Shallow-water Data Research Articles

Highly structured populations of deep-sea copepods associated with hydrothermal vents across the Southwest Pacific, despite contrasting life history traits

Hydrothermal vents are extreme environments, where abundant communities of copepods with contrasting life history traits co-exist along hydrothermal gradients. Here, we discuss how these traits may contribute to the observed differences in molecular diversity and population genetic structure. Samples were collected from vent locations across the globe including active ridges and back-arc basins and compared to existing deep-sea hydrothermal vent and shallow water data, covering a total of 22 vents and 3 non-vent sites. A total of 806 sequences of mtDNA from the Cox1 gene were used to reconstruct the phylogeny, haplotypic relationship and demography within vent endemic copepods (Dirivultidae, Stygiopontius spp.) and non-vent-endemic copepods (Ameiridae, Miraciidae and Laophontidae). A species complex within Stygiopontius lauensis was studied across five pacific back-arc basins at eight hydrothermal vent fields, with cryptic species being restricted to the basins they were sampled from. Copepod populations from the Lau, North Fiji and Woodlark basins are undergoing demographic expansion, possibly linked to an increase in hydrothermal activity in the last 10 kya. Highly structured populations of Amphiascus aff. varians 2 were also observed from the Lau to the Woodlark basins with populations also undergoing expansion. Less abundant harpacticoids exhibit little to no population structure and stable populations. This study suggests that similarities in genetic structure and demography may arise in vent-associated copepods despite having different life history traits. As structured meta-populations may be at risk of local extinction should major anthropogenic impacts, such as deep-sea mining, occur, we highlight the importance of incorporating a trait-based approach to investigate patterns of genetic connectivity and demography, particularly regarding area-based management tools and environmental management plans.

Usability of multibeam echosounder for wreck investigations using backscatter and water column data in shallow waters

With the increasing accuracy of survey equipment and the strict survey standards in modern times, the use of sophisticated equipment for hydrographic surveys has become standard practice. Multibeam echosounders (MBES) are an essential part of the inventory. This paper presents an analysis of two data sets collected using MBES and examines their applicability for wreck investigation. While the topic of MBES and wreck investigation is vast, this study is limited to two types of MBES in shallow waters. The analysis of the data sets indicates that MBES can effectively be used for wreck investigation in shallower waters when utilizing water column and backscatter data, adhering to the rigorous survey standards of today. This approach offers valuable insights for enhancing wreck investigation capabilities and meeting the demands of modern hydrographic surveys.

THE VAN DER MEER FORMULA FOR ROCK SLOPE STABILITY AT SHALLOW WATER

In Van der Meer (2021) the stability formula for rock slopes under wave attack has been rewritten to remove the mean period Tm and to include the spectral period Tm-1,0. This formula is now identical to the Modified Van der Meer formula in the Rock Manual (2007 – Eqs 5.139 and 5.140), except for its coefficients and the use of H2 percent in the Rock Manual and H1/3 in Van der Meer (2021). A method has been given in Van der Meer (2021) by coefficients cpl and csu in the rewritten formula, to include (new) data on rock slope stability where results differ from the original formula and data. The re-fit of the shallow water data by Van Gent (2004) leading to the Modified Van der Meer formula for shallow water comes now to cpl=0.92 and csu=0.96, showing that rock slopes are less stable in shallow water than in deep water (coefficients smaller than 1 show less stability). The main objective is then: when and where do shallow water stability results deviate from the known stability formulae and how can we describe the deviating results. The original dataset by Van Gent (2004) and additional tests by Eldrup (2019) as well as other data have been analysed in depth to come to an improved understanding of rock slope stability in shallow water.

