Search Results (23)

Aiming at the problem of accuracy degradation caused by the mixing of spatiotemporal disturbance patterns in sound speed profile (SSP) inversion using the traditional geographic grid division method, this study proposes an acoustic stability pre-clustering framework that integrates principal component analysis and machine learning clustering. Disturbance mode principal component analysis is first used to extract characteristic parameters, and then a machine learning clustering algorithm is adopted to pre-classify SSP samples according to acoustic stability. The SSP inversion experimental results show that: (1) the SSP samples of the South China Sea can be divided into three clusters of disturbance modes with statistically significant differences. (2) The regression inversion method based on cluster attribution reduces the average error of SSP inversion for data from 2018 to 1.24 m/s, which is more than 50% lower than what can be achieved with the traditional method without pre-clustering. (3) Transmission loss prediction verification shows that the proposed method can produce highly accurate sound field calculations in environmental assessment tasks. The acoustic stability pre-clustering technology proposed in this study provides an innovative solution for the statistical modeling of marine environment parameters by effectively decoupling the mixed effect of SSP spatiotemporal disturbance patterns. Its error control level (<1.5 m/s) is 37% higher than that of the single empirical orthogonal function regression method, showing important potential in underwater acoustic applications in complex marine dynamic environments. Full article

The rapid expansion of deep-sea sensing networks underscores the critical need for accurate underwater positioning of observation base stations. However, achieving precise acoustic localization, particularly at depths exceeding 4 km, remains a significant challenge due to systematic ranging errors, clock drift, and inaccuracies in sound speed modeling. This study proposes and validates a three-tier calibration framework consisting of a Dynamic Single-Difference (DSD) solver, a geometrically optimized reference buoy selection algorithm, and a Virtual Beacon (VB) depth inversion method based on sound speed profiles. Through simulations under varying noise conditions, the DSD method effectively mitigates common ranging and clock errors. The geometric reference optimization algorithm enhances the selection of optimal buoy layouts and reference points. At a depth of 4 km, the VB method improves vertical positioning accuracy by 15% compared to the DSD method alone, and nearly doubles vertical accuracy compared to traditional non-differential approaches. This research demonstrates that deep-sea underwater target calibration can be achieved without high-precision time synchronization and in the presence of fixed ranging errors. The proposed framework has the potential to lower technological barriers for large-scale deep-sea network deployments and provides a robust foundation for autonomous deep-sea exploration. Full article

Sound speed profile (SSP) inversion based on remote sensing parameters allows for the acquisition of global quasi-real-time SSPs without the need for on-site measurements, thereby fulfilling the requirements of many acoustic applications. This study makes two enhancements to the single empirical orthogonal function regression (SEOF-R) method. First, the k-means clustering algorithm is utilized to cluster SSP samples, ensuring the consistency of perturbation modes in the physical regression. Second, baroclinic modes are employed to derive a novel SSP basis function, named the ocean mode basis, which accurately characterizes the inversion relationship. Validation experiments using data from the South China Sea yield promising results. Compared with the SEOF-R method, the reconstruction error of the improved approach is reduced by 27%, with an average reconstruction error of 1.73 m/s. The average prediction transmission loss error decreases by 70%, reaching 1.29 dB within 50 km. The grid-free processing and low sample dependence of the proposed method further enhance the applicability and accuracy of remote sensing-based SSP inversion. Full article

The sound speed profile (SSP) is an important feature in the field of ocean acoustics. The accurate estimation of SSP is significant for the development of underwater position, communication, and associated fundamental marine research. The Res-SACNN model is proposed for SSP inversion based on the convolutional neural network (CNN) embedded with the residual network and self-attention mechanism. It combines the spatiotemporal characteristics of sea level anomaly (SLA) and sea surface temperature anomaly (SSTA) data and establishes a nonlinear relationship between satellite remote sensing data and sound speed field by deep learning. The single empirical orthogonal function regression (sEOF-r) method is used in a comparative experiment to confirm the model’s performance in both the time domain and the region. Experimental results demonstrate that the proposed model outperforms sEOF-r regarding both spatiotemporal generalization ability and inversion accuracy. The average root mean square error (RMSE) is decreased by 0.92 m/s in the time-domain experiment in the South China Sea, and the inversion results for each month are more consistent. The optimization ratio hits 71.8% and the average RMSE decreases by 7.39 m/s in the six-region experiment. The Res-SACNN model not only shows more superior inversion ability in the comparison with other deep-learning models, but also achieves strong generalization and real-time performance while maintaining low complexity, providing an improved technical tool for SSP estimation and sound field perception. Full article

