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The conventional technique for positioning seafloor geophones in ocean bottom seismic exploration encounters several challenges, including the significant impact of outliers on positioning results, underutilization of high-precision observations, and low efficiency in real-time data processing. These issues inevitably affect the quality of seismic exploration outcomes. To address these challenges and enhance the accuracy of geophone positioning, this paper proposes a rigorous real-time acoustic positioning method for geophones based on sequential adjustment and Baarda's outlier detection approach. The proposed method comprises three key steps: grouping the original acoustic observations, constructing the intra-group acoustic positioning model, and synthesizing the positioning results across the different groups. The validity and practicality of this approach are confirmed through a simulation experiment as well as the field experiment conducted in the Bohai Sea, China. The results demonstrate that the proposed method effectively eliminates outliers in the original observations and maximizes the utilization of high-quality observations. Compared to traditional acoustic positioning methods, it significantly reduces positioning errors from meters to decimeters, and in some cases can achieve centimeter-level precision. When the sound velocity profile in the operating sea area is measured, the method can attain the posterior standard deviation at the millimeter level and positioning errors within 10 cm. When the sound velocity profile is unknown, the method can achieve the posterior standard deviation at centimeter-level and positioning errors of approximately 20 cm. [ABSTRACT FROM AUTHOR]

It has been proven that the quality factor (Q) is important for marine sediment attenuation attribute representation and is helpful for sediment classification. However, the traditional spectral-ratio (SR) method is affected by the interference effect caused by thin interbeds, which seriously degrade the performance of the SR method. Aimed at this problem, a novel method based on variational mode decomposition (VMD) correlation analysis is presented in this paper, which realizes the separation between interference reflections and effective signals. After obtaining the effective signals, a frequency band selection method is employed to weaken the influence of background noise. To better apply the proposed method to large-area sediment classification, a sediment clustering method based on texture features is introduced. Experiments on real data validate the effectiveness of the proposed method. The accuracy of the correlation analysis method using the modified parameters is 94 percent. The stability improvement in the standard deviation of the Q calculation can reach more than 90 percent. Moreover, the interpretation of sediment categories using the mean value of Q fits the drilling data well. It is believed that the proposed method has huge potential for the engineering applications in sub-bottom sediment classification. [ABSTRACT FROM AUTHOR]

In light of the prevailing approach in which data from side-scan sonar (SSS) from Autonomous Underwater Vehicles (AUVs) are primarily processed and visualized post mission, failing to meet the requirements in terms of timeliness for on-the-fly image acquisition, this paper introduces a novel method for real-time processing and superior imaging of navigation strip data from SSS aboard AUVs. Initially, a comprehensive description of the real-time processing sequence is provided, encompassing the integration of multi-source navigation data using Kalman filtering, and high-pass filtering of attitude and heading data to exclude anomalies, as well as the use of bidirectional filtering techniques within and between pings, ensuring real-time quality control of raw data. In addition, this study adopts the semantic segmentation Unet network for automatic real-time tracking of seafloor lines, devises a real-time correction strategy for radial distortion based on historical echo data, and utilizes the alternating direction multiplier method for real-time noise reduction in strip images. With the combined application of these four pivotal techniques, we adeptly address the primary challenges in real-time navigation data processing. In conclusion, marine tests conducted in Bohai Bay substantiate the efficacy of the methodologies delineated in this research, offering a fresh paradigm for real-time processing and superior visualization of SSS navigation strip data on AUVs. [ABSTRACT FROM AUTHOR]

The limited number and under-representation of side-scan sonar samples hinders the training of high-performance underwater object detection models. To address this issue, in this paper, we propose a diffusion model-based method to augment side-scan sonar image samples. First, the side-scan sonar image is transformed into Gaussian distributed random noise based on its a priori discriminant. Then, the Gaussian noise is modified step by step in the inverse process to reconstruct a new sample with the same distribution as the a priori data. To improve the sample generation speed, an accelerated encoder is introduced to reduce the model sampling time. Experiments show that our method can generate a large number of representative side-scan sonar images. The generated side-scan sonar shipwreck images are used to train an underwater shipwreck object detection model, which achieves a detection accuracy of 91.5% on a real side-scan sonar dataset. This exceeds the detection accuracy of real side-scan sonar data and validates the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

