Direction-of-arrival (DOA) measurement for underwater wideband signals using a linear array is a critical task in oceanic instrumentation, with accuracy directly impacting applications like target tracking and navigation. However, current measurement methods often exhibit significant performance degradation in low signal-to-noise ratio (SNR) scenes. To address these measurement challenges, this article proposes a novel deep fusion measurement framework that enhances measurement accuracy and comprehensively quantifies its measurement uncertainty, which introduces a dedicated input feature construction strategy to better preserve measurement information of the wideband signals, along with a physics-informed loss term designed to ensure physical consistency of the measurement model. The framework employs a hybrid deep learning architecture that captures intrinsic angle-amplitude relationships and long-range dependencies across frequency bands through a cross-modal channel attention (CMCA) mechanism and a frequency Mamba (Fmamba) module. Functioning as a regression-based measurement system, the proposed approach can estimate an arbitrary number of targets without predefining the source count, thereby overcoming the grid precision limitation of classification-based methods. Additionally, uncertainty quantification via Monte Carlo dropout is integrated to evaluate how parametric uncertainty affects the model's predictions. Extensive simulation and lake trial results demonstrate that the proposed method achieves state-of-the-art measurement performance in terms of average precision (AP), mean absolute error (MAE), and root-mean-square error (RMSE), while the uncertainty analysis also confirms its value as a robust and self-aware solution for underwater acoustic measurement.

The acoustic reflection coefficient of a layered seabed exhibits an oscillatory structure that varies with frequency and grazing angle. These oscillatory characteristics are linked to the seabed's stratification and its geoacoustic parameters. Through numerical simulation, the influence of seabed layering and geoacoustic parameters on the frequency-angle oscillatory structure of the bottom reflection coefficient (BRC) is investigated. Based on this, a deep learning geoacoustic inversion method is introduced for retrieving near-field seabed layering and its geoacoustic parameters from the frequency- and angle-dependent bottom reflection coefficient. A deep neural network model based on self-attention and cross-attention mechanism, Cross-ViT, is employed to learn features from the two-dimensional BRC matrix. The model is trained to perform the inversion of geoacoustic parameters using a multi-task learning strategy with gradient normalization (GradNorm). Simulation results indicate that, compared to convolutional neural network and transformer models, the presented model more effectively learns the mapping between seabed reflection characteristics and multiple geoacoustic parameters and possesses relatively strong noise robustness. The method's effectiveness is validated using near-field acoustic data from an acoustic inversion experiment conducted on the northern continental shelf of the South China Sea in 2022.

Extensive dam development and climate change have altered seasonal stage variation patterns, which are critical to residents in the riparian zone along the Lancang–Mekong River. However, the effects of morphological changes and reservoirs on stage variability are not clear due to data-scarce fluvial systems. In this work, discharge and water level data from five hydrological stations (1960–2020) were acquired to assess temporal shifts in rating curves during different disturbance periods. The contribution of channel geometry adjustment to the stage variance of the extreme flow regime under high- and low-flow conditions were empirical analysed by rating-curve method. Analysis revealed that the stage variance along the main channel was modulated by channel geometry adjustment under both high- and low-flow conditions, even though discharge was the dominant factor. Moreover, the degree of modulation resulting from geometric adjustment varied under different flow and reach conditions, which varied in the ranges of -0.57 ~ 0.27 m and − 0.41 ~ 0.39 m under low- and high-flow conditions, respectively. Furthermore, an inverse channel geometry adjustment response was observed for 60% of the high-flow conditions versus 40% of the low-flow conditions. The Luang Prabang–Vientiane reach was transitional in terms of the effects of channel geometry adjustment on stage variation. Our findings quantified how channel geometry adjustment modulated water levels across various extreme regimes, offering insights into the morphological processes of data-scarce river reaches.

High-responsivity hydrophones play a crucial role in monitoring ocean acoustic fields, detecting underwater targets, and conducting underwater acoustics research. To address the challenges of long-term, high-responsivity vector acoustic detection for low-frequency signals in deep-ocean environments, we have developed and evaluated an underwater acoustic subsurface buoy system capable of operating at depths of up to 2000 m. This system utilizes fiber-optic acoustic vector sensors to capture vector information of particle acceleration and acoustic pressure in the surrounding field. It offers excellent directionality and can autonomously function for six months with reliability. After undergoing various tests including laboratory tests, hydrostatic pressure tests, and pool tests, the system underwent a 9-day trial in the northern South China Sea. During the trial, ambient noise, anthropogenic noise, bioacoustics, and microseisms induced by tropical cyclone Ma-on were investigated. The system demonstrated the ability to detect signals from explosive sources up to 100 km away and pick up microseisms at frequencies as low as sub-0.1 Hz. These trials showcased the system's capability to operate effectively in deep-ocean environments for over six-month deployments and acquire high-quality target signals, with potential applications in seismology, acoustics, biology, and oceanography research.