Sound speed profiles (SSPs) must be detected simultaneously to perform a multibeam depth survey. Accurate real-time sound speed profile (SSP) acquisition remains a critical challenge in deep-sea multibeam bathymetry due to the limitations regarding direct measurements under harsh operational conditions. To address the issue, we propose a joint inversion framework integrating World Ocean Atlas 2023 (WOA23) temperature–salinity model data, historical in situ SSPs, and surface sound speed measurements. By constructing a high-resolution regional sound speed field through WOA23 and historical SSP fusion, this method effectively mitigates spatiotemporal heterogeneity and seasonal variability. The artificial lemming algorithm (ALA) is introduced to optimize the inversion of empirical orthogonal function (EOF) coefficients, enhancing global search efficiency while avoiding local optimization. An experimental validation in the northwest Pacific Ocean demonstrated that the proposed method has a better performance than that of conventional substitution, interpolation, and WOA23-only approaches. The results indicate that the mean absolute error (MAE), root mean square error (RMSE), and maximum error (ME) of SSP reconstruction are reduced by 41.5%, 46.0%, and 49.4%, respectively. When the reconstructed SSPs are applied to multibeam bathymetric correction, depth errors are further reduced to 0.193 m (MAE), 0.213 m (RMSE), and 0.394 m (ME), effectively suppressing the “smiley face” distortion caused by sound speed gradient anomalies. The dynamic selection of the first six EOF modes balances computational efficiency and reconstruction fidelity. This study provides a robust solution for real-time SSP estimation in data-scarce deep-sea environments, particularly for underwater autonomous vehicles. This method effectively mitigates the seabed distortion caused by missing real-time SSPs, significantly enhancing the accuracy and efficiency of deep-sea multibeam surveys. Full article

The remote sensing-based inversion of sound speed profile (SSP) enables the acquisition of high-spatial-resolution SSP without in situ measurements. The spatial division of the inversion grid is crucial for the accuracy of results, determining both the number of samples and the consistency of inversion relationships. The result of our research is the introduction of a physics-inspired self-organizing map (PISOM) that facilitates SSP inversion by clustering samples according to the physical perturbation law. The linear physical relationship between sea surface parameters and the SSP drives dimensionality reduction for the SOM, resulting in the clustering of samples exhibiting similar disturbance laws. Subsequently, samples within each cluster are generalized to construct the topology of the solution space for SSP reconstruction. The PISOM method significantly improves accuracy compared with the SOM method without clustering. The PISOM has an SSP reconstruction error of less than 2 m/s in 25% of cases, while the SOM method has none. The transmission loss calculation also shows promising results, with an error of only 0.5 dB at 30 km, 5.5 dB smaller than that of the SOM method. A physical interpretation of the neural network processing confirms that physics-inspired clustering can bring better precision gains than the previous spatial grid. Full article

Real-time and accurate construction of regional sound speed profiles (SSPs) is important for building underwater positioning, navigation, and timing (PNT) systems as it greatly affects signal propagation modes. In this paper, we summarize and analyze the current research status in the field of underwater SSP construction, where the mainstream methods include direct SSP measurement and SSP inversion. For the direct measurement method, we compare the performance of popular international and commercial brands of temperature, conductivity, and depth profilers (CTDs). For the inversion methods, the framework and basic principles of matched field processing (MFP), compressive sensing (CS), and deep learning (DL) are introduced, and their advantages and disadvantages are compared. Presently, SSP inversion relies on sonar observation data, limiting its applicability to areas that can only be reached by underwater observation systems. Furthermore, these methods are unable to predict the distribution of sound velocity in future time. Therefore, the mainstream trend in future research on SSP construction will involve comprehensive utilization of multi-source data to offer elastic sound velocity distribution estimation services for underwater users without the need for sonar observation data. Full article

