Multibeam Backscatter Research Articles

Multispectral multibeam backscatter response of heterogeneous rhodolith beds

Acoustic backscatter has been used as a tool to map the seafloor in greater detail and plays an increasingly important role in seafloor mapping to meet multiple ocean management needs. An outstanding challenge to the use of backscatter for seafloor mapping is the distinction between acoustically similar substrates, such as mixed sediments from rhodoliths. Rhodolith beds are a biogenic substrate that provides important ecological services, and are typically classified as a single categorical substrate type—though nodules coverage may be spatially variable. Recently, multispectral acoustic backscatter has demonstrated great potential to improve thematic seafloor mapping compared to single-frequency systems. This work employs multispectral multibeam backscatter and underwater imagery to characterize and map rhodolith beds in the Costa das Algas Marine Protected Area (Brazil). A support vector machine classifier was used to classify multifrequency backscatter mosaics according to rhodolith classes identified from underwater imagery. Results suggest that multispectral backscatter is effective both in providing information for mapping different proportions of rhodolith coverage and in predicting the presence or absence of these nodules. The backscatter of the lowest frequency was the most useful for distinguishing variable proportions of rhodolith coverage, and the two higher frequencies were better predictors of presence and absence.

Hunting paleoceanographic archives of ice sheet-ocean interaction in the northwestern Ross Sea, Antarctica

The analysis of sedimentary deposits influenced by bottom currents in glaciated continental margins provides crucial insights into paleo-depositional and oceanographic conditions. These reconstructions enable the assessment of interactions between advance and retreat of grounded ice sheets and past ocean circulation patterns. However, questions regarding these interactions and their specific mechanisms remain largely unanswered due to a lack of data in this remote area. In this study, we conducted a comprehensive analysis by integrating marine geophysical data, surficial sediment cores, oceanographic measurements, and ocean circulation models. Our aim was to understand spatial and temporal variations in sedimentary and oceanographic conditions during the past glacial and interglacial periods in combination with the long-term stratigraphic evolution. By integrating and cross-referencing diverse datasets, we were able to infer how bottom-current-controlled deposits (i.e., contourites) developed along the western bathymetric high of the Central Basin in the northwestern Ross Sea margin, Antarctica. Contouritic deposits lying over and along the flanks of bathymetric highs were identified through their mound-shaped external geometry and acoustically stratified facies, characterized by reflectors pinching toward the moat. Acoustic facies and multi-beam backscatter results, in conjunction with sedimentary core data, revealed contrasting patterns. Bathymetric highs exhibited thin (<10 m thick) coarser-grained sedimentary layers with higher backscatter, while the lower slope and rise displayed thick (>10 m thick), finer-grained stratified sediments with lower backscatter. These findings indicate that seabed winnowing occurred by strong bottom current during past glacial periods as supported by sedimentological analysis. The pathways of the westward-deflected dense shelf water outflow and the westward-flowing along-slope current, as simulated by oceanographic models, explain the distinctive development of contourites influenced by bottom-current processes. Moreover, the large accumulations of sediment in the contourites, resulting from bathymetric barriers in the north of the Central Basin, may contribute to submarine slope failures.

Multi resolution appraisal of Cork Harbour estuary: An object based image analysis approach

Lithic sedimentary benthic habitats are often organised by the overlying hydrodynamic regime, generating seabed sedimentary bedforms (SSBs) whose morphology and sediment composition are controlled by the current flow properties including speed, direction, and duration. Furthermore, SSBs can manifest at a variety of spatial scales reflecting different aspects of the hydrodynamic regime that operate at distinct intensities and temporal scales. Acoustic seabed mapping of SSBs permits the quantification of the complex seafloor hydrodynamics without in situ measurements. As such, seabed mapping efforts must determine the minimum spatial resolution required to effectively capture the complete seabed hydrodynamic picture. Here, we provide a semi-automated, object-based image analysis (OBIA) of SSBs occurring over 0.5, 1.0, 1.5, 2.0, and 2.5 m spatial resolutions of multibeam echosounder (MBES) data acquired in Cork Harbour, Ireland, to ascertain the optimum resolution at which to derive current velocity attributes. Estimations of seabed sediment grain size were completed through the angular range analysis (ARA) of co-located 400 kHz multibeam backscatter data. Results show that current flow estimations derived at 0.5, 1.0 and 1.5 m resolutions of MBES bathymetry agree with previous in situ current speed measurements. Moreover, sediment grain size estimations produced during the ARA of the 400 kHz backscatter data displayed a clear pattern of sediment resuspension at each section of the harbour, and this was effectively depicted at 0.5, 1.0, and 1.5 m resolution of MBES bathymetry data. However, current flow estimates derived from 2.0 and 2.5 m resolution failed to portray the short-term current variation induced by the ebb phase of the tide. Therefore, it is recommended that MBES data with a pixel size of >50 % of the wavelength of the SSB observed is insufficient for inclusion in an OBIA workflow.

