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Aiming at the problem that lack of the measured sound velocity profile (SVP) leads to the unreliable underwater positioning solution, this paper proposed an efficient underwater positioning method by the self-constraint conditions of water depth and sound velocity gradient. To construct the depth constraint condition, the sound propagation distance error model is deduced by acoustic ray tracing, and the sound vertical propagation error model related to the incident angle and sound velocity error is given firstly. By fitting the vertical propagation error model, the reference depth is solved, and the vertical propagation distances between the transducer and the underwater control points of all observation epochs are gotten. Then with the solved vertical distance of each epoch and the sound velocity gradient from neighbor SVPs as the constraint conditions, the SVP is retrieved by the simulated annealing (SA) algorithm. With the retrieved SVP, the underwater positioning can be performed when the measured SVP is absent. The proposed method was verified by an experiment in the 3000 m depth water area of the South China Sea. The results achieved 2.07 m/s of standard deviation of the SVP inversion, centimeter-level horizontal positioning accuracy and 0.54 m of vertical positioning accuracy under the circumstance of lack of SVP measurement. [ABSTRACT FROM AUTHOR]

We propose a new method that considers the envelope, phase attributes, and texture analysis of the subbottom profile to automatically obtain continuous and accurate horizon picking. This method overcomes the shortcomings of the traditional methods in envelope and phase automatic horizon picking in terms of accuracy and continuity, and in efficiency when it comes to manual picking. Under the constraint of the envelope threshold from the envelope image and the texture segmentation from the phase image, the accurate envelope horizons are picked from the envelope image. Through mean filtering and template enhancement, the fine phase horizons are picked from the phase image. Finally, by combining the two kinds of picked horizons, more continuous, accurate, and finer horizons are obtained. The proposed method was tested on a real data set. Plain Language Summary: The proposed method considers the complementarity combination of envelope and phase horizon picking and integrates the envelope, phase, and texture attributes, allowing to get better horizons than a single horizon‐picking method. In the proposed method, the envelope horizons constrain the locations of layers and reduce the redundancy of the phase horizons, while the phase horizons avoid the influence of the envelope and guarantee the continuity of the picked horizons. Key Points: The proposed method obtains envelope horizons by combining envelope threshold segmentation on the envelope image and texture segmentation on the phase imageThe proposed method obtains phase horizons by applying template enhancement and horizon tracking to the phase imageThe final horizons can be obtained by combining envelope horizons and phase horizons [ABSTRACT FROM AUTHOR]

In order to solve the problems of low efficiency and dependability of human experience in shot hole depth recognition by manual interpretation of the massive drilling videos, this paper proposed a repetition counting framework from the perspective of representation object motion, and firstly studied and realized the automatic shot hole depth recognition by adopting the proposed framework by combining multiple deep learning-based models. Firstly, the periodic motion characteristics of the objects in the drilling video were analyzed, and the motion extraction model of the representative object was established based on YOLOv5 and Deep SORT algorithm to obtain the spatiotemporal sequence. Then, the sequence noise and redundant information were filtered according to the spatiotemporal relationship between the drill pipes and workers, to obtain a coherent, smooth, and clean motion sequence. On this basis, a drill pipe motion counting model was established by using the YOLOv5 network to detect the temporal boundary and repetition number of the drill pipe motion. Finally, combined with the pipe motion extraction model and counting model, the comprehensive reliable recognition method of shot hole depth was presented. The shot hole depth recognition trial was carried out on 1029 drilling videos from the Bureau of Geophysical Prospecting INC. The proposed method achieved 90.5% recognition accuracy (Acc) and 0.027 mean absolute error (MAE) with 64 frames per second (FPS) and 0.39 average recognition time ratio (R t). Therefore, the proposed repetition counting framework solved the problem of efficient and intelligent drilling depth recognition of massive drilling videos and thus provided a new idea for repetition counting. • A new repetition counting framework for shot hole depth recognition based on representative object motion was proposed. • The explicit features were added into the optimization of the framework components. • The multi-scale temporal correlation was transformed into process detection to handle the non-stationary period problem. • A sequence denoising method based on spatiotemporal relationship was proposed to handle the noise and redundant information. [ABSTRACT FROM AUTHOR]

To accurately achieve side scan sonar (SSS) image target detection, a novel target detection algorithm based on a neutrosophic set (NS) and diffusion maps (DMs) is proposed in this paper. Firstly, the neutrosophic subset images were obtained by transforming the input SSS image into the NS domain. Secondly, the shadowed areas of the SSS image were detected using the single gray value threshold method before the diffusion map was calculated. Lastly, based on the diffusion map, the target areas were detected using the improved target scoring equation defined by the diffusion distance and texture feature. The experiments using SSS images of single clear and unclear targets, with or without shadowed areas, showed that the algorithm accurately detects targets. Experiments using SSS images of multiple targets, with or without shadowed areas, showed that no false or missing detections occurred. The target areas were also accurately detected in SSS images with complex features such as sand wave terrain. The accuracy and effectiveness of the proposed algorithm were assessed. [ABSTRACT FROM AUTHOR]

Airborne LiDAR bathymetry (ALB) has been shown to have the ability to retrieve water turbidity using the waveform parameters (i.e., slopes and amplitudes) of volume backscatter returns. However, directly and accurately extracting the parameters of volume backscatter returns from raw green-pulse waveforms in shallow waters is difficult because of the short waveform. This study proposes a new accurate and efficient method for the remote sensing of suspended sediment concentrations (SSCs) in shallow waters based on the waveform decomposition of ALB. The proposed method approaches raw ALB green-pulse waveforms through a synthetic waveform model that comprises a Gaussian function (for fitting the air-water interface returns), triangle function (for fitting the volume backscatter returns), and Weibull function (for fitting the bottom returns). Moreover, the volume backscatter returns are separated from the raw green-pulse waveforms by the triangle function. The separated volume backscatter returns are used as bases to calculate the waveform parameters (i.e., slopes and amplitudes). These waveform parameters and the measured SSCs are used to build two power SSC models (i.e., SSC (C)-Slope (K) and SSC (C)-Amplitude (A) models) at the measured SSC stations. Thereafter, the combined model is formed by the two established C-K and C-A models to retrieve SSCs. SSCs in the modeling water area are retrieved using the combined model. A complete process for retrieving SSCs using the proposed method is provided. The proposed method was applied to retrieve SSCs from an actual ALB measurement performed using the Optech Coastal Zone Mapping and Imaging LiDAR in a shallow and turbid water area. A mean bias of 0.05 mg/L and standard deviation of 3.8 mg/L were obtained in the experimental area using the combined model. [ABSTRACT FROM AUTHOR]

