A new algorithm for high-precision submarine topography imaging

730-738In this paper, a new algorithm for high-precision submarine topography imaging is proposed. The innovative idea comes from the most effective and widely used instruments: Multibeam echo sounder (MBES) and side scan sonar (SSS). The MBES can acquire bathymetric information with high precision, but its along-track resolution is related to the result of the beam angle multiplied by the slant range. The SSS combined with synthetic aperture sonar technology can achieve a high-precision along-track imaging resolution, but it cannot acquire bathymetric information directly below it. The proposed algorithm uses the beam footprints of the MBES in the along-track direction to perform the aperture synthesis and uses the time-domain and beam-domain imaging algorithms to acquire high-precision along-track imaging resolution and bathymetric information, to improve the along-track resolution and obtain the bathymetric information with high precision at the same time. Finally, an experiment is performed to evaluate the effectiveness of our method. Experimental results demonstrate that two targets, 13 cm in size, can be clearly observed from the obtained imaging. Moreover, their bathymetric information can be calculated by using the beamforming angle information

A Novel Sound Speed Profile Prediction Method Based on the Convolutional Long-Short Term Memory Network

As an important marine environmental parameter, sound velocity greatly affects the sound propagation characteristics in the ocean. In marine surveying work, prompt and low-cost acquisition of accurate sound speed profiles (SSP) is of immense significance for improving the measurement and positioning accuracy of marine acoustic equipment and ensuring underwater wireless communication. To address the problem of not being able to glean the accurate SSP in real time, we propose a convolution long short-term memory neural network (Conv-LSTM) which combines the long short-term memory (LSTM) neural network and convolution operation to predict the complete sound speed profile based on historical data. Considering SSP is a typical time series and has strong spatial correlation, Conv-LSTM can grasp not only the temporal relevance of time series, but also the spatial characteristics. The Argo temperature and salinity grid data of the North Pacific from 2004 to 2019 is imported to establish the model’s SSP dataset, and the convolution of input data is performed before going through the neurons in this recurrent neural network to extract the spatial relevance of the data itself. In the meantime, in order to prove the advanced nature of this model, we compare it with the LSTM network under the same parameter settings. The experimental results show that predicting the SSP time series at a single coordinate position under the same parameter conditions, it is best to predict the future SSP next month through the historical data of 24 months, and the prediction effect of Conv-LSTM is much better than that of the LSTM network, and the relative error (RE) is 0.872 m/s, which is 1.817 m/s less than that of LSTM. Predictions in the selected area are also exceedingly accurate relative to the actual data; the prediction error of deep water is less than 0.3 m/s, while RE on the surface layer is larger, exceeding 1.6 m/s

A Novel Sound Speed Profile Prediction Method Based on the Convolutional Long-Short Term Memory Network

As an important marine environmental parameter, sound velocity greatly affects the sound propagation characteristics in the ocean. In marine surveying work, prompt and low-cost acquisition of accurate sound speed profiles (SSP) is of immense significance for improving the measurement and positioning accuracy of marine acoustic equipment and ensuring underwater wireless communication. To address the problem of not being able to glean the accurate SSP in real time, we propose a convolution long short-term memory neural network (Conv-LSTM) which combines the long short-term memory (LSTM) neural network and convolution operation to predict the complete sound speed profile based on historical data. Considering SSP is a typical time series and has strong spatial correlation, Conv-LSTM can grasp not only the temporal relevance of time series, but also the spatial characteristics. The Argo temperature and salinity grid data of the North Pacific from 2004 to 2019 is imported to establish the model’s SSP dataset, and the convolution of input data is performed before going through the neurons in this recurrent neural network to extract the spatial relevance of the data itself. In the meantime, in order to prove the advanced nature of this model, we compare it with the LSTM network under the same parameter settings. The experimental results show that predicting the SSP time series at a single coordinate position under the same parameter conditions, it is best to predict the future SSP next month through the historical data of 24 months, and the prediction effect of Conv-LSTM is much better than that of the LSTM network, and the relative error (RE) is 0.872 m/s, which is 1.817 m/s less than that of LSTM. Predictions in the selected area are also exceedingly accurate relative to the actual data; the prediction error of deep water is less than 0.3 m/s, while RE on the surface layer is larger, exceeding 1.6 m/s

Shape Similarity Assessment Method for Coastline Generalization

Although shape similarity is one fundamental element in coastline generalization quality, its related research is still inadequate. Consistent with the hierarchical pattern of shape recognition, the Dual-side Bend Forest Shape Representation Model is presented by reorganizing the coastline into bilateral bend forests, which are made of continuous root-bends based on Constrained Delaunay Triangulation and Convex Hull. Subsequently, the shape contribution ratio of each level in the model is expressed by its area distribution in the model. Then, the shape similarity assessment is conducted on the model in a top–down layer by layer pattern. Contrast experiments are conducted among the presented method and the Length Ratio, Hausdorff Distance and Turning Function, showing the improvements of the presented method over the others, including (1) the hierarchical shape representation model can distinguish shape features of different layers on dual-side effectively, which is consistent with shape recognition, (2) its usability and stability among coastlines and scales, and (3) it is sensitive to changes in main shape features caused by coastline generalization

Effects of non-sinusoidal pitching motion on the propulsion performance of an oscillating foil.