Generally, most inversion approaches model the seabed as a stack of range-independent homogeneous layers with unknown geoacoustic parameters and layer numbers. In our previous study, we established a layered geoacoustic seabed model based on sub-bottom profiler data to characterize low-frequency (100–500 Hz) airgun signal propagation at short ranges (0–20 km). However, when applying the same model to simulate high-frequency (500–1000 Hz) explosive sound signal propagation, it failed to adequately reproduce the observed significant transmission loss phenomenon. Through systematic analysis of transmission loss (including water column sound speed profiles, seabed topography, and sediment properties), this study proposes a range-dependent layered geoacoustic model using the Range-dependent Acoustic Model–Parabolic Equation (RAM-PE). Stepwise inversion implementation has successfully explained the observed experimental phenomena. To generalize the proposed model, this study further introduces a trans-dimensional inversion framework that automatically resolves sediment property interfaces along propagation paths. The method effectively combines prior information with trans-dimensional inversion techniques, providing improved characterization of range-dependent seabed environments.

As the threat of unstable braided river geomorphology to the resilience of local communities grows, a better understanding of the morphological changes in a river subject to climate is essential. However, little research has focused on the long-term planform change of the braided reaches and its response to hydrological changes. The reach around Majuli Island (Majuli Reach), the first and typical braided reach of the Brahmaputra River emerging from the gorge, experiences intense geomorphological change of the channels and loss of riparian area every year due to the seasonal hydrological variability. Therefore, focusing on the Majuli Reach, we quantitatively investigate changes in its planform morphology from 1990 to 2020 using remote sensing images from the Landsat dataset and analyze the influence of discharge in previous years on channel braiding. The study shows that the Majuli Reach is characterized by a high braiding degree with an average Modified Plan Form Index (MPFI) of 4.39, an average reach width of 5.58 km, and the development of densely migrating bars and active braided channels. Analysis shows a control point near Borboka Pathar with little morphological change, and the braided channel shows contrasting morphological changes in the braiding degree, bars, and main channel between the reach upstream and downstream of it. The area of the riparian zone of the Majuli Reach decreased by more than 50 km2 during the study period due to migration of the main channel toward the island. The braiding degree of Majuli Reach is positively correlated with the discharge in previous years, with the delayed response time of the MPFI to discharge being just 3–4 years, indicating the unstable feature of the Majuli Reach with varied hydrology conditions.

Sound propagation in shallow water is significantly influenced by geoacoustic properties.Estimating these geoacoustic parameters is essential for sound field analysis and sonar performance assessment.As a common practice,the seafloor is often treated as a single-layer or two-layer range-independent geoacoustic model to reduce the number of involved parameters.However,acoustic parameters inverted through these two geoacoustic models are typically limited in their applicability to a specific frequency range,thus posing challenges when applied across a broader frequency range.A range-dependent multi-layer geoacoustic model based on experimental measurements obtained with a sub-bottom profiler is proposed in this study.The inversion scheme combines three inversion methods to estimate geoacoustic parameters,considering the different sensitivities of geoacoustic parameters to different physical parameters within the acoustic field.Firstly,modal dispersion is used to invert the geoacoustic parameters of each layer,with the dispersion curve obtained through warping transform and the Wigner-Ville distribution.After that,both the localization using matched field processing and the dispersion curve fitting demonstrate the effectiveness of the inversion results for each layer,although the peak of the probability distribution of sound speed in the first layer is broader than in others.Secondly,matched field processing is employed to invert the geoacoustic parameters of the first layer.This method is based on the theory that as frequency increases,the depth of sound rays penetrating the seabed decreases,revealing changes in the first layer's sound speed with the seabed depth.Lastly,bottom attenuation coefficients at different frequencies are inverted by the transmission loss(TL),and a fitting relationship between the attenuation coefficient and the frequency is derived.The inversion results obtained by using the range-dependent multi-layer geoacoustic model are compared with results estimated by the single-layer geoacoustic model.The findings indicate that the transmission loss(TL)error from the range-dependent multi-layer geoacoustic model in this study is smaller than that from the single-layer geoacoustic model,especially in the lower frequency band.The range-dependent multi-layer geoacoustic model proves to be suitable for a broader frequency range,providing better precision in explaining various acoustic phenomena.