Indications of marine benthos occurrence from multi-spectral multi-beam backscatter data: a case study in the North Sea

To facilitate the conservation of seafloor habitats and planning of offshore activities, there is a growing need for mapping marine benthos in an effective and efficient way. Acoustic data acquired by multi-beam echosounders (MBES) have been extensively used for large-scale and high-resolution seafloor characterization. A deeper understanding of the relationship between backscatter data and sediment compositions can help to identify the benthos occurrence from the MBES data. With two multi-spectral MBES datasets collected near the western Wadden Sea islands in the North Sea, we investigated the potential of mapping marine benthos through backscatter classification. Two unsupervised classification methods, i.e., Bayesian classification, which mainly exploits the backscatter strength from incident angles larger than 20°, and hierarchical clustering of the backscatter strength at different angular ranges, were employed and the results were compared. The classification results from both methods showed a good correspondence with sediment properties such as the median grain size. Moreover, based on a principal component analysis of bottom sample properties, the hierarchical clustering results indicated a better distinction between contributions from the gravel content and benthos occurrence, e.g., sand mason worm density, than Bayesian classification, through involving the backscatter angular variations. Classification for multiple frequencies, on the other hand, showed little difference regarding the relationship with bottom sample properties. Although the backscatter difference between frequencies was also found to positively correlate with certain sample properties, using multi-spectral features for acoustic classification in this study did not reveal additional information compared to single-frequency classification results.

Use of multibeam bathymetry and backscatter to improve seabed geochemical surveys — Part 1: Historical review, technical description, and best practices

Oil and gas seeps have been a key tool in hydrocarbon exploration since ancient times. Basin-wide reconnaissance exploration, focused on basic geology and identification of hydrocarbon seepage, has been typical of onshore basin analysis since the beginning of the petroleum industry. Since the discovery of marine chemosynthetic “cold seep” communities in the mid-1980s, and their association with offshore oil and gas seepage, the energy industry has been mapping seeps to target them for exploration and avoid them in development. For exploration, the successful sampling of oil or gas at the seafloor reduces exploration risk by demonstrating generative source rock, maturation, migration, and charge — all key data about the subsurface petroleum systems. In the marine environment, seep communities and associated diagenetic precipitates can modify the bathymetry and/or the backscatter and can be imaged by multibeam echo sounder (MBES). MBES can provide detailed bathymetry of the seafloor; multibeam backscatter can provide not only potential targets for seep sampling but also information on the seafloor characteristics at or just below the seafloor, and multibeam water column data can image gas plumes rising from the seafloor. Multibeam was introduced outside of military applications in the 1970s with the application of multibeam to seep science in the oil and gas industry, and the use of ultrashort baseline-positioned cores in a real-time geographical information system (GIS) to target seeps, began in the late 1990s, with the first proprietary survey in 2000. We review the history of multibeam, the history of seep science, and “lessons learned” over decades to best practices in seep hunting, from vessel specification to dry dock to presurvey to survey operations to target selection.