To obtain the high-resolution seabed topography and overcome the limitations of existing topography reconstruction methods in requiring external bathymetric data and ignoring the effects of sediment variations and Side-Scan Sonar (SSS) image quality, this study proposes a method of reconstructing seabed topography from SSS images with a self-constraint condition. A reconstruction model is deduced by Lambert's law and the seabed scattering model. A bottom tracking method is put forward to get the along-track SSS towfish heights and the initial seabed topography in the SSS measuring area is established by combining the along-track towfish heights, towfish depths and tidal levels obtained from Global Navigation Satellite System (GNSS). The complete process of reconstructing seabed topography is given by taking the initial topography as self-constraint and the high-resolution seabed topography is finally obtained. Experiments verified the proposed method by the data measured in Zhujiang River, China. The standard deviation of less than 15 cm is achieved and the resolution of the reconstructed topography is about 60 times higher than that of the Digital Elevation Model (DEM) established by bathymetric data. The effects of noise, suspended bodies, refraction of wave in water column, sediment variation, the determination of iteration termination condition as well as the performance of the proposed method under these effects are discussed. Finally, the conclusions are drawn out according to the experiments and discussions. The proposed method provides a simple and efficient way to obtain high-resolution seabed topography from SSS images and is a supplement but not substitution for the existing bathymetric methods. [ABSTRACT FROM AUTHOR]

The angular response (AR) effect which reduces multibeam backscatter image quality significantly cannot be removed by the traditional AR correction methods effectively. Therefore, the paper puts forward two self-adaptive AR correction methods, namely the AR modeling method and the AR clustering method. The former uses the AR parameters of the average AR curve for modeling and correction, while the latter uses the AR curve of each seabed sediment for AR correction. Both methods overcome the defects of the traditional methods, and improve the image quality obviously. The validities of the methods have been verified by the experiments. [ABSTRACT FROM AUTHOR]

To fulfill side scan sonar (SSS) image segmentation accurately and efficiently, a novel segmentation algorithm based on neutrosophic set (NS) and quantum-behaved particle swarm optimization (QPSO) is proposed in this paper. Firstly, the neutrosophic subset images are obtained by transforming the input image into the NS domain. Then, a co-occurrence matrix is accurately constructed based on these subset images, and the entropy of the gray level image is described to serve as the fitness function of the QPSO algorithm. Moreover, the optimal two-dimensional segmentation threshold vector is quickly obtained by QPSO. Finally, the contours of the interested target are segmented with the threshold vector and extracted by the mathematic morphology operation. To further improve the segmentation efficiency, the single threshold segmentation, an alternative algorithm, is recommended for the shadow segmentation by considering the gray level characteristics of the shadow. The accuracy and efficiency of the proposed algorithm are assessed with experiments of SSS image segmentation. [ABSTRACT FROM AUTHOR]

To decrease the time consumption and the labor intensity in the absolute datum transfer of traditional seafloor control network measurement, a new method, namely sailing-circle positioning method, is put forward in this paper. First, the traditional intersection positioning model is improved by considering the equivalent sound velocity profile error as an unknown parameter in the adjustment model. Second, the effect of geometric dilution of precision (GDOP) on positioning accuracy is analyzed. By seeking for the minimum of GDOP, it is concluded that the absolute datum transfer can achieve the highest accuracy in the condition of sailing along a circle relative to other sailing paths. Moreover, the optimal radius of the circle for the accurate datum transfer is also given out. Besides, the correlation between the accuracy of datum transfer and the sound velocity error in this method is analyzed. Finally, the new method was tested and verified by the experiments in Songhua lake with the water depth of 60 m and in South China sea with the water depth of 2000 m, respectively. These experiment results show that the new method can improve the accuracy and efficiency of traditional datum transfer method significantly. [ABSTRACT FROM AUTHOR]

Being influenced by the factors of water level difference, tide, shortening closure gap, surrounding terrain and riverbed topography, etc., both the variations of flow velocity and water level in the closure gap are markedly larger than those in the general river. The phenomenon will bring potential safety hazards to the reservoir dam under construction, and thus the measurements of flow velocity and water level in the closure gap become very important in guaranteeing the safety of the reservoir dike project. Because of the safety, the measuring accuracy and the instrument performance, the traditional measuring methods of water level and flow velocity are less than ideal. In order to solve the problem, a buoy-based synthetic measuring system, which includes buoy body, balance weight, GPS (Global Positioning System), MRU (Motion Reference Unit), compass and computer, is developed. Besides, a complete data processing method, which includes the data synchronism, attitude correction and quality control of the measuring data, the self-adaptive extraction of water level and the calculation of flow velocity, is put forward in this paper. The system and the method were used for the project of Qingcaosha reservoir closure, and achieved the water level with a bias of less than 7 cm and the standard deviation of ±3.7 cm, and the flow velocity with a bias of less than 7 cm/s and the standard deviation of ±3.8 cm/s, satisfied the accuracy requirements of monitoring water level and flow velocity, and solved the measurement problem in the project. [ABSTRACT FROM AUTHOR]