The limitations of underwater acoustic communications mean that the side-scan sonar data of an autonomous underwater vehicle (AUV) cannot be transmitted back and processed in real time, which means that targets cannot be detected in real time. To address the problem, this paper proposes an autonomous underwater vehicle-based side-scan sonar real-time detection method for underwater targets. First, the paper describes the system and operation of real-time underwater-target detection by the side-scan sonar mounted on the autonomous underwater vehicle. Next, it proposes a real-time processing method for side-scan sonar data, method for constructing a deep-learning-based underwater-target detection model, and real-time method for underwater-target detection based on navigation strip images, which, together, solve the three key technical problems of real-time data processing, deep-learning-based detection model construction, and real-time target detection based on the autonomous underwater vehicle. Finally, through sea-based experiments, the effectiveness of the proposed methods is evaluated, providing a new solution for the autonomous underwater vehicle-based side-scan sonar real-time detection of underwater targets. [ABSTRACT FROM AUTHOR]

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]

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]

Conventional sediment classification methods based on Multibeam Echo System (MBES) data have low accuracy since the correlation between features and sediment has not been fully considered. Moreover, their poor resistance to the residual error of MBES backscatter strength (BS) processing also degrades their performances. Toward these problems, we propose a seabed sediment classification method using spatial statistical features extracted from angular response curve (ARC), topography, and geomorphology. First, to reduce interference of noise and residual error of beam pattern correction, we propose a robust method combining the Generic Seafloor Acoustic Backscatter (GSAB) model and Huber loss function to estimate the parameters of ARC which is strongly correlated with seabed sediments. Second, a feature set is constructed by AR features composed of GSAB parameters, BS mosaic and its derivatives, and seabed topography and its derivatives to characterize seabed sediments. After that, feature selection and probability map acquisition are employed based on the random forest algorithm (RF). Finally, a denoising and final sediment map generation method is proposed and applied to probability maps to obtain the sediment map with reasonable sediment distribution and clear boundaries between classes. We implement experiments and achieve the classification accuracy of 93.3%, which verifies the validity of our method. [ABSTRACT FROM AUTHOR]

Sub-bottom profiler (SBP) measurements play a very important role in marine pipeline detection and checking. However, sometimes because of bad sea conditions and complex underwater environments, only poor SBP images can be obtained. Pipeline recognition based on a poor SBP image manually is tedious and has low accuracy. In this article, based on the specific hyperbola shapes caused by pipelines on SBP images, a novel method to detect the marine pipelines automatically and robustly is presented. Two main steps are used. First, a comprehensive edge extraction method is proposed, which includes line-like structure enhancement and preliminary edge extraction, local phase calculation and phase edge extraction, final edge extraction, as well as edge processing. By applying this method, interference such as reverberation can be eliminated, and fine hyperbola edges can be obtained. Second, by referencing the theoretical hyperbola and using a buffer zone and a hyperbola contour consistency (HCC) criterion, the location of the pipeline can be determined robustly. This method was implemented and verified using data collected under real conditions. The results show that the proposed method is practicable. [ABSTRACT FROM AUTHOR]

When exposed to corrosive environments, metals, which are relatively common industrial materials, undergo serious corrosion, leading to significant economic losses. Therefore, studying the corrosion rate of metals in their early stages holds great pedagogical significance. This study employs a standard specimen of 20# steel as an experimental object, designing the experimental teaching of metal corrosion rate under varying temperatures, PH levels, and hydrated salt materials. The weightlessness method determines the overall corrosion rate of the metal, while the electrochemical method determines the localized corrosion rate of the metal. When the experimental temperature is 80℃ and 100℃, the corrosion rate of 20# steel is the largest; both are 0.00800g·m−2·d−1, and the charge transfer resistance increases gradually when the temperature is −20℃~5℃, and decreases gradually when the temperature is 5℃~100℃. The corrosion rate of the metal did not change much at pH=7 and 8, and the corrosion rate reached 0.688 mm/a, and the impedance curve polarization resistance Rp was the smallest when pH=5, and its corrosion resistance was poor under acidic conditions. The average corrosion rates in MgSO4·7H2O, and CaCl2·6H2O and Ba(OH)2·8H2O were 0.0030g/m−2h−1, 0.0018g/m−2h−1, and 0.0050 g/m−2h−1, respectively. Teaching experiments were used to carry out the present study. The study aims to let students experience the process of metal corrosion through teaching experiments and better help them solve cognitive difficulties in metal corrosion.