A Sample Enhancement Method Based on Simple Linear Iterative Clustering Superpixel Segmentation Applied to Multibeam Seabed Classification

Full-coverage and high-efficiency seabed sediment detection and identification are critical elements of digital marine construction that support the three-dimensional and thematic development of maritime spatial geographic information systems. With the development of multibeam echo sounder (MBES), the use of MBES backscatter intensity and bathymetry data to extract backscatter angular response (AR) features has increased. Using backscatter intensity features and seabed terrain features for classification is an effective way to achieve large-scale seabed sediment classification. However, it was still limited by small sample size problems and the poor stability of the classification model due to the complexity of performing seabed sediment sampling. In response to the above issues, this paper proposes a sample enhancement method based on simple linear iterative clustering (SLIC) superpixel segmentation to address the problems. First, a superpixel-based sample homogeneity expansion method is combined with multibeam backscatter intensity images to achieve adaptive sample range selection. Then, a Random Forest (RF)-based model is constructed using MBES backscatter intensity and seabed terrain features. To assess the model’s validity, the experiment uses data from an extensive MBES survey and field sampling information from the Celtic Sea, UK. It achieves accurate predictions for the area’s eight sediment types. The experimental results show that the proposed method expands 60 groups of original sample points to 9293 groups of valid sample points, and achieves an overall classification accuracy and kappa coefficient of 84.05% and 0.81 for the seabed sediment, respectively. In addition, the classification accuracy is significantly better compared to the traditional methods.

Mixed Seabed Sediment Classification Based on Transferred Convolutional Neural Network: A Case Study in the Ancient River Valley

The coastal zone is connected to the hinterland of the basin and the wide sea, which is not only affected by complex natural factors, but also by human activities. The study of sediment classification in this area will help to further explain topographic evolution and dynamic mechanism. Aiming at the complex mixed sediment environment in the ancient valley area of shallow seas, an advanced deep convolutional neural network classification model based on transfer learning—selective kernel hybrid dilated ResNet-50 (SKHD-ResNet-50) is thus proposed and applied to the coastal area in the Firth of Forth, Scotland, UK. The model overcomes problems of low accuracy of sediment classification with small samples, while effectively improving the efficiency of mixed sediment classification. Five transferred convolutional neural network models including CNN, AlexNet, VGG-19, GoogleNet, and SKHD-ResNet-50, were used to conduct seabed sediment classification experiments on multibeam backscatter mosaics in the study area. An ablation study about the effect of selective kernel convolution (SKC) and hybrid dilated convolution (HDC) was also conducted to further prove the usability of the proposed method. Finally, the optimized convolutional neural network was used to build a multi-layer deep learning model for sediment classification, combining the transfer learning method with the neural network to obtain the optimal network weights through deep global optimization, thus accurately mining statistical characteristics and distribution rules of backscatter intensity data. The experimental results show that the advanced model obtains better classification accuracy than that of state-of-art methods, with overall accuracy and Kappa coefficient of 96.87% and 0.9482, respectively. Findings highlight the proposed sediment classification model, which can effectively distinguish mixed sediments, and greatly improve the accuracy and efficiency of sediment classification.

A multi-proxy approach for delineation of ferromanganese mineralization from the West Sewell Ridge, Andaman Sea

Based on the Multibeam backscatter data, Sub-bottom profiling and Remotely Operated Vehicle (ROV) dives, an attempt has been made to understand the spatial distribution of ferromanganese crusts and nodules on the West Sewell Ridge (WSR), Andaman Sea. Slope analysis of the multibeam data and echo type characters from sub-bottom profiles were used to identify favorable areas for crust and nodule occurrence and these areas were selected for detailed seafloor feature analysis using ROV videography. Further, histogram analysis of backscatter intensity revealed five boundary values and integrated these threshold values with ground truth observation from ROV videos identified crust dominated, crust and nodule dominated, the hard substrate with thin sediment cover, the hard substrate with thick sediment cover, and complete sediment covered seabed. The seafloor variation map prepared from the different geological classes identified shows that crust-dominated seabed is distributed in the eastern summit region while crust and nodule-dominated seabed in the western summit region of WSR. Further, the flanks of the WSR are covered with turbidity sediments and sediments derived from the slope failure and possess the least possibility of the occurrence of crusts and nodules on the flanks.