Based on postprocessor GPS PPK technology, we carried out the research on long-distance tide observation and extraction, the determination of vertical datum and the construction of vertical-datum transformation model. A model for calculating a height series for instantaneous water surface was researched, meanwhile, the best cut-off periods for tidal extraction for anchored and on-the-fly status are proposed. Finally, based on long-term anchor PPK and tide-gauge tidal levels for different situations, a determination of MSL and chart datum as well as the models for calculating their geodetic heights were investigated and proposed. A method to establish a seamless chart datum geodetic height model is presented. An test project showed that the accuracy of GPS tide level reference to chart datum measurement method as presented in paper can be better than 10 cm when the GPS baseline is less than 100 km. [ABSTRACT FROM AUTHOR]

For mitigating the impact of outliers in measurements and background map, a new method for underwater terrain matching navigation, base on robust statistics theory is proposed in this paper. By applying the M estimation theory, the robustness of the algorithm is improved. Experiments proved the good performance of the method. [ABSTRACT FROM AUTHOR]

Multibeam bathymetric system (MBS) has been widely applied in the marine surveying for providing high-resolution seabed topography. However, some factors degrade the precision of bathymetry, including the sound velocity, the vessel attitude, the misalignment angle of the transducer and so on. Although these factors have been corrected strictly in bathymetric data processing, the final bathymetric result is still affected by their residual errors. In deep water, the result usually cannot meet the requirements of high-precision seabed topography. The combined effect of these residual errors is systematic, and it's difficult to separate and weaken the effect using traditional single-error correction methods. Therefore, the paper puts forward a new method for weakening the effect of residual errors based on the frequency-spectrum characteristics of seabed topography and multibeam bathymetric data. Four steps, namely the separation of the low-frequency and the high-frequency part of bathymetric data, the reconstruction of the trend of actual seabed topography, the merging of the actual trend and the extracted microtopography, and the accuracy evaluation, are involved in the method. Experiment results prove that the proposed method could weaken the combined effect of residual errors on multibeam bathymetric data and efficiently improve the accuracy of the final post-processing results. We suggest that the method should be widely applied to MBS data processing in deep water. [ABSTRACT FROM AUTHOR]

To improve the underwater positioning precision of the traditional underwater positioning method that only uses one-way sound propagation, the paper proposes a new underwater positioning method by considering the location difference between transmission and reception of sound waves in circle-sailing positioning. In the method, firstly a fast calculation of the incidence angles was given under the depth constraint. And then, a precise positioning method was put forward by correcting the one-way travel time and considering the process of transmitting and receiving sound waves to double the observations to improve the underwater positioning precision. Two data sets observing the identical transponder were used to test the validity. The experiments verified the proposed method and the results showed that the precision of underwater positioning was improved considerably, especially in the vertical direction. The proposed method achieved the more uniform horizontal locations than the traditional method. • Fast calculation method of incidence angles under depth constraint. • Separated incidence angles and one-way travel times in a round trip. • Double the number of observation equation. • Improve the precision of the seafloor transponder. [ABSTRACT FROM AUTHOR]

Sub-bottom sediment classifications have been widely used in marine science and engineering to obtain high-resolution information on types of sediments; however, these are often plagued by inaccuracies. Classification difficulties arise from the inability to effectively filter multiple reflections, extract representative lithology characteristic parameters, identify sub-bottom layer interfaces, extract image samples, control sample quality, optimise characteristic parameters, etc. To generate a highly accurate sub-bottom profile sediment map, a five-step classification method that considers two key lithology characteristic parameters of sub-bottom profile acoustic data was proposed. First, multiple reflections were filtered from the sea surface and sub-bottom layer interfaces of the primary signal. Second, two key characteristic parameters (relative backscattering intensity difference and attenuation compensation residual) were calculated. These reflect the relative differences in backscattering intensity and the attenuation compensation between adjacent interfaces based on the sound intensity attenuation model of a sub-bottom profile. Third, a combined method based on the sediment quality factor and peak trough of the echo signal loss level curve was employed to identify the actual interfaces between layers. An additional technique was proposed to determine the image sample width and preferred characteristic parameters. The resulting high-quality image samples and preferred characteristic parameters not only resulted in a faster convergence rate and increased ability of self-aggregation and identification, but also ensured that the training results met the convergence accuracy requirement. Ultimately, the preferred image samples were trained to classify the overall sub-bottom map of a selected test area of approximately 36 km2 in Bahai Bay, China. Compared with traditional methods, a considerably higher sediment identification accuracy was obtained. The experimental results indicate that the contribution rate of the two key lithology characteristic parameters was 65.49% according to principal component analysis, and the internal and external compatibilities were 97.98% and 84.76% for the training image samples, respectively. The total identification accuracy for the sub-bottom profile map was 98.2%. The two key characteristic parameters accurately captured the acoustic characteristics of sub-bottom sediments, significantly improving sediment classification. These results show that this method could be used to help refine the distributional estimates of submarine mineral resources. • A combined filtering method using predictive deconvolution and iterative feedback was proposed. • Two key parameters were derived from sound intensity attenuation model of sub-bottom profile. • Sample width selection and characteristic parameter preferred-selection method were proposed. • Experimental result indicated that identification accuracy for sub-bottom profile map was 98.2%. [ABSTRACT FROM AUTHOR]

The integration of GPS compass, GPS differential technique and ADCP effectively eliminates the impact of magnetic interference and bottom tracking bias due to bedload movement, well improves the accuracy and widens the applications of ADCP measurement. However, an accompanying problem, the heading misalignment angle α between the ADCP's Y axis and external compass will be introduced into ADCP measurement and brings a significant impact on the calculation of flow velocity. Therefore it becomes very necessary to determine and compensate α. Although the conventional method, the Course Method, has been widely used in the determination of α, it still needs to be improved in the operability, efficiency, robustness, self-adapting detection and accuracy. Thus, a robust method, Multi-epoch Method, is proposed in this paper. In this method, with the help of GPS velocity and bottom-tracking velocity, a α can be got at each epoch, then a series of αs can be obtained at a course. Then, the final α can be achieved through filtering and averaging the series of αs. The new method efficiently overcomes the drawbacks of the conventional method, and can conveniently achieve an accurate α. A synthetic experiment has implemented to testify the new method. A α of 6.22° with internal-verification accuracy of ±0.04° and external-verification accuracy of ±0.04° is determined by the new method, which causes an absolute flow velocity error of −0.31~0.29m/s and a relative flow velocity error of nearly 56% in 4 transects, while after the correction of α, both of them decrease to less than 0.02m/s and 3.3% respectively, which are meet with the requirement of ADCP measurement. [ABSTRACT FROM AUTHOR]