The intensity characteristics of deep-sea ambient noise are important parameters for the evaluation of the operating distance and signal-to-noise ratio of underwater equipment. This study conducted research on the prediction method of ambient noise intensity based on long short-term memory network. The prediction was conducted on measured deep-sea data in two scenarios, medium-long time scale and short time scale. The results show that under medium-long-time scale conditions, the temporal trend of predicted noise intensity is consistent with the real trend, but in some frequency bands, considerable errors and time delays exist. The mean of root mean square error (RMS) in the frequency band of 20 Hz-5 kHz is 4.31 dB. Under short time scale condition, the average RMS between the prediction and measured is 0.73 dB within the same frequency range. At the same time, the correlation coefficient between the predicted noise intensity curve with frequency and the true value curve is 0.96.

A vertical line array (VLA) deployed at the seabed bottom captures the arrival angle interference structure in the frequency-beam domain resulting from the Direct (D) and Surface-Reflected (SR) arrivals of a broadband source. This interference structure, sensitive to the source's depth, serves as a basis for depth estimation. In order to address limitations related to bandwidth and nonlinear errors stemming from environmental differences, and to enhance the applicability of interference structures, a hybrid source localization method based on deep learning is proposed. This method employs an optimized residual network (ORN) to effectively extract and evaluate features from the frequency-beam domain sound intensity matrix. Simulated and experimental datasets are used to test the performance of the proposed method, and results suggest that the performance of the ORN model is much better than those of multi-Fourier transform approach (MSTDE), matching field processing (MFP) and traditional Convolutional Neural Network (CNN) models.

AbstractThe low frequency line spectrum noise radiated by ships has strong stability and is difficult to eliminate, which is the key information required for passive signal detection. A vector coherent frequency‐domain batch adaptive line enhancement method is proposed to address the issue of insufficient detection capability of traditional scalar adaptive line enhancement (ALE) algorithms for ship characteristic line spectra in complex deep‐sea environments. This method not only introduces the idea of frequency‐domain batch processing, but also uses synchronously collected sound pressure and particle velocity as dual input, fully utilising the coherence characteristics between vector channels to output high gain line spectrum signals and improve computational efficiency. In simulation and sea trial data validation, compared with the time‐domain vector coherent adaptive line enhancement algorithm, this method has shorter time consumption, higher efficiency, and can improve the detection ability of line spectrum signals under low signal‐to‐noise ratio conditions. The bearing estimation results output by this algorithm is also more accurate.

When employing the conventional beamforming (CBF) for the estimation of the direction of arrival of the Direct rays, one can observe a corresponding relationship between the arrival angle and the source distance, which can be used for range estimation. In the actual deep ocean environment, the arrival angle matched location method performs effectively in solving range estimation problems, although its performance is susceptible to the signal-to-noise ratio (SNR). To enhance the environmental adaptability and expand the application range of the source ranging method using the arrival structures in the beam do-main received by a vertical line array (VLA), we introduce a deep transfer learning (DTL) based source ranging method. Initially, a pre-trained model is established using simulation data generated under various SNRs through an ocean ambient noise model. Then high SNR experimental data is employed for DTL of the pre-trained model to fine tune the parameters. Finally, the experimental datasets are used to test the performance of the proposed method, and results suggest that the performance of the deep transferred model is much better than those of the traditional arrival angle matched location method and the model trained on noise-free data.

Hydrological conditions are strongly regulated by monsoon systems in the Mekong River Basin (MRB), while relevant studies investigating the intensity of the rainy season are still insufficient. This study employed the Mann-Kendall (M-K) test, Sen’s slope estimator, and innovation trend analysis to detect the variation of summer discharge in the MRB. Wavelet analysis is used to investigate the correlation between discharge and two South Asian summer monsoon subsystems (SAMI1 and SAMI2). Results show that the summer discharge in the MRB generally shows significant downward trends during 1970–2016 with a Z value range of −3.59–−1.63, while the high discharge at Vientiane, Mukdahan, and Pakse increases after 1970. The mutation years of the summer discharge series are around 2010 for Chiang Sean and Vientiane, and in 2015 for Luang Prabang, which resulted from the newly built large dams, Xiaowan and Nuozhadu. The wavelet analysis shows that the SAMI1 can be used to predict the summer discharge at Chiang Sean at a ~8-year timescale, while the SAMI2 correlates with the summer discharge well at a 1–8-year scale, especially at Mukdahan and Kratie during 1980–2016.

