Search Results (15)

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

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

The sound speed in the ocean has a considerable impact on the characteristics of underwater acoustic propagation. The swift gathering of the underwater three-dimensional (3D) sound speed field is essential for target detection, underwater acoustic communication, and navigation. Currently, the reconstruction of the underwater sound speed utilizing satellite remote sensing data of the sea surface has emerged as a significant area of research. However, dynamic activities within the ocean result in varying degrees of perturbation in the sound speed structure. Relying solely on sea surface information will restrict the accuracy of sound speed reconstruction. In response to this issue, by utilizing multi-source satellite remote sensing data alongside Argo profiles, we first implemented the random forest (RF) algorithm to establish the statistical mapping relationship from the sea surface temperature (SST), sea level anomaly (SLA), and absolute dynamic topography (ADT) to the density, and thus, reconstructed a 3D density field. Subsequently, based on the sea surface environmental information, we introduced the underwater vertical density as a novel input for sound speed calculations and proposed a new model for 3D sound speed field reconstruction (RF-SDR). The experimental results indicate that utilizing both the sea surface environmental variables and underwater density as inputs yielded an average root-mean-square error (RMSE) of 1.51 m/s for the reconstructed sound speed, along with an average mean absolute error (MAE) of 0.85 m/s. Following the incorporation of density into the reconstruction inputs, the two error metrics exhibited reductions of 31% and 35%, respectively. And the proposed RF-SDR model demonstrated a reduction in the RMSE by 36% and in the MAE by 43% when compared with the commonly utilized single Empirical Orthogonal Function regression (sEOF-r) method. Furthermore, simulations of the sound propagation with both the reconstructed sound speed and Argo sound speed demonstrated a high degree of consistency in the computed acoustic propagation losses. The correlation coefficients consistently exceeded 0.7, thereby reinforcing the validity of the reconstructed sound speed. 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

The speed of sound in seawater plays a crucial role in determining the accuracy of multibeam bathymetric measurements. In deep-sea multibeam measurements, the challenge of inadequate longitudinal coverage of sound speed profiles arises from variations in seafloor topography, meteorological conditions, measurement equipment, and operational efficiency, resulting in diminished measurement precision. Building upon the EOF (Empirical Orthogonal Function), a method employed to analyze spatiotemporal data such as sound speeds, this paper addresses the limitations of the EOF method caused by the shallowest sampling depth of the sound speed profile samples. It proposes two methods for EOF reconstruction of measured sound speed profiles extended to full water depth by splicing measured sound speed profiles at non-full water depths with historical average sound speed profiles of the surveyed sea area. Specially, Method 2 introduces the latest metaheuristic optimization algorithm, CPO (Crested Porcupine Optimizer), which exhibited superior performance on multiple standard test functions in 2024. The study reconstructs randomly sampled measured sound speed profiles using the two proposed methods and commonly employed substitution and splicing methods, followed by a comparative analysis of the experimental outcomes. At a sampling depth of 200 m, Method 2 demonstrates performance superior to other methods, with RMSE, MAE, MAPE, and R² values of 0.9511 m/s, 0.8492 m/s, 0.0566%, and 0.9963, respectively. Method 1 yields corresponding values of 0.9594 m/s, 0.8492 m/s, 0.0568%, and 0.9962, respectively. Despite its slightly inferior performance compared with Method 2, it offers substantial advantages over the substitution and splicing methods. Varying the sampling depth of measured sound speed profiles reveals that Methods 1 and 2 exhibit inferior reconstruction performance in shallow water compared with the substitution and splicing methods. Nevertheless, when the sampling depth surpasses the depth range of initial spatial modes with abrupt variations, both methods achieve notably higher reconstruction accuracy compared with the substitution and splicing methods, reaching a stabilized state. Sound ray tracing reveals that the reconstructed sound speed profiles from both methods meet the stringent accuracy standards for bathymetric measurements, achieving an effective beam ratio of 100%. The proposed methods not only provide rapid reconstruction of sound speed profiles, thereby improving the efficiency of multibeam bathymetric surveys, but also provide references for the reasonable determination of sampling depths of sound speed profiles to ensure reconstruction accuracy, demonstrating practical application value. Full article