The towing operation mode of side‐scan sonar system (SSS) easily results in dislocations and distortions of targets in the SSS strip image, brings difficulty to the mosaic of these strip images by the geocoding method or the tessellation‐line method and affects the recognition and understanding for seabed relief. Therefore this study proposes a new method, namely the segment‐image mosaic method based on corresponding features of the two adjacent SSS strip images. Through SSS image preprocessing, segment match based on corresponding features and image fusion in the common coverage area based on wavelet transformation, this new method overcomes the drawbacks of the traditional methods of image mosaic, fulfils well the mosaic of SSS strip images and finally a whole‐area SSS image is formed. Experiments have verified that the mosaic image formed by the new method can correctly reflect the position, shape and distribution of seabed targets, which is helpful to understand seabed relief. [ABSTRACT FROM AUTHOR]

The shift in IF (instantaneous frequency) series and the corresponding relaxation time have the potential to characterize sediment properties. However, these attributes derived from SBP (sub-bottom profiler) data are seldom used for offshore site investigations because of the unsoundness in attribute calculation. To overcome this problem, a new reliable method combining VMD (variational mode decomposition) and WVD (Wigner–Ville distribution), as well as relaxation time, is presented. Since the number of modes in classical VMD should be provided in advance, a modified VMD algorithm, MVMD (modified variational mode decomposition), is proposed here, where the distribution of the frequency domain of modes is taken into account to automatically determine the number of modes. Through the relaxation time model, the IF data of a series of pings calculated through MVMD-WVD are transformed into a relaxation time map. A robust estimation algorithm is applied to the relaxation time map to reduce the effects of interferences and obtain robust relaxation times. The final relaxation time data are used to determine the sediment types. Real data from SBP experiments, as well as borehole sampling and geotechnical analysis results, verified the good performance of the proposed method. [ABSTRACT FROM AUTHOR]

With the increasing number of underwater pipeline investigation activities, the research on automatic pipeline detection is of great significance. At this stage, object detection algorithms based on Deep Learning (DL) are widely used due to their abilities to deal with various complex scenarios. However, DL algorithms require massive representative samples, which are difficult to obtain for pipeline detection with sub-bottom profiler (SBP) data. In this paper, a zero-shot pipeline detection method is proposed. First, an efficient sample synthesis method based on SBP imaging principles is proposed to generate samples. Then, the generated samples are used to train the YOLOv5s network and a pipeline detection strategy is developed to meet the real-time requirements. Finally, the trained model is tested with the measured data. In the experiment, the trained model achieved a mAP@0.5 of 0.962, and the mean deviation of the predicted pipeline position is 0.23 pixels with a standard deviation of 1.94 pixels in the horizontal direction and 0.34 pixels with a standard deviation of 2.69 pixels in the vertical direction. In addition, the object detection speed also met the real-time requirements. The above results show that the proposed method has the potential to completely replace the manual interpretation and has very high application value. [ABSTRACT FROM AUTHOR]

Horizon picking from sub-bottom profiler (SBP) images has great significance in marine shallow strata studies. However, the mainstream automatic picking methods cannot handle multiples well, and there is a need to set a group of parameters manually. Considering the constant increase in the amount of SBP data and the high efficiency of deep learning (DL), we proposed a physicals-combined DL method to pick the horizons from SBP images. We adopted the DeeplabV3+ net to extract the horizons and multiples from SBP images. We generated a training dataset from the Jiaozhou Bay survey (Shandong, China) and the Zhujiang estuary survey (Guangzhou, China) to increase the applicability of the trained model. After the DL processing, we proposed a simulated Radon transform method to eliminate the surface-related multiples from the prediction by combining the designed pseudo-Radon transform and correlation analysis. We verified the proposed method using actual data (not involved in the training dataset) from Jiaozhou Bay and Zhujiang estuary. The positions of picked horizons are accurate, and multiples are suppressed. [ABSTRACT FROM AUTHOR]

To overcome the shortcomings of the traditional manual detection of underwater targets in side-scan sonar (SSS) images, a real-time automatic target recognition (ATR) method is proposed in this paper. This method consists of image preprocessing, sampling, ATR by integration of the transformer module and YOLOv5s (that is, TR–YOLOv5s), and target localization. By considering the target-sparse and feature-barren characteristics of SSS images, a novel TR–YOLOv5s network and a down-sampling principle are put forward, and the attention mechanism is introduced in the method to meet the requirements of accuracy and efficiency for underwater target recognition. Experiments verified the proposed method achieved 85.6% mean average precision (mAP) and 87.8% macro-F2 score, and brought 12.5% and 10.6% gains compared with the YOLOv5s network trained from scratch, and had the real-time recognition speed of about 0.068 s per image. [ABSTRACT FROM AUTHOR]

A sub-bottom profiler (SBP) can capture the sediment interfaces and properties of different types of sediment. Horizon picking from SBP images is one of the most crucial steps in marine sub-bottom sediment interpretation. However, traditional horizon picking methods are good at obtaining the main horizons representing the main reflectors while ignoring the detailed horizons. While detailed horizons are the prime objective, many tiny structures caused by interference echoes will also be picked. To overcome this limitation, an integrated horizon picking method for obtaining the main and detailed horizons simultaneously is proposed in this paper. A total of three main process steps: the diffusion filtering method, the enhancement filtering method as well as the local phase calculation method, are used to help obtain the main and detailed horizons. The diffusion filtering method smooths the SBP images and preserves reflectors. Enhancement filtering can eliminate outliers and enhance reflectors. The local phase can be used to highlight all of the reflections and help in the choosing of detailed horizons. A series of experiments were then performed to validate the effectiveness of the proposed method, and good performances were achieved. [ABSTRACT FROM AUTHOR]