The propagation mode of reliable acoustic path (RAP) in deep water is widely used in the field of target detection. A receiver set at the bottom of the seabed can receive acoustic signals from RAP. The direct (D) wave and the surface-reflected (SR) wave from a broadband source form an undulating interference structure in the frequency domain. This feature is sensitive to target depth and is thus used for depth esti-mation. Traditionally, depth estimation using the interference pattern, the scalar intensity in the fre-quency domain is processed by the optimal Fourier transform to extract the line spectrum in the depth domain. Based on this method, we proposed a passive depth estimation method for underwater broadband sources in deep water based on the orthogonal matching pursuit (OMP) algorithm. The OMP-based method is applied to scalar vertical line array (VLA) and single vector sensor (SVS) data. The OMP-based method largely eliminates side lobe interference, reduces the width of the main lobe, and improves depth estimation resolution while ensuring the stability and reliability of the results. Finally, the experimental data of VLA and SVS in the South China Sea are processed to validate the pro-posed method. (c) 2023 Elsevier Ltd. All rights reserved.

For a narrowband signal, an oscillating interference pattern is formed with a target's moving when receiving at the bottom of the sea. In this Letter, the interference pattern of a narrowband source is observed using a single vector sensor (SVS). A passive depth estimation method employing a SVS is proposed. This approach processes the signals after the adaptive line enhancing and extracts the vector intensity, which oscillates periodically with the vertical azimuth. The passive estimation is achieved based on the Fourier-transform relationship between the depth and interference period. The simulation and sea experiment verify this method.

The relationship between modal elevation angle and the relative arrival time between modes, derived from exploiting modal dispersion, provides source information that is less susceptible to environmental influences. However, the standard method based on modal dispersion has limitations for application. To overcome this, we propose a hybrid method for passive source ranging of low-frequency underwater acoustic-pulse signals in a range-independent shallow-water waveguide. Our method leverages deep learning, utilizing the intermediate results from the standard method as inputs, and short-time conventional beamforming to transform signals received by a vertical line array into a beam-time-domain sound-intensity map. The source range is estimated using an attention-based regression model with a ResNet backbone that has been trained on the beam-time-domain sound-intensity map. Our experimental results demonstrate the superiority of the proposed method, with a mean relative-error reduction of 71%, mean root-squared error reduction of 2.25 km, and an accuracy of 85%, compared to matched-field processing.

To improve the inversion efficiency,a method for quickly and directly estimating the sound speed of the surface layer of the seabed is proposed.According to the energy flux theory,the ocean ambient noise data received by the vertical line array can be used to extract the bottom loss (BL) passively.The BL curve has an effect on the critical angle,which is used to estimate the sound speed of the surface layer of the seabed.Based on the ray model,the difference between the BL calculated from the noise extraction and the theoretical value is derived,while the beamforming performances of the array at different angles and frequencies is discussed.Considering that the sound ray bends in the environment with a non-constant sound-speed gradient,the angle needs to be corrected to improve the universality of the method.Different critical angles of different frequencies correspond to different effective depths.Data processing result of an experiment conducted in the Yellow Sea shows that within the effective depth where the critical angle remains unchanged,the surface layer of the seabed can be re-garded as a constant speed layer.The sound speed of the surface layer of the seabed in this sea area is estimated at 1547 m/s within 0.5 m,which is similar to the active inversion result.

In the deep ocean, a vector sensor is deployed near the seabed to receive direct waves from the reliable acoustic path (RAP). In the space domain, the vector sensor can estimate the vertical arrival azimuth of the signal, which is sensitive to distance. In the frequency domain, the periodic interference pattern formed by the direct and surface-reflected wave is decided by both the source depth and the vertical arrival azimuth. In this work, a multi-step method for passive broadband source localisation using a single-vector sensor (SVS) is proposed. At first, the depth is estimated by the interference pattern in the frequency domain. Then the ranging problem is solved based on the depth estimated result. The impact of the surface-generated noise is considered. Compared with the scalar vertical line array (VLA) in simulation, the localisation performance of SVS has higher requirements for the signal-to-noise ratio (SNR) but is much smaller in size. In the experiment, Gaussian white noise from 310 to 430 Hz was emitted to simulate the underwater target. Compared with traditional match-field processing (MFP), the multi-step method is much more stable, accurate, and efficient. The computing time is drastically shortened.