One of the crucial branches of activity at the Łukasiewicz Research Network—Institute of Aviation is developing a suborbital rocket vehicle capable of launching small payloads beyond the Earth’s atmosphere, reaching over 100 km in altitude. Ensuring safety is a primary concern, particularly given the finite flight zone and impact area. Crucial to safety analysis is the wind profile, especially in the very first seconds of a flight, when rocket velocity is of the same order as the wind speed. Traditional near-ground wind data sources, ranging from wind towers to numerical models of the atmosphere, have limitations. Wind towers are costly and unfeasible at many test ranges used for launches, while numerical modeling may not reflect the specific ground profile near the launcher due to their large cell size (2 to +10 km). Meteorological balloons are not favorable for such measurements as they aim to provide the launch operator with a wind profile at high altitudes, and are launched only 1–2 times per flight attempt. Our study sought to prototype a wind measurement system designed to acquire near-ground wind profile data. It focuses on measuring wind direction and speed at near-ground altitudes with higher flight frequency, offering data on demand shortly before launch to help ensure safety. This atmosphere sounding system consists of an Unmanned Aerial Vehicle (UAV) equipped with an onboard ultrasonic wind sensor. Some reports in the literature have discussed the possibility of using UAV-borne anemometers, but the topic of measurement errors introduced by placing the anemometer onboard an UAV remains under studied. Limited research in this area underlines the need for experimental validation of design choices–for specific types of UAVs, anemometers, and mounting. This paper presents a literature review, a detailed overview of the prototyped system, and flight test results in both natural (outdoor) and controlled (indoor, no wind) conditions. Data from the UAV system’s anemometer was benchmarked against a stationary reference weather station, in order to examine the influence of the UAV’s rotor on the anemometer readings. Our findings show a wind speed Root Mean Square Error (RMSE) of 5 m/s and a directional RMSE of below 5.3° (both averaged for 1 min). The results were also compared with similar UAV-based wind measurements. The prototyped system was successfully used in a suborbital rocket launch campaign, thus demonstrating the feasibility of integrating UAVs with dedicated sensors for performing regular meteorological measurements in automatic mode. Full article

The ultra-short baseline (USBL) positioning system has been widely used for autonomous and remotely operated vehicle (ARV) positioning in marine resource surveying and ocean engineering fields due to its flexible installation and portable operation. Errors related to the sound speed are a critical factor limiting the positioning performance. The conventional strategy adopts a fixed sound velocity profile (SVP) to correct the spatial variation, especially in the vertical direction. However, SVP is actually time-varying, and ignoring this kind of variation will lead to a worse estimation of ARVs’coordinates. In this contribution, we propose a two-step sound speed correction method, where, firstly, the deviation due to the acoustic ray bending effect is corrected by the depth-based ray-tracing policy with the fixed SVP. Then, the temporal variation of SVP is considered, and the fixed SVP is adaptively adjusted according to the in situ sound velocity (SV) measurements provided by the conductivity–temperature–depth (CTD) sensor equipped at the ARV. The proposed method is verified by semi-physical simulation and sea-trail dataset in the South China Sea. When compared to the fixed-SVP method, average positioning accuracy with the resilient SVP be improved by 8%, 21%, and 26% in the east, north, and up directions, respectively. The results demonstrate that the proposed method can efficiently improve the adaptability of sound speed observations and deliver better performance in USBL real-time positioning. Full article

Understanding the vertical structures of cloud condensation nuclei (CCN) and ice-nucleating particle (INP) number concentrations in desert source regions is crucial for examining dust-cloud interactions and other related impacts. To explore the vertical profiles of the CCN and INP number concentrations and their possible atmospheric–dynamic influence factors at the center of the Taklimakan Desert, intensive observations were conducted by employing a ground-based polarization Raman lidar, sounding balloons, and a sun photometer in Tazhong (83.39° E, 38.58° N, 1103 m above sea level) during the summer of 2019. Based on the GRASP algorithm, the extinction-to-volume conversion factor of dust aerosols was 0.85 × 10⁻¹² Mmm³ m⁻³, and the extinction-to-number conversion factor was predicted to be 0.20 Mm cm⁻³ on the basis of the sun photometer observations. Thus, the vertical CCN and INP number concentration profiles obtained with different parameterization schemes in the presence of various pollution levels were calculated by combining dust extinction coefficients retrieved by lidar and meteorological data observed by sounding balloon observations. The achieved results indicated that the CCN number concentration varied from 10⁻² to 10² cm⁻³ and decreased from ground level to 12 km with an average value of 36.57 cm⁻³ at the 10–12 km height range, while the INP number concentration based on parameterization schemes D10 and D15 mainly varied from 10⁻¹ to 10² L⁻¹ and from 1 L⁻¹ to 10³ L⁻¹, with average values of 3.50 L⁻¹ and 7.80 L⁻¹, respectively. Moreover, we observed a strong relationship between the INP number concentration of scheme D10 and the wind speed, with an R² value of 0.72, but a weak relationship between the CCN number concentration and the relative humidity in the boundary layer, with a Spearman’s rank correlation coefficient R² value of 0.38. The present study provides original and valuable information regarding the CCN and INP number concentrations and their related influencing factors at the center of the Taklimakan Desert and can improve our understanding of the vertical distributions of dust–cloud–atmosphere dynamic interactions, as well as of the roles of dust aerosols in the desert hydrological cycle. Full article