Deep-Sea Seabed Sediment Classification Using Finely Processed Multibeam Backscatter Intensity Data in the Southwest Indian Ridge

In 2007, China discovered a hydrothermal anomaly in the Longqi hydrothermal area of the Southwest Indian Ridge. It was the first seabed hydrothermal area discovered in the ultraslow spreading ocean ridge in the world. Understanding the types of seabed sediments in this area is critical for studying the typical topography and geological characteristics of deep-sea seabed hydrothermal areas. The traditional classification of deep-seabed sediments adopts box sampling or gravity column sampling and identifies the types of seabed sediments through laboratory analysis. However, this classification method has some shortcomings, such as the presence of discrete sampling data points and the failure of full-coverage detection. The geological sampling in deep-sea areas is particularly inefficient. Hence, in this study, the EM122 multibeam sonar data collected in the Longqi hydrothermal area, Southwest Indian Ridge, in April 2019 are used to analyze multibeam backscatter intensity. Considering various errors in the complex deep-sea environment, obtaining backscatter intensity data can truly reflect seabed sediment types. Through unsupervised and supervised classification, the seabed sediment classification in the Longqi hydrothermal area was studied. The results showed that the accuracy of supervised classification is higher than that of unsupervised classification. Thus, unsupervised classification is primarily used for roughly classifying sediment types without on-site geological sampling. Combining the genetic algorithm (GA) and the support vector machine (SVM) neural network, deep-sea sediment types, such as deep-sea clay and calcareous ooze, can be identified rapidly and efficiently. Based on comparative analysis results, the classification accuracy of the GA-SVM neural network is higher than that of the SVM neural network, and it can be effectively applied to the high-precision classification and recognition of deep-sea sediments. In this paper, we demonstrate the fine-scale morphology and surface sediment structure characteristics of the deep-sea seafloor by finely processing high-precision deep-sea multibeam backscatter intensity data. This research can provide accurate seabed topography and sediment data for the exploration of deep-sea hydrothermal resources and the assessment of benthic habitats in deep-sea hydrothermal areas.

Multiannual Seafloor Dynamics around a Subtidal Rocky Reef Habitat in the North Sea

Rocky reefs provide complex structures in the otherwise largely sand-dominated coastal North Sea. Therefore, these reefs are highly important natural habitats for the functioning of coastal ecosystems, as they provide shelter, refuge and nursery grounds for various mobile and sessile species. In the North Sea, the spatial distribution of these habitats has been intensively investigated over recent years. However, these studies generally provide static accounts of the current state of these reef systems, but limited data exist on the temporal variations in sediment dynamics at and around natural rocky reefs. In this study, we provide observations from a multiannual time series of hydroacoustic seafloor surveys conducted at an isolated rocky reef in the North Sea. We use multibeam bathymetry and side-scan sonar backscatter data in combination with video observations, sediment sampling, and sub-bottom profiler data to assess the long-term variations of the rocky reef system. The reef is located in water depths between 11 and 17 m with an areal extent of ~0.5 km2 and is surrounded by mobile sands. The topography of the rocky reef appears to create a distinct hydrodynamic system that permits mobile sands to settle or move into bathymetrical deeper parts of the reef. Our results suggest a very dynamic system surrounding the reef with large scale scouring, sediment reworking and transport, while the shallower central part of the reef remains stable over time. We demonstrate the importance of hydrodynamics and current scouring around reefs for the local variability in seafloor properties over time. These small-scale dynamics are likewise reflected in the spatial distribution of sessile species, which are less abundant in proximity to mobile sands. The hydroacoustic mapping and monitoring of seafloor dynamics at higher spatial and temporal resolutions presents an important future direction in the study of valuable coastal habitats.

Deep-Sea Sediment Mixed Pixel Decomposition Based on Multibeam Backscatter Intensity Segmentation

The ability to accurately map the seabed sediments plays an important role in seabed habitat development and stakeholder decision-making. In conventional seabed sediment classification methods, maps of seabed sediment are provided in categorical form (sediment classes). Therefore, the prediction of the sediment compositions in multibeam observational units has become a difficult issue in using conventional methods. To tackle this challenge, a new strategy is developed to realize the subpixel decomposition of seabed sediments. A key attribute of the proposed sediment decomposition model is that it utilizes spatial–spectral information provided by multibeam backscatter angular responses (ARs). First, an AR feature extraction method utilizing a bidirectional sliding window is proposed and a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means clustering algorithm is used for segmentation. Second, a deep-sea sediment decomposition model based on the fuzzy method is constructed by selecting experimental samples that are distributed within a single clustering region. This model inverts the abundance of each sediment composition in the form of membership degrees. Finally, deep-sea multibeam survey data collected from the central Philippine Sea are used for verification. The overall mean square error and coefficient of determination reach 0.043 and 0.856, respectively. The experimental results show that the new method can accurately decompose deep-sea sediment compositions, thus providing a new technique for deep-sea acoustic sediment remote sensing and quantitative analysis.