This paper is dedicated to the acoustic inversion of the vertical sound speed profiles (SSPs) in the underwater marine environment. The method of automatic differentiation is applied for the first time in this context. Representing the finite-difference Padé approximation of the propagation operator as a computational graph allows for the analytical computation of the gradient with respect to the SSP directly within the numerical scheme. The availability of the gradient, along with the high computational efficiency of the numerical method used, enables rapid inversion of the SSP based on acoustic measurements from a hydrophone array. It is demonstrated that local optimization methods can be effectively used for real-time sound speed inversion. Comparative analysis with existing methods shows the significant superiority of the proposed method in terms of computation speed. Full article

The distribution of ocean sound speed profiles (SSPs) profoundly influences the design of underwater acoustic communication and positioning systems. Conventional methods for measuring sound speed by instruments entail high time costs, while sound speed inversion methods offer rapid estimation of SSPs. However, these methods heavily rely on sonar observational data and lack the capacity to swiftly estimate SSPs in arbitrary oceanic regions, particularly in scenarios with few-shot data. Precisely estimating non-cooperative maritime SSPs under such conditions poses a significant challenge. To explore temporal distribution patterns of sound speed and achieve precise SSP predictions with limited data, we propose a hierarchical long short-term memory transfer learning (H-LSTM-TL) framework. The core idea involves pre-training the base model on extensive public datasets, transferring the acquired knowledge to task models, and fine-tuning the task model on few-shot data to predict future SSPs. Through H-LSTM-TL, it accelerates model convergence, enhances sensitivity to few-shot input data, alleviates overfitting issues, and notably improves the accuracy of SSP predictions. Experimental results demonstrate that the H-LSTM-TL model exhibits strong generalization capabilities in few-shot data scenarios, effectively reducing overfitting problems and proving its applicability for rapid prediction of SSPs. Full article

The spatial-temporal distribution of underwater sound speed plays a critical role in determining the propagation mode of underwater acoustic signals. Therefore, rapid estimation and prediction of sound speed distribution are imperative for facilitating underwater positioning, navigation, and timing (PNT) services. While sound speed profile (SSP) inversion methods offer quicker response times compared to direct measurement methods, these methods often focus on constructing spatial sound velocity fields and heavily rely on sonar observation data, thus imposing stringent requirements on data sources. To delve into the temporal distribution pattern of sound speed and achieve SSP prediction without relying on sonar observation data, we introduce the hierarchical long short-term memory (H-LSTM) neural network for SSP prediction. Our method enables the estimation of sound speed distribution without the need for on-site data measurement, significantly enhancing time efficiency. Compared to other state-of-the-art approaches, the H-LSTM model achieves a root mean square error (RMSE) of less than 1 m/s in predicting monthly average sound speed distribution. Its prediction accuracy has improved several-fold over alternative methods, which validates the robust capability of our proposed model in predicting SSP. Full article

Ocean sound speed is important for underwater acoustic applications, such as communications, navigation and localization, where the assumption of uniformly distributed sound speed profiles (SSPs) is generally used and greatly degrades the performance of underwater acoustic systems. The acquisition of SSPs is necessary for the corrections of the sound ray propagation paths. However, the inversion of SSPs is challenging due to the intricate relations of interrelated physical ocean elements and suffers from the high costs of calculations and hardware deployments. This paper proposes a novel sound speed inversion method based on multi-source ocean remote sensing observations and machine learning, which adapts to large-scale sea regions. Firstly, the datasets of SSPs are generated utilizing the Argo thermohaline profiles and the empirical formulas of the sound speed. Then, the SSPs are analyzed utilizing the empirical orthogonal functions (EOFs) to reduce the dimensions of the feature space as well as the computational load. Considering the nonlinear regression relations of SSPs and the observed datasets, a general framework for sound speed inversion is formulated, which combines the designed machine learning models with the reduced-dimensional feature representations, multi-source ocean remote sensing observations and water temperature data. After being well trained, the proposed machine learning models realize the accurate inversion of the targeted ocean region by inputting the real-time ocean environmental data. The experiments verify the advantages of the proposed method in terms of the accuracy and effectiveness compared with conventional methods. Full article