Bubble plumes, as main manifestations of seabed gas leakage, play an important role in the exploration of natural gas hydrate and other resources. Multibeam water column images have been widely used in detecting bubble plume targets in recent years because they can wholly record water column and seabed backscatter strengths. However, strong noises in multibeam water column images cause many issues in target detection, and traditional target detection methods are mainly used in optical images and are less efficient for noise-affected sonar images. To improve the detection accuracy of bubble plume targets in water column images, this study proposes a target detection method based on the bag of visual words (BOVW) features and support vector machine (SVM) classifier. First, the characteristics of bubble plume targets in water column images are analyzed, with the conclusion that the BOVW features can well express the gray scale, texture, and shape characteristics of bubble plumes. Second, the BOVW features are constructed following steps of point description extraction, description clustering, and feature encoding. Third, the quadratic SVM classifier is used for the recognition of target images. Finally, a procedure of bubble plume target detection in water column images is described. In the experiment using the measured data in the Strait of Georgia, the proposed method achieved 98.6% recognition accuracy of bubble plume targets in validation sets, and 91.7% correct detection rate of the targets in water column images. By comparison with other methods, the experimental results prove the validity and accuracy of the proposed method, and show potential applications of our method in the exploration and research on ocean resources. [ABSTRACT FROM AUTHOR]

Traditional manual horizon picking is time-consuming and laborious, while automatic picking methods often suffer from the limited scope of their applications and the discontinuity of picked results. In this paper, we propose a novel method for automatic horizon picking from sub-bottom profiles (SBP) by an improved filtering algorithm. First, a clear and fine SBP image is formed using an intensity transformation method. On this basis, a novel filtering method is proposed by improving the multi-scale enhancement filtering algorithm to obtain clear horizons from an SBP image. The improvement is performed by applying a vertical suppression weighting term based on the form of logistic function, which is constructed by using the eigenvectors from the Hessian matrix. Then, the filtered image is segmented using a threshold method, and the horizon points in the SBP image are picked. After that, a horizon linking method is applied, which uses the horizon directions to refine the picked horizon points. The proposed method has been verified experimentally, and accurate and continuous horizons were obtained. Finally, the proposed method is discussed and some conclusions are drawn. [ABSTRACT FROM AUTHOR]

The detection of gas plumes from multibeam water column (MWC) data is the most direct way to discover gas hydrate reservoirs, but current methods often have low reliability, leading to inefficient detections. Therefore, this paper proposes an automatic method for gas plume detection and segmentation by analyzing the characteristics of gas plumes in MWC images. This method is based on the AdaBoost cascade classifier, combining the Haar-like feature and Local Binary Patterns (LBP) feature. After obtaining the detected result from the above algorithm, a target localization algorithm, based on a histogram similarity calculation, is given to exactly localize the detected target boxes, by considering the differences in gas plume and background noise in the backscatter strength. On this basis, a real-time segmentation method is put forward to get the size of the detected gas plumes, by integration of the image intersection and subtraction operation. Through the shallow-water and deep-water experiment verification, the detection accuracy of this method reaches 95.8%, the precision reaches 99.35% and the recall rate reaches 82.7%. Integrated with principles and experiments, the performance of the proposed method is analyzed and discussed, and finally some conclusions are drawn. [ABSTRACT FROM AUTHOR]

Due to the influence of equipment instability and surveying environment, scattering echoes and other factors, it is sometimes difficult to obtain high-quality sub-bottom profile (SBP) images by traditional denoising methods. In this paper, a novel SBP image denoising method is developed for obtaining underlying clean images based on a non-local low-rank framework. Firstly, to take advantage of the inherent layering structures of the SBP image, a direction image is obtained and used as a guidance image. Secondly, the robust guidance weight for accurately selecting the similar patches is given. A novel denoising method combining the weight and a non-local low-rank filtering framework is proposed. Thirdly, after discussing the filtering parameter settings, the proposed method is tested in actual measurements of sub-bottom, both in deep water and shallow water. Experimental results validate the excellent performance of the proposed method. Finally, the proposed method is verified and compared with other methods quantificationally based on the synthetic images and has achieved the total average peak signal-to-noise ratio (PSNR) of 21.77 and structural similarity index (SSIM) of 0.573, which is far better than other methods. [ABSTRACT FROM AUTHOR]

To obtain the mudflat topography when existing measuring systems and data processing methods are impracticable under special conditions, this paper presents a hovercraft-borne LiDAR (light detection and ranging) system and a novel comprehensive filtering method. The system is based on a hovercraft and equipped with a laser scanner and a POS (position and orientation system). The filtering method firstly segments the point cloud into different segments by combining the geometric and intensity information, then fitting the ground surface by cloth simulation method, and finally synthetically extracts the ground points with three constraints. These constraints are the distance of the point to the fitting surface, the normal difference between the point and the fitting surface, and the proportion of the possible ground points in the total points of each segment. The effectiveness of the measurement system and the development of the post-processing results were verified on the basis of field measurements, in which a total filtering error of 0.3% and the elevation accuracy of 6.4 cm were obtained. The proposed system and methods provide a new way for efficient and accurate topographic survey on mudflats. [ABSTRACT FROM AUTHOR]