Remote sensing and the UN Ocean Decade: high expectations, big opportunities

Remote sensing and the UN Ocean Decade: high expectations, big opportunities

Sounding Out the Carbon: The Potential of Acoustic Backscatter Data to Yield Improved Spatial Predictions of Organic Carbon in Marine Sediments

Marine sediments hold vast stores of organic carbon (OC). Techniques to spatially map sedimentary OC must develop to form the basis of seabed management tools that consider carbon-rich sediments. While the natural burial of carbon (C) provides a climate regulation service, the disturbance of buried C could present a significant positive feedback mechanism to atmospheric greenhouse gas concentrations. We present a regional Scottish case study that explores the suitability of integrating archived seafloor acoustic data (i.e., multibeam echosounder bathymetry and backscatter) with physical samples toward improved spatial mapping of surface OC in a dynamic coastal environment. Acoustic backscatter is a proxy for seabed sediments and can be collected over extensive areas at high resolutions. Sediment type is also an important predictor of OC. We test the potential of backscatter as a proxy for OC which may prove useful in the absence of exhaustive sediment data. Overall, although statistically significant, correlations between the variables OC, sediment type, and backscatter are relatively weak, likely reflecting a combination of limited and asynchronous data, sediment mobility over time, and complex environmental processing of OC in shelf sediments. We estimate linear mixed models to predict OC using backscatter and Folk sediment type as covariates. Our results show that incorporating backscatter in the model improves the precision of OC predictions by 14%. Backscatter discriminates between coarse and fine sediments, and therefore low and high OC regimes; however, was not able to discriminate amongst finer sediments. Although sediment type is a stronger predictor of OC, these data are available at a much lower spatial resolution and do not account for fine-scale variability. The resulting maps display varying spatial distributions of OC reflecting the different scales of the predictor variables, demonstrating a need for further methodological development. Backscatter shows considerable promise as a high-resolution predictor variable to improve the precision of surface OC maps, or to reduce the number of OC measurements required to achieve a specified precision. Applications of such maps have potential in improved C-stock estimates and the design of conservation and management strategies that consider marine sediments as valuable C stores.

Predicted Mapping of Seabed Sediments Based on MBES Backscatter and Bathymetric Data: A Case Study in Joseph Bonaparte Gulf, Australia, Using Random Forest Decision Tree

Predictive mapping of seabed sediments based on multibeam bathymetric (BM), and backscatter (BS) data is effective for mapping the spatial distribution of the substrate. A robust modeling technique, the random forest decision tree (RFDT), was used to predict the seabed sediments in an area of the Joseph Bonaparte Gulf, Northern Australia, using the multibeam data and seabed sediment samples collected simultaneously. The results showed that: (1) Using multibeam bathymetry data in addition to multibeam backscatter data improves the prediction performance of the RFDT. In comparison to only multibeam backscatter data, the prediction performance achieved a ~10% improvement in sediment properties; it achieved a ~44.45% improvement of overall accuracy in sediment types, and a ~0.55 improvement in Kappa. (2) The underlying relationships between sediment properties and multibeam data show that there is an opposite non-linear correlation between sediment property-BS and sediment property-BM. For example, there is an obvious negative relationship between %mud-BS at incidence angles of 13° and 21°, but the relationship between %mud-BM is positive. As such, the RFDT is a useful and well-performing method in predicting the relationship between sediment properties and multibeam data and in predicting the distribution of sediment properties and types. However, the sediment prediction method in deep-water areas with high gravel content needs to be further evaluated.