Underwater Sound Speed Profile (SSP) distribution is crucial for the propagation mode of acoustic signals, so fast and accurate estimation of SSP is of great importance in building underwater observation systems. The state-of-the-art SSP inversion methods include frameworks of matched field processing (MFP), compressive sensing (CS), and feed-forward neural networks (FNNs), among which the FNN shows better real-time performance while maintaining the same level of accuracy. However, the training of FNN needs quite a lot historical SSP samples, which is difficult to satisfy in many ocean areas. This situation is called few-shot learning. To tackle this issue, we propose a multi-task learning (MTL) model with partial parameter sharing among different training tasks. By MTL, common features could be extracted, which accelerates the learning process on given tasks, and reduces the demand for reference samples, enhancing the generalization ability in few-shot learning. To verify the feasibility and effectiveness of MTL, a deep-ocean experiment was held in April 2023 in the South China Sea. Results show that MTL outperforms the other mainstream methods in terms of accuracy for SSP inversion, while inheriting the real-time advantage of FNN during the inversion stage. Full article

The inversion of acoustic field data to estimate geoacoustic parameters has been a prominent research focus in the field of underwater acoustics for several decades. Modal-dispersion curves have been used to inverse seabed sound speed and density profiles, but such techniques do not account for attenuation inversion. In this study, a new approach where modal-dispersion and waveform envelope data are simultaneously inversed under a multi-objective framework is proposed. The inversion is performed using the Multi-Objective Bayesian Optimization (MOBO) method. The posterior probability densities (PPD) of the estimation results are obtained by resampling from the exploited state space using the Gibbs Sampler. In this study, the implemented MOBO approach is compared with individual inversions both from modal-dispersion curves and the waveform data. In addition, the effective use of the Wasserstein metric from optimal transport theory is explored. Then the MOBO performance is tested against two different cost functions based on the L2 norm and the Wasserstein metric, respectively. Numerical experiments are employed to evaluate the effect of different cost functions on inversion performance. It is found that the MOBO approach may have more profound advantages when applied to Wasserstein metrics. Results obtained from our study reveal that the MOBO approach exhibits reduced uncertainty in the inverse results when compared to individual inversion methods, such as modal-dispersion inversion or waveform inversion. However, it is important to note that this enhanced uncertainty reduction comes at the cost of sacrificing accuracy in certain parameters other than the sediment sound speed and attenuation. Full article

The sound speed profile (SSP) is a necessary prerequisite for acoustic field computation and underwater target localization and monitoring. Due to the dynamic nature of the ocean, the reconstruction of SSPs with surface characteristics is a big challenge. In this study, the Single Empirical Orthogonal Function Regression (sEOF-R) method is employed to establish the regression relationship between the surface parameters and the sound speed anomaly profile (SSAP) in three typical sea areas, namely the equator, Kuroshio Extension (KE), and Northeast Pacific. Based on the established regression relationship and the surface parameters, the underwater SSP is reconstructed. Results show that the reconstruction effects in the three areas show the best performance in the Northeast Pacific, followed by the equator and finally the KE. The quantitative analysis suggests that the local sea level anomaly (SLA) plays the dominant role in influencing the reconstruction effect, followed by the sea surface temperature anomaly (SSTA). Further analysis demonstrates that the sEOF-R method is limited in time-varying and space-varying areas. The SSP reconstructed from the sea surface information in this study is useful for the inversion of the underwater structures. Full article

Understanding the vertical structure of ozone concentrations in different seasons and their correlations with the associated meteorological conditions is crucial for exploring atmospheric ozone variability and improving the accuracy of regional ozone prediction. In this study, an ozone-sounding experiment was carried out at the Shaowu sounding Station in Fujian from November 2021 to May 2022 in order to obtain vertical profiles of ozone concentrations and synoptic variables. Based on these observations, we examined the characteristics of tropospheric ozone profiles in spring over the Wuyishan region and their comparison with wintertime ozone. The results show that compared with winter, the total ozone column (TOC) in spring has increased by 64.4%, with an enhancement of 23.8% for the troposphere and a greater increment of 69.1% for the stratosphere. The sub-peaks of tropospheric ozone below 12 km are found in both spring and winter of 2022, which are accompanied by lower relative humidity (<10% in winter and <15% in spring), temperature inversions in some cases, and intensive westerly winds. Furthermore, we investigated the relationship between ozone volume mixing ratio (OVMR) and synoptic conditions in the Wuyishan region and concluded that OVMR above 1.5 km is negatively correlated with temperature and relative humidity but positively correlated with wind speed. Additionally, springtime OVMR in the middle and upper troposphere exhibits a “funnel” distribution, showing a higher OVMR on the day of sounding observations and one day before and after that on adjacent days with low-level southwesterly winds and updrafts. While in winter, the strong downdrafts dominate on the sounding observation day. Full article