Zhao, X.; Wang, X.; Zhao, J., and Zhou, F., 2021. An improved water-land discriminator using laser waveform amplitudes and point cloud elevations of airborne LIDAR. Journal of Coastal Research, 37(6), 1158–1172. Coconut Creek (Florida), ISSN 0749-0208. Laser waveforms or point clouds of airborne LIDAR are used to distinguish water and land. However, false alarms often occur in coastal areas with complex environments when laser waveforms are used. Elevations of three-dimensional (3D) point clouds can be used to help discriminate ocean and land but fail to identify inland waters. An improved water-land discriminator that uses laser waveform amplitudes and point cloud elevations is proposed in this study. First, 3D point cloud elevations derived using infrared (IR) laser are used as features to conduct ocean-land discrimination with fuzzy c-means clustering. Second, amplitudes of IR laser waveforms are used to identify inland waters from land derived by ocean-land discrimination. The proposed method is applied to data collected using Optech coastal zone mapping and imaging LIDAR (CZMIL). Land boundary derived from digital orthophoto map is used as a reference to evaluate different water-land discriminators. Results showed that consistency of the water-land interface derived using the improved water-land discriminator is higher with the reference interface than that derived by traditional waveform saturation, waveform clustering, and 3D point cloud methods. Overall accuracy of water-land points discriminated via waveform saturation, waveform clustering, 3D point cloud, and improved water-land discriminator is 93.80%, 93.84%, 95.66%, and 99.27%, respectively. High accuracy of the water-land interface and points indicates the effectiveness of the improved water-land discriminator for Optech CZMIL. [ABSTRACT FROM AUTHOR]

Sailing‐circle GPS‐acoustic method, prevalently applied for measuring ocean floor crustal deformation, allows the precise positioning of an underwater transponder in a global frame. In the method, distance estimation requires prior information, such as transponder depth or incident angle. However, sometimes the prior information is inaccurate or absent. Under these circumstances, this study proposes two kinds of positioning methods that utilize an iteration composed of an inverse or direct ray tracing method and the least squares approach to constantly modify the initial data to obtain an accurate incident angle and coordinates of an underwater transponder. Even though an accurate incident angle can be got and there is no time delay or spatial‐temporal heterogeneities of SSP (sound speed profile), SSP density and geometric incident angle still influence ranging errors and PDOP (position dilution of precision) during distance intersection. In order to reduce the two kinds of impacts, this paper deduces a positioning error model based on the principle of sailing‐circle method. On the one hand, the proposed algorithms and model are verified by a simulated experiment for various SSP density, geometric incident angle, and transponder depth. On the other hand, in a practical experiment, average positioning accuracy of 0.025 m and vertical accuracy of −0.02 m are achieved, and the improved accuracy is about 85% higher than that of the ordinary sailing‐circle method. Plain Language Summary: GPS‐acoustic method is a kind of method that utilizes GPS, which is usually placed in our vehicle for navigation, and sonars, which can measure the distance between two points in water, for determining the position of a fixed point underwater. By continuously monitoring the location of this point, the position change is obtained and the change can help us predict earthquakes and study earthquakes. Among GPS‐acoustic methods, sailing‐circle method that the vessel equipped with GPS and sonars drives a circle centered on the underwater point can gain the best positioning accuracy. However, the method requires accurate prior information, such as transponder depth or incident angle. When the prior information is inaccurate or absent, accuracy will decrease and therefore, we propose two kinds of new sailing‐circle methods to deal with this situation. And it is noted that whether the prior information is accurate or not, there will be errors in the final positioning results caused by instruments, algorithms, and environments. Consequently, based on the principle of sailing‐circle method, we deduce the positioning error model to guide us design various parameters in a practical experiment, such as sailing radius, and improve the positioning accuracy. The ultimate goal of this paper is to improve the positioning accuracy of sailing‐circle method. To check the effectiveness of the two proposed methods and model, on the one hand, they are verified by a simulated experiment for various situations. On the other hand, in a practical experiment, positioning accuracy of the proposed method is about 85% higher than that of the ordinary method. Key Points: Two new sailing‐circle GPS‐acoustic methodsPositioning error model with respect to geometric incident angle (or sailing radius) and the density of sound speed profile is deducedGeometric incident angle should be controlled within 50° and the smaller the density of sound speed profile, the better [ABSTRACT FROM AUTHOR]

Side-scan sonar (SSS) is used for obtaining high-resolution seabed images, but with low position accuracy without using the Ultra Short Base Line (USBL) or Short Base Line (SBL). Multibeam echo sounder (MBES), which can simultaneously obtain high-accuracy seabed topography as well as seabed images with low resolution in deep water. Based on the complementarity of SSS and MBES data, this paper proposes a new method for acquiring high-resolution seabed topography and surface details that are difficult to obtain using MBES or SSS alone. Firstly, according to the common seabed features presented in both images, the Speeded-Up Robust Features (SURF) algorithm, with the constraint of image geographic coordinates, is adopted for initial image matching. Secondly, to further improve the matching performance, a template matching strategy using the dense local self-similarity (DLSS) descriptor is adopted according to the self-similarities within these two images. Next, the random sample consensus (RANSAC) algorithm is used for removing the mismatches and the SSS backscatter image geographic coordinates are rectified by the transformation model established based on the correct matched points. Finally, the superimposition of this rectified SSS backscatter image on MBES seabed topography is performed and the high-resolution and high-accuracy seabed topography and surface details can be obtained. [ABSTRACT FROM AUTHOR]

To obtain large-scale seabed surface image, this paper proposes a side-scan sonar (SSS) image mosaic method using couple feature points (CFPs) with constraint of track line positions. The SSS geocoded images are firstly used to form a coarsely mosaicked one and the overlapping areas between adjacent strip images can be determined based on geographic information. Inside the overlapping areas, the feature point (FP) detection and registration operation are adopted for both strips. According to the detected CFPs and track line positions, an adjustment model is established to accommodate complex local distortions as well as ensure the global stability. This proposed method effectively solves the problem of target ghosting or dislocation and no accumulated errors arise in the mosaicking process. Experimental results show that the finally mosaicked image correctly reflects the object distribution, which is meaningful for understanding and interpreting seabed topography. [ABSTRACT FROM AUTHOR]