Examining the Links between Multi-Frequency Multibeam Backscatter Data and Sediment Grain Size

Acoustic methods are routinely used to provide broad scale information on the geographical distribution of benthic marine habitats and sedimentary environments. Although single-frequency multibeam echosounder surveys have dominated seabed characterisation for decades, multifrequency approaches are now gaining favour in order to capture different frequency responses from the same seabed type. The aim of this study is to develop a robust modelling framework for testing the potential application and value of multifrequency (30, 95, and 300 kHz) multibeam backscatter responses to characterize sediments’ grain size in an area with strong geomorphological gradients and benthic ecological variability. We fit a generalized linear model on a multibeam backscatter and its derivatives to examine the explanatory power of single-frequency and multifrequency models with respect to the mean sediment grain size obtained from the grab samples. A strong and statistically significant (p &lt; 0.05) correlation between the mean backscatter and the absolute values of the mean sediment grain size for the data was noted. The root mean squared error (RMSE) values identified the 30 kHz model as the best performing model responsible for explaining the most variation (84.3%) of the mean grain size at a statistically significant output (p &lt; 0.05) with an adjusted r2 = 0.82. Overall, the single low-frequency sources showed a marginal gain on the multifrequency model, with the 30 kHz model driving the significance of this multifrequency model, and the inclusion of the higher frequencies diminished the level of agreement. We recommend further detailed and sufficient ground-truth data to better predict sediment properties and to discriminate benthic habitats to enhance the reliability of multifrequency backscatter data for the monitoring and management of marine protected areas.

Using multibeam backscatter strength to analyze the distribution of manganese nodules: A case study of seamounts in the Western Pacific Ocean

Multibeam backscatter strength can be used to characterize seafloor acoustic characteristics, and classify seabed substrate types. Herein, we use acoustic reflectance data to examine the distribution of manganese nodules in deep-sea substrates in a seamount region of the Western Pacific Ocean. Based on the multibeam backscatter strength, the backscatter mosaic image is firstly corrected by a series of error corrections, and then a size-insensitive integrity-based Fuzzy C-Means (siib-FCM) technique is used to analyze the distribution of manganese nodules. The results show that the method can reveal the distribution of deep-sea manganese nodules and is suitable for the classification of seabed substrates with few sampling points. The distribution of manganese nodules has two characteristics: (1) In basin and plain areas with relatively flat terrain, manganese nodules are associated with sediment, occur in patches and their distribution is relatively sparse. (2) In seamount areas with complex terrain, manganese crusts are distributed mainly at mid-slope elevations displaying a circular distribution around the seamount. The processing technique and results of the present study form a basis for future investigations into the occurrence and distribution of manganese nodule resources in the deep oceans.

Findings from a Combined Subsea LiDAR and Multibeam Survey at Kingston Reef, Western Australia

Light Detection and Ranging (LiDAR), a comparatively new technology in the field of underwater surveying, has principally been used for taking precise measurement of undersea structures in the oil and gas industry. Typically, the LiDAR is deployed on a remotely operated vehicle (ROV), which will “land” on the seafloor in order to generate a 3D point cloud of its environment from a stationary position. To explore the potential of subsea LiDAR on a moving platform in an environmental context, we deployed an underwater LiDAR system simultaneously with a multibeam echosounder (MBES), surveying Kingston Reef off the coast of Rottnest Island, Western Australia. This paper compares and summarises the relative accuracy and characteristics of underwater LiDAR and multibeam sonar and investigates synergies between sonar and LiDAR technology for the purpose of benthic habitat mapping and underwater simultaneous localisation and mapping (SLAM) for Autonomous Underwater Vehicles (AUVs). We found that LiDAR reflectivity and multibeam backscatter are complementary technologies for habitat mapping, which can combine to discriminate between habitats that could not be mapped with either one alone. For robot navigation, SLAM can be effectively applied with either technology, however, when a Global Navigation Satellite System (GNSS) is available, SLAM does not significantly improve the self-consistency of multibeam data, but it does for LiDAR.

Seabed sediment classification using multibeam backscatter data based on the selecting optimal random forest model

Seabed sediment classification using acoustic remote sensing technique is an attractive approach due to its high coverage capabilities and limited costs compared to taking samples of the seafloor. This paper focuses on backscatter intensity correction, sonar image quality improvement, and classifier construction, which aims to improve the accuracy of seabed sediment classification. The details are as follows. 1) A series of multibeam echosounder backscatter intensity correction model is constructed, including time-varying gains (TVG), transmission loss, actual area of insonification, source level, transmitting and receiving beam patterns, specular area correction, etc., to obtain accurate intensity values that accurately reflect seabed sediment types. 2) The pulse coupled neural network (PCNN) image enhancement model is established to improve the quality of sonar images, and 40 dimensional features are included to enrich the intensity description. 3) Selecting optimal random forest (SORF) seabed sediment automatic classification models that can select the input feature vectors and optimize the model parameters automatically are established. 4) Taking multibeam backscatter intensity data collected in Jiaozhou Bay as an example, the effectiveness and advantages of SORF are verified by comparing with support vector machine (SVM) and random forest (RF) classifiers.

