrate maps are particularly useful in supraregional, transnational and pan-European marine spatial planning. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle
Sub-Antarctic Auckland Islands Seafloor Mapping Investigations Using Legacy Data
Geosciences 2019, 9(2), 56; https://doi.org/10.3390/geosciences9020056 - 23 Jan 2019
Abstract
This paper demonstrates the richness of data collected for nautical charting and considers ways in which chart data can support scientific research, through a case study of two modern navigation surveys undertaken in the Auckland Islands. While legacy charts have coarser resolution, and [...] Read more.
This paper demonstrates the richness of data collected for nautical charting and considers ways in which chart data can support scientific research, through a case study of two modern navigation surveys undertaken in the Auckland Islands. While legacy charts have coarser resolution, and may synthesize different epochs together into one final product, we examine how they may be used on their own and to complement more recent hydrographic surveys. We argue that the hydrographic and ancillary data, only a fraction of which appears on the final chart, also has scientific value and that the hydrographic surveying principles applied during data collection are equally relevant for all seabed mapping. While the benefits of full bottom coverage obtained by state of-the-art multibeam surveys are clear, there is much more to be discovered in legacy singlebeam datasets than what is displayed on the nautical chart alone. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle
Insights into the Short-Term Tidal Variability of Multibeam Backscatter from Field Experiments on Different Seafloor Types
Geosciences 2019, 9(1), 34; https://doi.org/10.3390/geosciences9010034 - 10 Jan 2019
Cited by 6
Abstract
Three experiments were conducted in the Belgian part of the North Sea to investigate short-term variation in seafloor backscatter strength (BS) obtained with multibeam echosounders (MBES). Measurements were acquired on predominantly gravelly (offshore) and sandy and muddy (nearshore) areas. Kongsberg EM3002 and EM2040 [...] Read more.
Three experiments were conducted in the Belgian part of the North Sea to investigate short-term variation in seafloor backscatter strength (BS) obtained with multibeam echosounders (MBES). Measurements were acquired on predominantly gravelly (offshore) and sandy and muddy (nearshore) areas. Kongsberg EM3002 and EM2040 dual MBES were used to carry out repeated 300-kHz backscatter measurements over tidal cycles (~13 h). Measurements were analysed in complement to an array of ground-truth variables on sediment and current nature and dynamics. Seafloor and water-column sampling was used, as well as benthic landers equipped with different oceanographic sensors. Both angular response (AR) and mosaicked BS were derived. Results point at the high stability of the seafloor BS in the gravelly area (<0.5 dB variability at 45° incidence) and significant variability in the sandy and muddy areas with envelopes of variability >2 dB and 4 dB at 45° respectively. The high-frequency backscatter sensitivity and short-term variability are interpreted and discussed in the light of the available ground-truth data for the three experiments. The envelopes of variability differed considerably between areas and were driven either by external sources (not related to the seafloor sediment), or by intrinsic seafloor properties (typically for dynamic nearshore areas) or by a combination of both. More specifically, within the gravelly areas with a clear water mass, seafloor BS measurements where unambiguous and related directly to the water-sediment interface. Within the sandy nearshore area, the BS was shown to be strongly affected by roughness polarization processes, particularly due to along- and cross-shore current dynamics, which were responsible for the geometric reorganization of the morpho-sedimentary features. In the muddy nearshore area, the BS fluctuation was jointly driven by high-concentrated mud suspension dynamics, together with surficial substrate changes, as well as by water turbidity, increasing the transmission losses. Altogether, this shows that end-users and surveyors need to consider the complexity of the environment since its dynamics may have severe repercussions on the interpretation of BS maps and change-detection applications. Furthermore, the experimental observations revealed the sensitivity of high-frequency BS values to an array of specific configurations of the natural water-sediment interface which are of interest for monitoring applications elsewhere. This encourages the routine acquisition of different and concurrent environmental data together with MBES survey data. In view of promising advances in MBES absolute calibration allowing more straightforward data comparison, further investigations of the drivers of BS variability and sensitivity are required. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle
A Multispectral Bayesian Classification Method for Increased Acoustic Discrimination of Seabed Sediments Using Multi-Frequency Multibeam Backscatter Data
Geosciences 2018, 8(12), 455; https://doi.org/10.3390/geosciences8120455 - 04 Dec 2018
Cited by 15
Abstract
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 [...] Read more.