A Sub-Bottom Type Adaption-Based Empirical Approach for Coastal Bathymetry Mapping Using Multispectral Satellite Imagery

Accurate bathymetric data in shallow water is of increasing importance for navigation safety, coastal management, and marine transportation. Satellite-derived bathymetry (SDB) is widely accepted as an effective alternative to conventional acoustic measurements in coastal areas, providing high spatial and temporal resolution combined with extensive repetitive coverage. Many previous empirical SDB approaches are unsuitable for precision bathymetry mapping in various scenarios, due to the assumption of homogeneous bottom over the whole region, as well as the neglect of various interfering factors (e.g., turbidity) causing radiation attenuation. Therefore, this study proposes a bottom-type adaption-based SDB approach (BA-SDB). Under the consideration of multiple factors including suspended particulates and phytoplankton, it uses a particle swarm optimization improved LightGBM algorithm (PSO-LightGBM) to derive depth of each pre-segmented bottom type. Based on multispectral images of high spatial resolution and in situ observations of airborne laser bathymetry and multi-beam echo sounder, the proposed approach is applied in shallow water around Yuanzhi Island, and achieves the highest accuracy with an RMSE value of 0.85 m compared to log-ratio, multi-band, and classical machine learning methods. The results of this study show that the introduction of water-environment parameters improves the performance of the machine learning model for bathymetric mapping.

An Inversion Method for Geoacoustic Parameters in Shallow Water Based on Bottom Reflection Signals

The inversion method based on the reflection loss-grazing angle curve is an effective tool to obtain local underwater acoustic parameters. Because geoacoustic parameters vary in sensitivity to grazing angle, it is difficult to get accurate results in geoacoustic parameter inversion based on small-grazing-angle data in shallow water. In addition, the normal-mode model commonly used in geoacoustic parameter inversion fails to meet the needs of accurate local sound field simulation as the influence of the secant integral is ignored. To solve these problems, an acoustic data acquisition scheme was rationally designed based on a sparker source, a fixed vertical array, and ship drifting with the swell, which could balance the trade-off among signal transmission efficiency and signal stability, and the actual local acoustic data at low-to-mid frequencies were acquired at wide grazing angles in the South Yellow Sea area. Furthermore, the bottom reflection coefficients (bottom reflection losses) corresponding to different grazing angles were calculated based on the wavenumber integration method. The local seafloor sediment parameters were then estimated using the genetic algorithm and the bottom reflection loss curve with wide grazing angles, obtaining more accurate local acoustic information. The seafloor acoustic velocity inverted is cp=1659 m/s and the sound attenuation is αp=0.656 dB/λ in the South Yellow Sea. Relevant experimental results indicate that the method described in this study is feasible for local inversion of geoacoustic parameters for seafloor sediments. Compared with conventional large-scale inversion methods, in areas where there are significant changes in the seabed sediment level, this method can obtain more accurate local acoustic features within small-scale areas.

Enhanced detectability using differential fields for marine controlled-source EM data in shallow waters

ABSTRACT In shallow waters, the interpretation of the frequency-domain marine controlled-source electromagnetic (CSEM) data is challenging due to the airwave. The airwave dominates and will lead to misinterpretation of the data set. A differential-field approach is presented to attenuate the airwave for the shallow-water marine CSEM data. The difference of the fields between two consecutive receivers or source points is calculated and weighted by the geometric spreading related factor. By using differential fields, the detectability given by the field ratio between the models with and without the target is enhanced, which indicates that the impact of airwaves is suppressed.

Smart Sensors System Based on Smartphones and Methodology for 3D Modelling in Shallow Water Scenarios

The aim of the paper was the implementation of low-cost smart sensors for the collection of bathymetric data in shallow water and the development of a 3D modelling methodology for the reconstruction of natural and artificial aquatic scenarios. To achieve the aim, a system called GNSS > Sonar > Phone System (G > S > P Sys) was implemented to synchronise sonar sensors (Deeper Smart Sonars CHIRP+ and Pro+ 2) with an external GNSS receiver (SimpleRTK2B) via smartphone. The bathymetric data collection performances of the G > S > P Sys and the Deeper Smart Sonars were studied through specific tests. Finally, a data-driven method based on a machine learning approach to mapping was developed for the 3D modelling of the bathymetric data produced by the G > S > P Sys. The developed 3D modelling method proved to be flexible, easily implementable and capable of producing models of natural surfaces and submerged artificial structures with centimetre accuracy and precision.