Currently, the pollution and clogging problems of injection wells in low-permeability reservoirs are becoming more and more serious, leading to a series of issues, such as the decline of fluid production in wells and the increase of pressure in injection wells. To cope with these challenges, SY organic polyacid solution was developed in this paper and its dissolution efficiency was thoroughly investigated. The experimental results show that SY organic polyacid solution has a high dissolution rate of 88.7%, which exhibits excellent scale dissolution effect, and the solvent's dissolution has a good slow-release performance to ensure the long-lasting action time. At the same time, the corrosion rate of the solvent on the N80 steel sheet is only 0.6291 g/m²-h, much lower than the industry standard, which effectively protects the safety of the construction pipeline and production column. In addition, the dissolution rate of SY organic polyacid on clay reaches 10.14% in five minutes, indicating that it prevents the ground collapse caused by excessive dissolution of clay and has good profound penetration ability. In summary, using SY organic polyacid solution can effectively solve multiple problems such as reservoir mud, inorganic scale, organic scale and water injection fouling oil, which provides an effective solution for the contamination and clogging of water injection wells in low permeability reservoirs.

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]

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]

Manual horizon picking and semiautomatic horizon picking are the traditional methods for data processing of subbottom profiles (SBPs). However, the former is time consuming and laborsome, whereas the latter requires frequent manual intervention and easily suffers from low accuracy and discontinuous picking due to noises. A comprehensive and automatic horizon picking method is proposed by combining the given gray mutation, horizon tracking and filtering, and horizon growth algorithms in this paper. Based on the peaks and valleys corresponding to the strong and weak impedance contrasts in an echo sequence, the gray mutation is used to prepick horizons from a SBP image. According to sediment layer continuity and horizontal resolution, the horizon tracking and filtering are applied to construct discrete horizon segments and remove outliers. In consideration of the correlation of adjacent ping sequences and the horizon orientation, the horizon growth based on the horizon orientation constraints is utilized to connect the discrete horizon segments. Experiments validated the proposed method. The automatic horizon picking results were compared with the bore-hole data and the manual horizon picking results as well, and good consistencies were achieved. The determinations of parameters and the performance of the proposed method are discussed based on the theoretical study and experiments, and some conclusions are drawn. [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 estimations of water column depth and the towfish altitude and the measurements of the target's location and shape all depend on the accuracy and reliability of bottom tracking of a sidescan sonar (SSS) waterfall image. Traditionally, the threshold method has often been adopted, but it is difficult to achieve an ideal bottom-tracking result due to contamination of the water column image in complicated measuring environments such as suspended solids and strong absorption sediments. Besides, it is also time consuming and easy to be influenced by operators' experience because the threshold values used need to be set segmentally. To track the bottom accurately and efficiently, first, this paper studies the threshold method, the last peak method, the assumption of seabed continuous variation, and the symmetry assumption. Then, a comprehensive bottom-tracking method is proposed. To validate the proposed method, an SSS experiment was conducted in the Bohai Sea of China. The bottom tracking for the SSS images was carried out automatically by the developed software. Comparing the port and starboard towfish altitudes tracked for all SSS images, the proposed method achieves $\pm$ 0.19 m of the standard deviation referenced to water depth at the points of measurement. Furthermore, taking the integration of the sounding data, the tidal level, and the towfish depth as reference, the proposed method obtains $\pm$0.17 m of the standard deviation. These parameters show that the proposed method has high accuracy in bottom tracking. Finally, this method is further discussed in the applications, and the results show that it has better performance than the traditional threshold method where the SSS waterfall image is influenced under complicated measurement environment. A shortcoming induced by the SSS imaging mechanism is also found that this method may be invalid when high seabed targets lie at two sides of towfish nadir, which can be solved by increasing the towfish altitude. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are about 22 nucleotides regulatory non-coding RNAs that play versatile roles in reprogramming plant responses to biotic and abiotic stresses. However, it remains unknown whether miRNAs confer the resistance to necrotrophic fungus Rhizoctonia solani in rice. To investigate whether miRNAs regulate the resistance to R. solani, we constructed 12 small RNA libraries from susceptible and resistant rice cultivars treated with water/pathogen at 5 h post inoculation (hpi), 10 hpi and 20 hpi, respectively. By taking the advantage of next-generation sequencing, we totally collected 400–450 known miRNAs and 450–620 novel miRNAs from the libraries. Expression analysis of miRNAs demonstrated different patterns for known and novel miRNAs upon R. solani challenge. Thirty-four miRNA families were identified to be expressed specifically in rice, and most of them were involved in plant disease resistance. A particular Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis result revealed that a great majority of target genes of regulated miRNAs belonged to the pathway of plant-pathogen interaction. Moreover, miR444b.2, miR531a, mir1861i, novel_miR1956 and novel_miR135 conferred response to R. solani infection confirmed by Northern blot. Our global understanding of miRNA profiling revealed that the regulation of miRNAs may be implicated in the control of rice immunity to R. solani. Analysis of the expression of miRNAs will offer the community with a direction to generate appropriate strategies for controlling rice sheath blight disease. Keywords: rice, microRNA, sheath blight disease, resistance, transcriptomics