Numerical simulations have been used in this paper to study the propulsion device of a wave glider based on an oscillating hydrofoil, in which the profile of the pitching and heaving motion have been prescribed for the sake of simplicity. A grid model for a two-dimensional NACA0012 hydrofoil was built by using the dynamic and moving mesh technology of the Computational Fluid Dynamics (CFD) software FLUENT and the corresponding mathematical model has also been established. First, for the sinusoidal pitching, the effects of the pitching amplitude and the reduced frequency were investigated. As the reduced frequency increased, both the mean output power coefficient and the optimal pitching amplitude increased. Then non-sinusoidal pitching was studied, with a gradual change from a sinusoid to a square wave as the value of β was increased from 1. It was found that when the pitching amplitude was small, the trapezoidal pitching profile could indeed improve the mean output power coefficient of the flapping foil. However, when the pitching amplitude was larger than the optimal value, the non-sinusoidal pitching motion negatively contributed to the propulsion performance. Finally, the overall results suggested that a trapezoidal-like pitching profile was effective for the oscillating foil of a wave glider when the pitching amplitude was less than the optimal value

A Comparison of Three Sediment Acoustic Models Using Bayesian Inversion and Model Selection Techniques

Many geoacoustic models are used to establish the relationship between the physical and acoustic properties of sediments. In this work, Bayesian inversion and model selection techniques are applied to compare combinations of three geoacoustic models and corresponding scattering models—the fluid model with the effective density fluid model (EDFM), the grain-shearing elastic model with the viscosity grain-shearing (VGS(λ)) model, and the poroelastic model with the corrected and reparametrized extended Biot–Stoll (CREB) model. First, the resolution and correlation of parameters for the three models are compared based on estimates of the posterior probability distributions (PPDs), which are obtained by Bayesian inversion using the backscattering strength data. Then, model comparison and selection techniques are utilized to assess the matching degree of model predictions and measurements qualitatively and to ascertain the Bayes factors in favor of each quantitatively. These studies indicate that the fluid and poroelastic models outperform the grain-shearing elastic model, in terms of both parameter resolution and the ability to produce predictions in agreement with measurements for sandy sediments. The poroelastic model is considered to be the best, as the inversion based on it can provide more highly resolved information of sandy sediments. Finally, the attempt to implement geoacoustic inversion with different models provides a relatively feasible remote sensing scheme for various types of sediments under unknown conditions, which needs further validation

A Method for Estimating Dominant Acoustic Backscatter Mechanism of Water-Seabed Interface via Relative Entropy Estimation

It is important to distinguish the dominant mechanism of seabed acoustic scattering for the quantitative inversion of seabed parameters. An identification scheme is proposed based on Bayesian inversion with the relative entropy used to estimate dominant acoustic backscatter mechanism. DiffeRential Evolution Adaptive Metropolis is used to obtain samples from posterior probability density in Bayesian inversion. Three mechanisms for seabed scattering are considered: scattering from a rough water-seabed interface, scattering from volume heterogeneities, and mixed scattering from both interface roughness and volume heterogeneities. Roughness scattering and volume scattering are modelled based on Fluid Theories using Small-Slope Approximation and Small-Perturbation Fluid Approximation, respectively. The identification scheme is applied to three simulated observation data sets. The results indicate that the scheme is promising and appears capable of distinguishing sediment volume from interface roughness scattering and can correctly identify the dominant acoustic backscatter mechanism

Quality Assessment Method for Linear Feature Simplification Based on Multi-Scale Spatial Uncertainty

This study discusses a method for quantitative quality assessment for the simplification of linear features. Considering the multi-scale nature of linear features, this paper combines the improved Douglas–Peucker method without threshold and the multiway tree model to construct a weighted hierarchical linear feature representation model called the Douglas–Peucker Multiway Tree (DMC-tree). Subsequently, the uncertainty computation is conducted from the root of the DMC-Tree top-down level by level to obtain the quality indexes. Then, the quality index of the whole linear feature is obtained by combining the indexes of every layer together with their weights. The results of the presented method are compared with those of the length ratio method and the Hausdorff distance method. The results show the advantages of the presented method over the others, including (1) its sensitivity to feature points of multiple scales, (2) the quantitative characteristics of the indexes, and (3) the finer granularity in assessment

Propulsion Performance of the Full-Active Flapping Foil in Time-Varying Freestream

A numerical investigation of the propulsion performance and hydrodynamic characters of the full-active flapping foil under time-varying freestream is conducted. The finite volume method is used to calculate the unsteady Reynolds averaged Navier–Stokes by commercial Computational Fluid Dynamics (CFD) software Fluent. A mesh of two-dimensional (2D) NACA0012 foil with the Reynolds number Re = 42,000 is used in all simulations. We first investigate the propulsion performance of the flapping foil in the parameter space of reduced frequency and pitching amplitude at a uniform flow velocity. We define the time-varying freestream as a superposition of steady flow and sinusoidal pulsating flow. Then, we study the influence of time-varying flow velocity on the propulsion performance of flapping foil and note that the influence of the time-varying flow is time dependent. For one period, we find that the oscillating amplitude and the oscillating frequency coefficient of the time-varying flow have a significant influence on the propulsion performance of the flapping foil. The influence of the time-varying flow is related to the motion parameters (reduced frequency and pitching amplitude) of the flapping foil. The larger the motion parameters, the more significant the impact of propulsion performance of the flapping foil. For multiple periods, we note that the time-varying freestream has little effect on the propulsion performance of the full-active flapping foil at different pitching amplitudes and reduced frequency. In summary, we conclude that the time-varying incoming flow has little effect on the flapping propulsion performance for multiple periods. We can simplify the time-varying flow to a steady flow field to a certain extent for numerical simulation