Supervised Learning Approach for Multi-Component Deghosting with a Focus on Shallow Water Data

In recent years, the use of machine learning (ML) has shown more and more successful applications, initially mainly in seismic interpretation (classification) but recently also in seismic processing challenges (regression) like denoising, interpolation and demultiple. In this paper, a supervised machine learning algorithm is proposed for receiver deghosting on multi-component data. A neural network is trained to map input pairs of hydrophone and geophone data into the deghosted up-going wavefield. This data-driven approach could offer a user-friendly alternative to existing deghosting methods, like the PZ-summation approach that is commonly used for multi-component streamer and ocean-bottom acquisitions. Although the PZ-summation approach offers an analytical solution to the deghosting problem, it requires some pre-processing steps that make it more time-consuming and user-intensive to run and QC. The advantage of the proposed ML approach is that it can be directly applied to multi-component data without any parameterization. However, the success of the supervised method strongly depends on whether a neural network can be trained easily with minimal user-interaction and can be applied to various types of (field) data without adapting the network for each individual case. To investigate if this is possible for the supervised multi-component receiver deghosting method, the selected neural network is trained on easy to model 1.5D data examples and applied to test data with increasing complexity. Results are very encouraging and evaluated to be at least on par in comparison to the existing PZ-summation deghosting method.

Evaluating deep-sea communities' susceptibility to mining plumes using shallow-water data

Increased suspended sediment concentrations (SSC) are a major stressor across aquatic habitats. Here, the literature was synthesized to show that animal responses to increases in relative SSC (test concentration/natural background concentration) were similar in type and negative across different shallow-water (marine, estuarine, freshwater) habitats. Further, animal sensitivities are similar across habitats based on relative SSC and occur starting at low relative SSC increases in all habitats despite differences in natural background SSC. Based upon these similarities in relative SSC sensitivities, deep-sea sensitivity values for acute exposure to increased SSC, where empirical data are almost non-existent, were estimated. Because of the low natural SSC in deep sea environments, very small increases in absolute SSC could result in acute effects. How the methods and results can be used to inform regulatory thresholds are discussed. Because of the large variability in shallow water datasets and differences between deep-sea and shallow-water habitats, deep-sea specific data are needed to verify the estimates and improve their precision. Following the precautionary principle and the results presented here, it is recommended that the threshold for acute plume impacts is set very close to natural background levels.

Three-dimensional higher-order raypath separation in a shallow-water waveguide.

Separating raypaths in a multipath shallow-water environment is a challenge due to the interferences between them and the colored noise that exists in an ocean environment, especially for two raypaths that arrive close to each other. Thus, in this paper, a three-dimensional (3D) higher-order raypath separation in an array to array configuration is proposed. Performance tests using simulation data in a multipath environment, real data obtained in an ultrasonic waveguide, and ocean shallow-water data, respectively, illustrate that the proposed algorithm achieves a higher resolution and a stronger robustness compared to the existing algorithms.

Integrated velocity and attenuation model building: Sakhalin case study

Shallow gas accumulations with varied spatial distribution and scales cause amplitude loss and phase distortion in seismic images in underlying reservoir intervals. We present an integrated velocity and attenuation (defined by the quality factor Q) model-building workflow to address the imaging challenges in a shallow-water data set acquired offshore Sakhalin Island, Russia. As part of the workflow, we use full-waveform inversion (FWI) to capture fine details of the velocity and Q models. We achieve a step change in image quality improvement and amplitude fidelity enhancement by leveraging the workflow. This image enhancement improves subsurface interpretation and influences well placement, subsurface model building, and depletion plan optimization. Key to this success is not only the advanced model-building tool kit, which includes FWI and tomography, but also the optimal integration of the tools specifically tailored for the geologic setting and imaging challenges offshore Sakhalin Island.