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; (2) the use of multiple frequencies allows for a better acoustic discrimination of seabed sediments than single-frequency data; 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. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle
The Hyper-Angular Cube Concept for Improving the Spatial and Acoustic Resolution of MBES Backscatter Angular Response Analysis
Geosciences 2018, 8(12), 446; https://doi.org/10.3390/geosciences8120446 - 30 Nov 2018
Cited by 2
Abstract
This study presents a novel approach, based on high-dimensionality hydro-acoustic data, for improving the performance of angular response analysis (ARA) on multibeam backscatter data in terms of acoustic class separation and spatial resolution. This approach is based on the hyper-angular cube (HAC) data [...] Read more.
This study presents a novel approach, based on high-dimensionality hydro-acoustic data, for improving the performance of angular response analysis (ARA) on multibeam backscatter data in terms of acoustic class separation and spatial resolution. This approach is based on the hyper-angular cube (HAC) data structure which offers the possibility to extract one angular response from each cell of the cube. The HAC consists of a finite number of backscatter layers, each representing backscatter values corresponding to single-incidence angle ensonifications. The construction of the HAC layers can be achieved either by interpolating dense soundings from highly overlapping multibeam echo-sounder (MBES) surveys (interpolated HAC, iHAC) or by producing several backscatter mosaics, each being normalized at a different incidence angle (synthetic HAC, sHAC). The latter approach can be applied to multibeam data with standard overlap, thus minimizing the cost for data acquisition. The sHAC is as efficient as the iHAC produced by actual soundings, providing distinct angular responses for each seafloor type. The HAC data structure increases acoustic class separability between different acoustic features. Moreover, the results of angular response analysis are applied on a fine spatial scale (cell dimensions) offering more detailed acoustic maps of the seafloor. Considering that angular information is expressed through high-dimensional backscatter layers, we further applied three machine learning algorithms (random forest, support vector machine, and artificial neural network) and one pattern recognition method (sum of absolute differences) for supervised classification of the HAC, using a limited amount of ground truth data (one sample per seafloor type). Results from supervised classification were compared with results from an unsupervised method for inter-comparison of the supervised algorithms. It was found that all algorithms (regarding both the iHAC and the sHAC) produced very similar results with good agreement (>0.5 kappa) with the unsupervised classification. Only the artificial neural network required the total amount of ground truth data for producing comparable results with the remaining algorithms. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle
Probabilistic Substrate Classification with Multispectral Acoustic Backscatter: A Comparison of Discriminative and Generative Models
Geosciences 2018, 8(11), 395; https://doi.org/10.3390/geosciences8110395 - 30 Oct 2018
Cited by 6
Abstract
We propose a probabilistic graphical model for discriminative substrate characterization, to support geological and biological habitat mapping in aquatic environments. The model, called a fully-connected conditional random field (CRF), is demonstrated using multispectral and monospectral acoustic backscatter from heterogeneous seafloors in Patricia Bay, [...] Read more.
We propose a probabilistic graphical model for discriminative substrate characterization, to support geological and biological habitat mapping in aquatic environments. The model, called a fully-connected conditional random field (CRF), is demonstrated using multispectral and monospectral acoustic backscatter from heterogeneous seafloors in Patricia Bay, British Columbia, and Bedford Basin, Nova Scotia. Unlike previously proposed discriminative algorithms, the CRF model considers both the relative backscatter magnitudes of different substrates and their relative proximities. The model therefore combines the statistical flexibility of a machine learning algorithm with an inherently spatial treatment of the substrate. The CRF model predicts substrates such that nearby locations with similar backscattering characteristics are likely to be in the same substrate class. The degree of allowable proximity and backscatter similarity are controlled by parameters that are learned from the data. CRF model results were evaluated against a popular generative model known as a Gaussian Mixture model (GMM) that doesn’t include spatial dependencies, only covariance between substrate backscattering response over different frequencies. Both models are used in conjunction with sparse bed observations/samples in a supervised classification. A detailed accuracy assessment, including a leave-one-out cross-validation analysis, was performed using both models. Using multispectral backscatter, the GMM model trained on 50% of the bed observations resulted in a 75% and 89% average accuracies in Patricia Bay and Bedford Basin, respectively. The same metrics for the CRF model were 78% and 95%. Further, the CRF model resulted in a 91% mean cross-validation accuracy across four substrate classes at Patricia Bay, and a 99.5% mean accuracy across three substrate classes at Bedford Basin, which suggest that the CRF model generalizes extremely well to new data. This analysis also showed that the CRF model was much less sensitive to the specific number and locations of bed observations than the generative model, owing to its ability to incorporate spatial autocorrelation in substrates. The CRF therefore may prove to be a powerful ‘spatially aware’ alternative to other discriminative classifiers. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle
Where, What, When, and Why Is Bottom Mapping Needed? An On-Line Application to Set Priorities Using Expert Opinion
Geosciences 2018, 8(10), 379; https://doi.org/10.3390/geosciences8100379 - 16 Oct 2018
Cited by 2
Abstract
Globally, there is a lack of resources to survey the vast seafloor areas in need of basic mapping data. Consequently, smaller areas must be prioritized to address the most urgent needs. We developed a systematic, quantitative approach and on-line application to gather mapping [...] Read more.