The sound speed profile data of seawater provide an important basis for carrying out underwater acoustic modeling and analysis, sonar performance evaluation, and underwater acoustic assistant decision-making. The data volume of the high-resolution sound speed profile is vast, and the demand for data storage space is high, which severely limits the analysis and application of the high-resolution sound speed profile data in the field of marine acoustics. This paper uses the dictionary learning method to achieve sparse coding of the high-resolution sound speed profile and uses a compressed sparse row method to compress and store the sparse characteristics of the data matrix. The influence of related parameters on the compression rate and recovery data error is analyzed and discussed, as are different scenarios and the difference in compression processing methods. Through comparative experiments, the average error of the sound speed profile data compressed is less than 0.5 m/s, the maximum error is less than 3 m/s, and the data volume is about 10% to 15% of the original data volume. This method significantly reduces the storage capacity of high-resolution sound speed profile data and ensures the accuracy of the data, providing technical support for efficient and convenient access to high-resolution sound speed profiles. Full article

Seafloor geoacoustic properties are important in determining sound propagation in the marine environment, which broadly affects sub-sea activities. However, geoacoustic investigation of the deep seafloor, which is required by the recent expansion of deep-water operations, is challenging. This paper presents a methodology for estimating the seafloor sound speed, $c_0$, and a sub-bottom velocity gradient, $K$, in a relatively deep-water-compacting (~1000 m) passive-margin setting, based on standard commercial 2D seismic data. Here we study the seafloor of the southeastern Mediterranean margin based on data from three commercial seismic profiles, which were acquired using a 7.2 km-long horizontal receiver array. The estimation applies a geoacoustic inversion of the wide-angle reflections and the travel times of the head waves of bending rays. Under the assumption of a constant positive $K$, the geoacoustic inversion converges to a unique set of parameters that best satisfy the data. The analysis of 24 measurement locations revealed an increase in the average estimates of $c_0$ from 1537 $\pm$ 13 m s⁻¹ to 1613 $\pm$ 12 m s⁻¹ for seafloor depths between ~1150 m and ~1350 m. $K$ ranged between 0.75 and 0.85 m s⁻¹ with an average of 0.80 $\pm$ 0.035 s⁻¹. The parameters were consistent across the different locations and seismic lines and they match the values that were obtained through depth-migration-velocity analysis and empiric relations, thereby validating our estimation methodology. Full article

In the current study, solid-state additive manufacturing (SSAM) of two temper conditions AA2011 was successfully conducted using the friction stir deposition (FSD) process. The AA2011-T6 and AA2011-O consumable bars of 20 mm diameter were used as a feeding material against AA5083 substrate. The effect of the rotation rate and feeding speed of the consumable bars on the macrostructure, microstructure, and hardness of the friction stir deposited (FSD) materials were examined. The AA2011-T6 bars were deposited at a constant rotation rate of 1200 rpm and different feeding speeds of 3, 6, and 9 mm/min, whereas the AA2011-O bars were deposited at a constant rotation rate of 200 mm/min and varied feeding speeds of 1, 2, and 3 mm/min. The obtained microstructure was investigated using an optical microscope and scanning electron microscope equipped with EDS analysis to evaluate microstructural features. Hardness was also assessed as average values and maps. The results showed that this new technique succeeded in producing sound additive manufactured parts at all the applied processing parameters. The microstructures of the additive manufactured parts showed equiaxed refined grains compared to the coarse grain of the starting materials. The detected intermetallics in AA2011 alloy are mainly Al₂Cu and Al₇Cu₂Fe. The improvement in hardness of AA2011-O AMPs reached 163% of the starting material hardness at the applied feeding speed of 1 mm/min. The hardness mapping analysis reveals a homogeneous hardness profile along the building direction. Finally, it can be said that the temper conditions of the starting AA2011 materials govern the selection of the processing parameters in terms of rotation rate and feeding speed and affects the properties of the produced additive manufactured parts in terms of hardness and microstructural features. Full article