The application of satellite derived bathymetry for coastline mapping

There is a gap in bathymetry data in shallow water due to the difficulty in measuring the depth. Meanwhile, to obtain an accurate datum-based coastline, sufficient depth and topography data are required. In the situation in which bathymetry data from the conventional method (sounding measurement) is limited, the bathymetry data resulted from Satellite Derived Bathymetry (SDB) algorithm can be an alternative solution. The SDB is estimated by correlating water depth and the spectral band of images. This research is conducted to develop a method that can be applied for coastline mapping by assessing the capability of SDB. For the integration of all input data, including land elevation and bathymetry data, a gridding method called stacked continuous curvature splines with tension is adopted. Coastline generated from the proposed method was relatively smooth and it was close to the shape of the land feature compared to coastline developed from other data sources such as the national bathymetry data and echo sounding data. From the results, we conclude that the SDB is promising in filling the gap of depth information in shallow water areas. A feasible coastline can be produced since depth information from the SDB model can increase the density of depth information required for generating a coastline model.

A truncated, translated Weibull distribution for shallow water sea states

Reliable probabilistic information of the long-term ocean wave climate is important in many coastal and ocean engineering applications such as the design of marine and coastal structures. There is therefore a need to establish accurate models for the long-term distribution of relevant metocean variables. One of the most important wave parameters in marine design is the significant wave height, which is a measure of the severity of the sea state, and wave loads are typically described in terms of this and other variables. Hence, a lot of effort has been put on the proper probabilistic modelling of significant wave height. However, probability distributions proposed to model significant wave height have mostly focused on deep water conditions and less efforts have been put on shallow water conditions. This paper proposes a truncated, translated Weibull distribution for the long-time distribution of significant wave height in shallow waters and demonstrates that the model fit shallow water data quite well. In particular, the proposed model describes shallow-water wave data better than the commonly used 3-parameter Weibull distribution, which is often the preferred distribution for deep water locations.

Dense Bathymetry in Turbid Coastal Zones Using Airborne Hyperspectral Images

We used airborne hyperspectral images to generate a dense survey of bathymetric data in the Bahía Blanca estuary (Buenos Aires Province, Argentina). This estuarine area is characterized by intense sediment transport turning the water muddy, and thus optical bathymetric estimations are difficult. We used 24 spectral bands in a range of 500–900 nm acquired with a hyperspectral camera aboard an unmanned aerial vehicle, together with 100 bathymetry data points surveyed with a sonar sensor aboard an unmanned surface vehicle, covering an area of about 800 m2. Random-forest and support-vector-machine regressors were trained with this data set. The resulting model yielded a determination coefficient of 0.815 with unseen data, a root-mean-square error of 0.166 m, and an absolute average error less than 2%. These results allow dense and accurate reconstructions of the underwater profile in wide, muddy, shallow regions of the Bahía Blanca estuary, showing the feasibility of hyperspectral imagery combined with sonar data in turbid shallow waters.

Improved Filtering of ICESat-2 Lidar Data for Nearshore Bathymetry Estimation Using Sentinel-2 Imagery