Suspended sediment concentrations (SSCs) have been retrieved accurately and effectively through waveform methods by using green-pulse waveforms of airborne LiDAR bathymetry (ALB). However, the waveform data are commonly difficult to analyze. Thus, this paper proposes a 3D point-cloud method for remote sensing of SSCs in calm waters by using the range biases of green surface points of ALB. The near water surface penetrations (NWSPs) of green lasers are calculated on the basis of the green and reference surface points. The range biases (ΔS) are calculated by using the corresponding NWSPs and beam-scanning angles. In situ measured SSCs (C) and range biases (ΔS) are used to establish an empirical C-ΔS model at SSC sampling stations. The SSCs in calm waters are retrieved by using the established C-ΔS model. The proposed method is applied to a practical ALB measurement performed by Optech Coastal Zone Mapping and Imaging LiDAR. The standard deviations of the SSCs retrieved by the 3D point-cloud method are less than 20 mg/L. [ABSTRACT FROM AUTHOR]

Precise sound speed profile (SSP) information is critical when using sonar for underwater terrain navigation. Nevertheless, acquiring SSP information in real-time is difficult, especially in underwater navigation environment. To account for this problem, this paper presents an underwater navigation method that applies real-time SSP correction to the terrain measurements. The method uses a probe to measure the surface sound speed simultaneously. Meanwhile, redundancy of topographic measurement data is exploited to derive the equivalent SSP information. The acquired equivalent SSP is updated continuously with particle filter algorithm. The terrain measurements can be corrected in real-time with the equivalent SSP to improve the performance of underwater terrain navigation. By removing the impact of inaccurate SSP from the terrain measurements, the proposed method can achieve precise and robust underwater navigation result without using an underway-profiling instrument. Simulated results confirm the good performance of the proposed method. [ABSTRACT FROM AUTHOR]

The determination precision of temporary tidal gauge's chart datum is influenced by tidal nature similarity, datum transfer distance and topographical environment characteristics. This paper has studied the relationship between precision and influencing factors, and specific precision functional models in south and north branches of Yangtze River Estuary have been given respectively, through a large number of chart datum transfer tests with using tidal gauges' data in the Yangtze River estuary. The tests results indicate that the chart datum transfer precision in the south branch can be superior to 8 cm, when the difference of tidal type is less than 0. 05 and transfer distance is less than 80 kilometers. While, the precision is 20 cm in the north branch when the distance no less than 40 km. The research achievement has an important significance to the determination precision of chart datum transfer in the Yangtze River estuary. [ABSTRACT FROM AUTHOR]

Outliers in terrain data are an obstacle to achieving accurate and robust solutions of Underwater Terrain Relative Navigation (UTRN). If not handled properly, navigation may be degraded or even divergent. To address the problem, this paper proposes a terrain-matching algorithm based on the robust estimation theory. In contrast to the conventional approach, the proposed algorithm can significantly reduce the interference of the outliers. Experimental results confirm the good performance of the proposed method. [ABSTRACT FROM AUTHOR]

The discrimination of water–land waveforms is a critical step in the processing of airborne topobathy LiDAR data. Waveform features, such as the amplitudes of the infrared (IR) laser waveforms of airborne LiDAR, have been used in identifying water–land interfaces in coastal waters through waveform clustering. However, water–land discrimination using other IR waveform features, such as full width at half maximum, area, width, and combinations of different features, has not been evaluated and compared with other methods. Furthermore, false alarms often occur when water–land discrimination in coastal areas is conducted using IR laser waveforms because of environmental factors. This study provides an optimal feature for water–land discrimination using an IR laser by comparing the performance of different waveform features and proposes a dual-clustering method integrating K-means and density-based spatial clustering applications with noise algorithms to improve the accuracy of water–land discrimination through the clustering of waveform features and positions of IR laser spot centers. The proposed method is used for practical measurement with Optech Coastal Zone Mapping and Imaging LiDAR. Results show that waveform amplitude is the optimal feature for water–land discrimination using IR laser waveforms among the researched features. The proposed dual-clustering method can correct mislabeled water or land waveforms and reduce the number of mislabeled waveforms by 48% with respect to the number obtained through traditional K-means clustering. Water–land discrimination using IR waveform amplitude and the proposed dual-clustering method can reach an overall accuracy of 99.730%. The amplitudes of IR laser waveform and the proposed dual-clustering method are recommended for water–land discrimination in coastal and inland waters because of the high accuracy, resolution, and automation of the methods. [ABSTRACT FROM AUTHOR]

As widely applicated in many underwater research fields, conventional side-scan sonars require the sonar height to be at the seabed for geocoding seabed images. However, many interference factors, including compensation with unknown gains, suspended matters, etc., would bring difficulties in bottom detection. Existing methods need manual parameter setups or to use postprocessing methods, which limits automatic and real-time processing in complex situations. To solve this problem, a one-dimensional U-Net (1D-UNet) model for sea bottom detection of side-scan data and the bottom detection and tracking method based on 1D-UNet are proposed in this work. First, the basic theory of sonar bottom detection and the interference factors is introduced, which indicates that deep learning of the bottom is a feasible solution. Then, a 1D-UNet model for detecting the sea bottom position from the side-scan backscatter strength sequences is proposed, and the structure and implementation of this model are illustrated in detail. Finally, the bottom detection and tracking algorithms of a single ping and continuous pings are presented on the basis of the proposed model. The measured side-scan sonar data in Meizhou Bay and Bayuquan District were selected in the experiments to verify the model and methods. The 1D-UNet model was first trained and applied with the side-scan data in Meizhou Bay. The training and validation accuracies were 99.92% and 99.77%, respectively, and the sea bottom detection accuracy of the training survey line was 99.88%. The 1D-UNet model showed good robustness to the interference factors of bottom detection and fully real-time performance in comparison with other methods. Moreover, the trained 1D-UNet model is used to process the data in the Bayuquan District for proving model generality. The proposed 1D-UNet model for bottom detection has been proven effective for side-scan sonar data and also has great potentials in wider applications on other types of sonars. [ABSTRACT FROM AUTHOR]