Complex Eyed Pockmarks and Submarine Groundwater Discharge Revealed by Acoustic Data and Sediment Cores in Eckernförde Bay, SW Baltic Sea

AbstractSubmarine groundwater discharge into coastal areas is a common global phenomenon and is rapidly gaining scientific interest due to its influence on marine ecology, the coastal sedimentary environment, and its potential as a future freshwater resource. We conducted an integrated study of hydroacoustic surveys combined with geochemical pore water and water column investigations at a well‐known groundwater seep site in Eckernförde Bay (Germany). We aim to better constrain the effects of shallow gas and submarine groundwater discharge on high‐frequency multibeam backscatter data and to present acoustic indications for submarine groundwater discharge. Our high‐quality hydroacoustic data reveal hitherto unknown internal structures within the pockmarks in Eckernförde Bay. Using precisely positioned sediment core samples, our hydroacoustic‐geochemical approach can differentiate intrapockmark regimes that were formerly assigned to pockmarks of a different nature. We demonstrate that high‐frequency multibeam data, in particular the backscatter signals, can be used to detect shallow free gas in areas of enhanced groundwater advection in muddy sediments. Intriguingly, our data reveal relatively small (typically &lt;15 m across) pockmarks within the much larger, previously mapped pockmarks. The small pockmarks, which we refer to as “intrapockmarks,” have formed due to the localized ascent of gas and groundwater; they manifest themselves as a new type of “eyed” pockmarks, revealed by their acoustic backscatter pattern. Our data suggest that, in organic‐rich muddy sediments, morphological lows combined with a strong multibeam backscatter signal can be indicative of free shallow gas and subsequent advective groundwater flow.

Acoustic quantitative analysis of ferromanganese nodules and cobalt-rich crusts distribution areas using EM122 multibeam backscatter data from deep-sea basin to seamount in Western Pacific Ocean

EM122 multibeam backscatter data acquired from the Caiwei seamounts and the deep-sea basin to its west in the western Pacific Ocean, is used to identify the distributions of ferromanganese nodules with different abundance, cobalt-rich crusts, pelagic calcareous and clay sediments, etc. Box and drill sample and video are used for ground truth. The backscatter data are processed by CARIS HIPS&SIPS 11.1, including transmission losses, insonified area, AVG, despeckling and mosaicking. The qualitative and quantitative relationship between backscatter and nodules, crusts and different sediments are established successfully. Significantly, low-backscatter values are observed on areas of pelagic clay sediments (C1) in the deep-sea basin and calcareous pelagic sediments (C2) on the summit of the seamount, whereas obvious high-backscatter values are observed on areas of nodules with high abundance around 40 kg/m2 (C4) in the deep sea basin and cobalt-rich crusts (C5) on the seamount, the important difference in backscatter between soft pelagic sediments (C1 and C2) and hard minerals (C4 and C5) is nearly 20dB. Quantitatively, the GSAB improved model is used to identify several parameters for different seafloor geological classes, e.g., BS45°, specular level, specular angular extent, Lambert reference and Lambert decrement. The parameters for nodules with different abundance, cobalt-rich crusts and different pelagic sediments provide insight into reflection coefficient, specular regime, seafloor roughness and impedance, etc. The profiles of angular response of nodules with high abundance (C4) in the basin and crusts (C5) on the seamount show high values in BS45°, Lambert reference but low specular level, and hence can be interpreted as representative areas with hard or relatively rough substrates. Contrarily, the profiles of the pelagic clay sediment (C1) and calcareous pelagic sediment (C2) both show low values in BS45°, Lambert references but high values in specular level, indicating similar high coherent reflection coefficient, low slope variance and smooth sediment interfaces for these two types of pelagic sediments.