The accurate estimation of nearshore bathymetry is necessary for multiple aspects of coastal research and practices. The traditional shipborne single-beam/multi-beam echo sounders and Airborne Lidar bathymetry (ALB) have a high cost, are inefficient, and have sparse coverage. The Satellite-derived bathymetry (SDB) method has been proven to be a promising tool in obtaining bathymetric data in shallow water. However, current empirical SDB methods for multispectral imagery data usually rely on in situ depths as control points, severely limiting their spatial application. This study proposed a satellite-derived bathymetry method without requiring a priori in situ data by merging active and passive remote sensing (SDB-AP). It realizes rapid bathymetric mapping with only satellite remotely sensed data, which greatly extends the spatial coverage and temporal scale. First, seafloor photons were detected from the ICESat-2 raw photons based on an improved adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, which could calculate the optimal detection parameters for seafloor photons by adaptive iteration. Then, the bathymetry of the detected seafloor photons was corrected because of the refraction that occurs at the air–water interface. Afterward, the outlier photons were removed by an outlier-removal algorithm to improve the retrieval accuracy. Subsequently, the high spatial resolution (0.7 m) ICESat-2 derived bathymetry data were gridded to match the Sentinel-2 data with a lower spatial resolution (10 m). All of the ICESate-2 gridded data were randomly separated into two parts: 80% were employed to train the empirical bathymetric model, and the remaining 20% were used to quantify the inversion accuracy. Finally, after merging the ICESat-2 data and Sentinel-2 multispectral images, the bathymetric maps over St. Thomas of the United States Virgin Islands, Acklins Island in the Bahamas, and Huaguang Reef in the South China Sea were produced. The ICESat-2-derived results were compared against in situ data over the St. Thomas area. The results showed that the estimated bathymetry reached excellent inversion accuracy and the corresponding RMSE was 0.68 m. In addition, the RMSEs between the SDB-AP estimated depths and the ICESat-2 bathymetry results of St. Thomas, Acklins Island, and Huaguang Reef were 0.96 m, 0.91 m, and 0.94 m, respectively. Overall, the above results indicate that the SDB-AP method is effective and feasible for different shallow water regions. It has great potential for large-scale and long-term nearshore bathymetry in the future.

Regression kriging analysis for predicting the shallow depth water from Sentinel-2 satellite multi-spectral images, study area: Coastline of Florida, USA

The shallow depth water mapping has become important to the study of morphology and resources management of the coastal area. Moreover, for city or urban planning, it can be used for determining the proper location for seaport or tourist destination areas such as diving spots, coral reef monitoring, etc. However, the acquisition of shallow depth water data in a large area is somehow costly. In this paper, we represent an approach of mapping the high accuracy bathymetry with input from open satellite access data and some point measurement of bathymetry in the field (can be from LiDAR, Fathometer, Single Beam, or Multi-Beam). The study area is located in the Florida coastline, the USA that has satellite data from Sentinel-2 while the bathymetry is from a single beam survey. The method is combining satellite-derived bathymetry (SDB) with the regression kriging analysis, which shows a better depth water prediction compared to the SDB alone or the ordinary kriging method. The statistical result of the bathymetry shows the regression kriging has a better mean value, standard deviation and coefficient correlation compared to the true bathymetry value. Thus, this method can be utilized as an alternative method to map shallow depth water.

Improved calibration method for refraction errors in multibeam bathymetries with a wider range of water depths

Errors induced by inaccurate or insufficient sound speed profiles (SSPs) in the water column will cause refraction errors in multibeam bathymetries. Most existing methods focus on eliminating errors in shallow water. Thus, to address the problems in a wider range of water depths, a new method is developed. First, beams in each ping are divided into two parts with the nadir beam as a boundary, and beams in each part have one equivalent sound speed profile (ESSP). Then, the overlaps of adjacent swaths are used to estimate the gradients of ESSPs by minimizing the depth difference in the overlaps with the differential evolution (DE) algorithm. To verify the proposed method, two datasets, i.e., one with artificially introduced refraction errors from water depths within 24 m and the other with refraction errors from water depths within 300 m, are calibrated. The results show that the mean and standard deviation (SD) of the depth difference between the results of the proposed method and the in situ measurements in the shallow water data are approximately -0.006 m and 0.006 m, respectively. The mean and SD of the depth difference between the results of the proposed method and the in situ measurements in the deeper water data are approximately less than -0.066 m and 0.266 m, respectively.