Jing River is a tributary of the Wei River which is the largest tributary of the Yellow River. Sediments eroded from the upland of the Jing River basin are one of the major contributors of sediment entering the lower Wei River (LWR). The Dongzhuang reservoir is designed to be constructed on the lower Jing River for flood control and water resources regulation, and this may change the sustainable management of the LWR as changed channel deposition by trapping sediments and releasing concentration-limited flow. Its effects on the LWR, especially the deposition distribution, should be analyzed. The steady quasi-two-dimensional dynamic model was adopted to estimate the deposition processes in the LWR. Then, the qualitative effects of the Dongzhuang reservoir on channel deposition were evaluated and compared with historical data, including capacity loss in other reservoirs and measured deposition in the LWR. Analyses indicated that the annual deposition in the LWR will decrease by approximately two-thirds due to the reservoir's operation. After 15 years of operation, the effects of the Dongzhuang reservoir on the lower channel will decrease gradually. Moreover, its effects on lateral distribution in different sub-reaches are different. After the reservoir's operation, the floodplain of the Xianyang-Lintong (XY-LT) sub-reach will change its sediment regime from deposition to erosion. For the Lintong-Huaxian (LT-HX) sub-reach, deposition in the main channel will be more serious during the first 30 years of operation. For the Huaxian-Tongguan (HX-TG) sub-reach, the reservoir will have almost no effects on the lateral distribution. All these analyses may benefit the sustainable management of the Wei River and the Yellow River.

Bedload grains in consecutive meandering bends either move longitudinally or across the channel centerline. This study traces and quantifies the grains’ movement in two laboratorial sine-generated channels, i.e., one with deflection angle θ0 = 30° and the other 110°. The grains originally paved along the channels are uniform in size with D = 1 mm and are dyed in various colors, according to their initial location. The experiments recorded the changes in the flow patterns, bed deformation, and the gain-loss distribution of the colored grains in the pool-bar complexes. We observed the formation of two types of erosion zones during the process of the bed deformation, i.e., Zone 1 in the foreside of the point bars and Zone 2 near the concave bank downstream of the bend apexes. Most grains eroded from Zone 1 are observed moving longitudinally as opposed to crossing the channel centerline. Contrastingly, the dominant moving direction of the grains eroded from Zone 2 changes from the longitudinal direction to the transversal one as the bed topography evolves. Besides, most building material of the point bars comes from the upstream bends, although low- and highly curved channels behave differently.

The flood travel time (FTT) along the Longmen-Tongguan Reach, part of the stem channel of the Middle Yellow River, is shorter than 30 h, and estimating the FTT of different discharges propagating from Wubu Hydrology Station to Tongguan Hydrology Station is necessary. However, the propagation of floods in this river network, the main channel of the Wubu-Tongguan Reach and related tributaries, has rarely been analyzed due to the lack of geometry data. Thus, a one-dimensional (1D) dynamic model was selected to simulate the FTT along the WT reach. Firstly, the 1986 flood event was selected to calibrate the physical parameters in the hydraulic model. Secondly, the FTT with different discharges (500–9000 m3/s) were estimated with calibrated parameters. Thirdly, an empirical formula based on simulated results was fitted. This empirical formula could be used to describe the relation between discharges, distances to Tongguan Hydrology Station, and the FTT. Analyses showed that the discharges with minimum FTT were different for different tributaries. For the river reach between Wubu Hydrology Station and the Wuding River, the discharge and corresponding minimum FTT were 6000 m3/s and approximately 30.4–34 h, respectively. For the river reach between the Zhouchuan and Qingjian Rivers, the discharge and FTT were 3000–3500 m3/s and 21–26.8 h, respectively. The formula can be used to estimate the FTT of flood events, which would be cost-saving and time-saving for river management. Sensitivity analyses indicated that the FTT were sensitive to the Tongguan elevation and Manning’s roughness coefficient in the main channel.