Globally, there is a lack of resources to survey the vast seafloor areas in need of basic mapping data. Consequently, smaller areas must be prioritized to address the most urgent needs. We developed a systematic, quantitative approach and on-line application to gather mapping suggestions from diverse stakeholders. Participants are each provided with 100 virtual coins to place throughout a region of interest to convey their mapping priorities. Inputs are standardized into a spatial framework using a grid and pull-down menus. These enabled participants to convey the types of mapping products that they need, the rationale used to justify their needs, and the locations that they prioritize for mapping. This system was implemented in a proposed National Marine Sanctuary encompassing 2784 km2 of Lake Michigan, Wisconsin. We demonstrate key analyses of the outputs, including coin counts, cell ranking, and multivariate cluster analysis for isolating high priority topics and locations. These techniques partition the priorities among the disciplines of the respondents, their selected justifications, and types of desired map products. The results enable respondents to identify potential collaborations to achieve common goals and more effectively invest limited mapping funds. The approach can be scaled to accommodate larger geographic areas and numbers of participants and is not limited to seafloor mapping. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle
Detection of Stones in Marine Habitats Combining Simultaneous Hydroacoustic Surveys
Geosciences 2018, 8(8), 279; https://doi.org/10.3390/geosciences8080279 - 28 Jul 2018
Cited by 7
Abstract
Exposed stones in sandy sublittoral environments are hotspots for marine biodiversity, especially for benthic communities. The detection of single stones is principally possible using sidescan-sonar (SSS) backscatter data. The data resolution has to be high to visualize the acoustic shadows of the stones. [...] Read more.
Exposed stones in sandy sublittoral environments are hotspots for marine biodiversity, especially for benthic communities. The detection of single stones is principally possible using sidescan-sonar (SSS) backscatter data. The data resolution has to be high to visualize the acoustic shadows of the stones. Otherwise, stony substrates will not be differentiable from other high backscatter substrates (e.g., gravel). Acquiring adequate sonar data and identifying stones in backscatter images is time consuming because it usually requires visual-manual procedures. To develop a more efficient identification and demarcation procedure of stone fields, sidescan sonar and parametric echo sound data were recorded within the marine protected area of “Sylt Outer Reef” (German Bight, North Sea). The investigated area (~5.900 km2) is characterized by dispersed heterogeneous moraine and marine deposits. Data from parametric sediment echo sounder indicate hyperbolas at the sediment surface in stony areas, which can easily be exported. By combining simultaneous recorded low backscatter data and parametric single beam data, stony grounds were demarcated faster, less complex and reproducible from gravelly substrates indicating similar high backscatter in the SSS data. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Open AccessArticle
Improved Interpretation of Marine Sedimentary Environments Using Multi-Frequency Multibeam Backscatter Data
Geosciences 2018, 8(6), 214; https://doi.org/10.3390/geosciences8060214 - 12 Jun 2018
Cited by 18
Abstract
Backscatter mosaics based on a multi-frequency multibeam echosounder survey in the continental shelf setting of the North Sea were compared. The uncalibrated backscatter data were recorded with frequencies of 200, 400 and 600 kHz. The results showed that the seafloor appears mostly featureless [...] Read more.
Backscatter mosaics based on a multi-frequency multibeam echosounder survey in the continental shelf setting of the North Sea were compared. The uncalibrated backscatter data were recorded with frequencies of 200, 400 and 600 kHz. The results showed that the seafloor appears mostly featureless in acoustic backscatter mosaics derived from 600 kHz data. The same area surveyed with 200 kHz reveals numerous backscatter anomalies with diameters of 10–70 m deviating between −2 dB and +4 dB from the background sediment. Backscatter anomalies were further subdivided based on their frequency-specific texture and were attributed to bioturbation within the sediment and the presence of polychaetes on the seafloor. While low frequencies show the highest overall contrast between different seafloor types, a consideration of all frequencies permits an improved interpretation of subtle seafloor features. Full article
(This article belongs to the Special Issue Geological Seafloor Mapping)
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