Satellite-derived bathymetry using the ICESat-2 lidar and Sentinel-2 imagery datasets

Accurate bathymetric data is essential for marine, coastal ecosystems, and related studies. In the past decades, a lot of studies were investigated to obtain bathymetric data in shallow waters using satellite remotely sensed data. Satellite multispectral imagery has been widely used to estimate shallow water depths based on empirical models and physics-based models. However, the in-situ water depth information is essential (as the priori) to use the empirical model in a specific area, which limits its application, especially for remote reefs. In this study, the bathymetric maps in shallow waters were produced based on empirical models with only satellite remotely sensed data (i.e., the new ICESat-2 bathymetric points and Sentinel-2 multispectral imagery). The bathymetric points from the spaceborne ICESat-2 lidar were used in place of the in-situ auxiliary bathymetric points to train the classical empirical models (i.e., the linear model and the band ratio model). The bathymetric points were firstly extracted from noisy ICESat-2 raw data photons by an improved point cloud processing algorithm, and then were corrected for bathymetric errors (which were caused by the refraction effect in the water column, the refraction effect on the water surface, and the fluctuation effect on the water surface). With the trained empirical models and Sentinel-2 multispectral images, the bathymetric maps were produced for Yongle Atoll, in the South China Sea and the lagoon near Acklins Island and Long Cay, to the southeast of Bahama with four-date Sentinel-2 images. The bathymetry performance (including the accuracy and consistency of multi-date data) was evaluated and compared with the in-situ measurements. The results indicate that the bathymetric accuracy is well, and the RMSE is lower or close to 10% of the maximum depth for the two models with four-date images in two study areas. The consistency of multi-date data is well with the mean R2 of 0.97. The main novelties of this study are that the accuracy bathymetric points can be obtained from the ICESat-2 raw data using the proposed signal processing and error correction method, and using the ICESat-2 bathymetric points, the satellite multispectral imagery based on empirical models is no longer limited by local priori measurements, which were essential in previous studies. Hence, In the future, with the help of free and open-access satellite data (i.e., ICESat-2 data and Sentinel-2 imagery), this approach can be extended to a larger scale to obtain bathymetric maps in the shallow water of coastal areas, surroundings of islands and reefs, and inland waters.

Response of GPS-Tracked Drifters to Wind and Water Currents in a Tidal Estuary

Lagrangian field data in tidal shallow waters are rare, but valuable for the understanding of the spatiotemporal structure of flow and particle transport. The response of drifters to the wind and water flow in tidal shallow water was examined using correlation, spectral, and coherence analyses. Under moderate wind conditions (0-4 m/s), floating drifter motions in bounded sheltered water are affected by wind through low-frequency induced wind current rather than direct wind drag, when only a small portion of the drifter is unsubmerged. The field validation of both high- and low-resolution drifters with surface measured velocity from a fixed acoustic Doppler current profiler is good in the streamwise direction. The correlation between the drifter and fixed instrument velocities is low in the cross-stream direction due to strong spatial variability of the flow field. The evaluation shows that drifters are applicable to studying the flow dynamics of tidal water bodies in relation to small-scale processes.

Ship source level estimation in an uncertain environment via trans-dimensional Bayesian inversion

This paper considers the joint estimation of ship source spectral levels and environment parameters of a layered seabed via a trans-dimensional Bayesian matched-field inversion approach, with applications to shallow-water data collected with acoustic arrays in the 2017 Seabed Characterization Experiment conducted on the New England Shelf. The approach samples probabilistically over possible model parameterizations (number of seabed layers), and provides uncertainty estimates of ship source levels that include uncertainty due to the environment and source depth/range. Approaches to modeling of a distributed source (i.e., as multiple point sources) in the inversions will also be considered. The approach is applied to low-frequency tonal (propeller and machinery) noise in the 10–300 Hz frequency band due to large container ships passing near the arrays.This paper considers the joint estimation of ship source spectral levels and environment parameters of a layered seabed via a trans-dimensional Bayesian matched-field inversion approach, with applications to shallow-water data collected with acoustic arrays in the 2017 Seabed Characterization Experiment conducted on the New England Shelf. The approach samples probabilistically over possible model parameterizations (number of seabed layers), and provides uncertainty estimates of ship source levels that include uncertainty due to the environment and source depth/range. Approaches to modeling of a distributed source (i.e., as multiple point sources) in the inversions will also be considered. The approach is applied to low-frequency tonal (propeller and machinery) noise in the 10–300 Hz frequency band due to large container ships passing near the arrays.