More than seven years of remote sensing data with high spatial and temporal resolution were investigated in this study. The 20-min moving averaged wind profiles form the acoustic sounding with Scintec MFAS sodar were derived every 10 min. The profiles covered from 30 to 600 m height with vertical resolution of 10 m. The wind speed probability and the Weibull distribution parameters were calculated by the maximum likelihood method at each level and then the profiles of the Weibull scale and shape parameters were analyzed. Diurnal wind speed at heights above 200 m has shown a well-expressed increase in the averaged values during the night hours, while during the day lower wind speeds were observed. The reversal height was explored from spatially and temporally homogenized diurnal wind speed data with applied quadratic functions for better interpretation of the results. In addition, analyses by type of air masses (land or sea air mass) were performed. One of the outcomes of the study was assessment of the internal boundary layer height, which was estimated to 50–80 m at the location of the sodar. The obtained information forms the basis for climatological insights on the vertical structure of the coastal boundary layer and is unique long-term data set important not only for Bulgaria but for coastal meteorology in general. Full article

Coastal acoustic tomography (CAT), as an innovative technology, can perform water temperature measurements both in horizontal and vertical slices. Investigations on vertical slice observations are significantly fewer in number than horizontal observations due to difficulties in multi-path arrival peak identification. In this study, a two-station sound transmission experiment is carried out in Thousand-Island Lake, Hangzhou, China, to acquire acoustic data for water temperature profiling. Time windows, determined by range-independent ray simulation, are used to identify multi-path arrival peaks and obtain corresponding sound wave travel times. Special attention is paid to travel time correction, whose errors are caused by position drifting by more than 2 m of moored stations. The sound speed and temperature profiling are divided into four layers and are calculated by regularized inversion. Results show a good consistency with conductivity–temperature–depth (CTD) measurements. The root mean square error (RMSE) of water temperature is 0.3494, 0.6838, 1.0236 and 1.0985 °C for layer 1, 2, 3 and 4, respectively. The fluctuations of measurement are further smoothed by the moving average, which decreases the RMSE of water temperature to 0.2858, 0.4742, 0.7719 and 0.9945 °C, respectively. This study illustrates the feasibility and high accuracy of the coastal acoustic tomography method in short-range water temperature measurement. Furthermore, 3D water temperature field profiling can be performed with combined analyzing in horizontal and vertical slices. Full article

The operation of a Remotely Piloted Aircraft System (RPAS) over a hilly area in northern Germany allows inspection of the variability of the profiles of temperature, humidity, and wind speed next to a small hill. Four cases in nearly stationary conditions are analyzed. Two events are windy, one overcast and the other with clear skies, whereas the two other cases have weak winds, one overcast, and one with clear skies and dissipating mist. The profiles are made at five locations surrounding the hill, separated by a distance from each other of 5 km at most, sampling up to 130 m above the ground. The average profiles and their standard deviations indicate that the variability in the windy cases is approximately constant with height, likely linked to the turbulent flow itself, whereas, for the weak wind cases, the variability diminishes with height, and it is probably linked to the surface variability. The variability between soundings is large. The computation of the root mean square error with respect to the average of the soundings for each case shows that the site closest to the average is the one over open terrain and low vegetation, whereas the site in the forest is the farthest from average. Comparison with the profiles to the nearest grid point of the European Centre for Medium-Range Weather Forecasts (ECMWF) model shows that the closest values are provided by the average of the soundings and by the site closest to the average. Despite the small dataset collected during this exercise, the methodology developed here can be used for more cases and locations with the aim to characterize better the local variability in the lower atmosphere. In this sense, a non-dimensional heterogeneity index is proposed to quantify the topographically and thermally induced variability in complex terrain. Full article