To meet the needs of coastline efficient extraction and dynamic monitoring, this paper proposes a new method for coastline extraction by combining the tidal level and the digital elevation model (DEM) of the coastal zone from tilt photography. Firstly, the DEM of coastal zone was obtained by using unmanned aerial vehicle (UAV) tilt photography; at the same time, the accuracy of aerial triangulation(AT) is improved referencing to the constraint of water boundary points, and then the mean high water spring tide was obtained by combining tidal harmonic analysis and Global Navigation Satellite System (GNSS) tidal level. Finally, the coastline and the dynamic water-surface line are extracted from the DEM of the coastal zone by tracking the contour lines with the elevation of the mean high water springs (MHWS) and the instantaneous sea-surface elevation, respectively. The experiments carried out in the coastal zones of Liaoning Province, China, proved the proposed method and achieved better than 0.2 m of horizontal position accuracy and 0.1 m of the vertical accuracy. [ABSTRACT FROM AUTHOR]

Sea bottom line is the echo boundary between the water column and seabed on side-scan sonar waterfall image. Tracking it correctly is the basis for geometric and radiative correction of side-scan sonar image. Because of the interfering echoes under complex water environment, the sea bottom line cannot be tracked through traditional threshold and smoothing algorithms. Therefore, a new method, taking full advantage of the spatial distribution characteristics of the sea bottom line, is proposed, in which points density clustering and chains seeking are the two core processes. The former clustering process can be regarded as a filter to cluster points along a direction that is approximately parallel to the track line,and the points are grouped to chains if they are density reachable in a searching neighborhood. Then, by adopting the five principles and tracking strategy given in this paper, the chains relevant to the sea bottom line are sought and identified to finish the tracking work. The test results show that the proposed method performs well even in a complex water environment and has better stability and anti-interference ability compared with traditional tracking methods. This research improves the side-scan sonar data processing, and makes it more accurate, automatic and intelligent. [ABSTRACT FROM AUTHOR]

As one of the most commonly used acoustic systems in seabed surveys, the altitude of the side scan sonar from the seafloor is always difficult to determine, especially when raw signal levels and gain information are unavailable. The inaccurate sonar altitudes would limit the applications of sonar image geocoding, target detection, and sediment classification. The sonar altitude can be obtained by using bottom tracking methods, but traditional methods often require manual thresholds or complex post-processing procedures, which cannot ensure accurate and real-time bottom tracking. In this paper, a real-time bottom tracking method of side scan data is proposed based on a one-dimensional convolution neural network. First, according to the characteristics of side scan backscatter strength sequences, positive (bottom sequences) and negative (water column and seabed sequences) samples are extracted to establish the sample sets. Second, a one-dimensional convolution neural network is carefully designed and trained by using the sample set to recognize the bottom sequences. Third, a complete processing procedure of the real-time bottom tracking method is established by traversing each side scan ping data and recognizing the bottom sequences. The auxiliary methods for improving real-time performance and sample data augmentation are also explained in detail. The proposed method is implemented on the measured side scan data from the marine area in Meizhou Bay. The trained network model achieves a 100% recognition of the initial sample set as well as 100% bottom tracking accuracy of the training survey line. The average bottom tracking accuracy of the testing survey lines excluding missed pings reaches 99.2%. By comparison with multi-beam bathymetric data and the statistical analysis of real-time performance, the experimental results prove the validity and accuracy of the proposed real-time bottom tracking method. [ABSTRACT FROM AUTHOR]

Seafloor substrate mapping has become increasingly important to guide the management of marine ecosystems. Full coverage substrate maps, however, cannot easily be created from point samples (e.g. grabs, videos) as a result of the time required for collection and their discrete spatial extent. Instead, relationships between substrate types and surrogate variables as obtained from bathymetric or backscatter data can be modelled to build predictive substrate maps. As calculation of these surrogate variables is scale-dependent, the scale(s) of analysis need(s) to be selected first, with multiple scales likely required to adequately capture substrate characteristics. This paper proposes an objective and automatic self-adaptive analysis scale determination approach at each bathymetric point to extract terrain features (e.g. slope, aspect, etc). Object-based image analysis (OBIA) is also used to calculate additional texture features for segmented backscatter image objects. Random Forest classification is then used to model the relationship between these extracted features and substrate types interpreted from ground-truth video data, and full-coverage seafloor substrate maps are produced. The proposed method was applied on two datasets from Newfoundland, Canada, and demonstrated good performance in terms of both overall (>80%) and per-class accuracies. The proposed method is easily transferable to other study areas and provides an objective, repeatable means for classifying seafloor substrates for environmental protection and management of marine habitats. • An objective scale determination approach is proposed to derive terrain features. • Principal Components Analysis is used to extract principle seafloor information. • Entropy is calculated to reflect the seafloor orderliness. • Polynomial methods are used to extract terrain attributes. • The proposed method achieved good performance in two study areas. [ABSTRACT FROM AUTHOR]

Determining the altitude of side-scan sonar (SSS) above the seabed is critical to correct the geometric distortions in the sonar images. Usually, a technology named bottom tracking is applied to estimate the distance between the sonar and the seafloor. However, the traditional methods for bottom tracking often require pre-defined thresholds and complex optimization processes, which make it difficult to achieve ideal results in complex underwater environments without manual intervention. In this paper, a universal automatic bottom tracking method is proposed based on semantic segmentation. First, the waterfall images generated from SSS backscatter sequences are labeled as water column (WC) and seabed parts, then split into specific patches to build the training dataset. Second, a symmetrical information synthesis module (SISM) is designed and added to DeepLabv3+, which not only weakens the strong echoes in the WC area, but also gives the network the capability of considering the symmetry characteristic of bottom lines, and most importantly, the independent module can be easily combined with any other neural networks. Then, the integrated network is trained with the established dataset. Third, a coarse-to-fine segmentation strategy with the well-trained model is proposed to segment the SSS waterfall images quickly and accurately. Besides, a fast bottom line search algorithm is proposed to further reduce the time consumption of bottom tracking. Finally, the proposed method is validated by the data measured with several commonly used SSSs in various underwater environments. The results show that the proposed method can achieve the bottom tracking accuracy of 1.1 pixels of mean error and 1.26 pixels of standard deviation at the speed of 2128 ping/s, and is robust to interference factors. [ABSTRACT FROM AUTHOR]