In practical application, there are a large amount of imbalanced data containing only a small number of labeled data. In order to improve the classification performance of this kind of problem, this paper proposes a semi-supervised learning algorithm based on mixed sampling for imbalanced data classification (S2MAID), which combines semi-supervised learning, over sampling, under sampling and ensemble learning. Firstly, a kind of under sampling algorithm UD-density is provided to select samples with high information content from majority class set for semi-supervised learning. Secondly, a safe supervised-learning method is used to mark unlabeled sample and expand the labeled sample. Thirdly, a kind of over sampling algorithm SMOTE-density is provided to make the imbalanced data set become balance set. Fourthly, an ensemble technology is used to generate a strong classifier. Finally, the experiment is carried out on imbalanced data with containing only a few labeled samples, and semi-supervised learning process is simulated. The proposed S2MAID is verified and the experimental result shows that the proposed S2MAID has a better classification performance.

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]

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]

[Objectives] Integrated power systems represent the current direction in ship power development,the rectifying device is an important part of the system, and its impact resistance will direcily affect its operation performance.[Methods] The finite element model of a certain electrical cabinet with nonlinear vibration isolation parameters is established,and the impact responses of the cabinet under elastic and rigid installation conditions are contrasted. A type of rectifier is specially designed for the rigid installation and a hydraulic double wave shock machine is used to carry out the impact test in an electrified state.[Results] The simulation results show that the maximum stress response of the equipment under rigid installation is close to 355 MPa;slight plastic deformation may occur but not cause damage,while the same part of the elastic installation far exceeds the stress yield limit. The hydraulic double-wave impact test shows that the limit load of the air switch is less than 3.2 m/s,and the other components can work normally when the impact spectrum velocity is 4.8 m/s.[Conclusions] The findings in this paper show that the electrical and electronic components possess high impact resistance,and welded or screwed components do not become detached, while the weak links under impact conditions are generally buckle-type fixed and air switches with mechanical action components.

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 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]

GaoFen-3 (GF-3) is the first commercial C-Band multi-polarimetric Synthetic Aperture Radar (SAR) satellite that was launched by China. The characteristics observed by both all-day and all-weather observation depict significant advantages of national sea area use dynamic monitoring. We have thoroughly discussed both the imaging mode and the standard preprocessing of GF-3 imagery by analyzing national sea area use dynamic monitoring. We have portrayed reclamation and aquaculture as significant examples of dynamic monitoring. We have presented both identification and classification results using various image modes of GF-3 satellite, compared with the existing approaches. Finally, we have elaborated on the scope for future research.

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 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]

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]

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]

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]

A damage localization approach based on modal strain energy index and evidence theory is proposed in this paper. First, the modal strain energy change ratio (MSECR) method and modal strain energy dissipation ratio (MSEDR) method are used to preliminarily detect the damage locations, respectively. Then, the evidence fusion technique is applied to integrate these results and determine the damage locations of structures. To investigate the effectiveness of the proposed method, a numerical example is considered. The numerical results show that the proposed method achieves a better performance in determining the damage locations of structures.