Abstract:Mesoscale eddies are ubiquitous in the world's oceans. They are rotated with a 10-100 km length scale. The relatively isolated water mass trapped with mesoscale eddies can propagate a long distance while maintaining the source thermohaline characteristics. Thus, there are significant differences in the thermohaline structures between the inside and outside eddies, which have profound effects on underwater acoustic propagation.The earliest studies on the impact of eddies on sound propagation were conducted by V… Show more
“…SSTA × SSTA I (mon) + p (2) SSTA (10) where p (1) and p (2) are calculated by the least squares method (LSM) method, and the coefficients p SSTA in ms −1 • C −1 denote the sensitivity of reconstruction error to SLA I and SSTA I , respectively. A bigger p (1) means a higher sensitivity.…”
Section: Effect Analysis Of the Seof-r Methods In Ssp Reconstructionmentioning
confidence: 99%
“…For the in situ observation method, observational instruments, such as Argo buoys, expendable conductivity temperature depth (XCTD) [7], expendable bathythermograph (XBT) [8], and sound velocity profile (SVP) [9], are applied to obtain the hydrological parameters such as T, S, and pressure. Although this method can obtain the highest-accuracy SSP, conducting in situ experiments is difficult because they are resource-and time-consuming endeavors and are performed only by a small number of communities [10][11][12].…”
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.
“…SSTA × SSTA I (mon) + p (2) SSTA (10) where p (1) and p (2) are calculated by the least squares method (LSM) method, and the coefficients p SSTA in ms −1 • C −1 denote the sensitivity of reconstruction error to SLA I and SSTA I , respectively. A bigger p (1) means a higher sensitivity.…”
Section: Effect Analysis Of the Seof-r Methods In Ssp Reconstructionmentioning
confidence: 99%
“…For the in situ observation method, observational instruments, such as Argo buoys, expendable conductivity temperature depth (XCTD) [7], expendable bathythermograph (XBT) [8], and sound velocity profile (SVP) [9], are applied to obtain the hydrological parameters such as T, S, and pressure. Although this method can obtain the highest-accuracy SSP, conducting in situ experiments is difficult because they are resource-and time-consuming endeavors and are performed only by a small number of communities [10][11][12].…”
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.
“…Various oceanic processes can disturb the sound speed profile (SSP), which is crucial in understanding the spatial correlation characteristics of the sound field with such spatiotemporal variations and their implications on the array gain [13]. With the development of satellite remote sensing technology, it is known that mesoscale eddies, pervasive in the ocean, significantly contribute to the kinetic energy of currents [14]. These eddies influence the distribution of temperature, salinity, density, and flow, thereby altering the structural features of SSP and subsequently impacting sound propagation [15,16].…”
To solve the problem of array detection performance in environments with mesoscale eddies, this study utilizes the Gaussian eddy model to describe the sound speed structure disturbed by eddies. Through numerical simulations, the corresponding sound field is obtained, and the transmission loss influenced by the eddy is analyzed. Furthermore, to investigate the relation between the array gain and spatial correlation in the eddy environments, the differences in vertical correlation at different positions and their effects on the vertical array gain of conventional beamforming (CBF) are studied. When the source is around the eddy center, the conclusions drawn are as follows: (1) The presence of an eddy changes the turning-point depth and the sound field distribution, significantly affecting the direct sound region and the first convergence zone, while having a minor impact on the first shadow zone. (2) In different eddy-induced environments, the first convergence zone maintains a high vertical correlation, but the vertical correlation of the direct sound region is greatly influenced by the eddy. (3) The array gain of CBF is consistent with the vertical correlation. When the correlation between each element of the sound field is great, the array gain increases with the number of array elements.
“…In the global oceans, mesoscale eddies are ubiquitous (Chelton et al, 2011). They are usually accompanied by temperature and salinity anomalies, and thus distort the sound speed profile (SSP) (Jian et al, 2009;Chen et al, 2022). Due to the significant abnormal sound fields, the impacts of mesoscale eddies on the acoustic properties gained considerable research attention.…”
Section: Introductionmentioning
confidence: 99%
“…Thus, based on the composite research, which combines Argo floats and satellite altimetry, the 3D structures of mesoscale eddies and their impacts on the acoustic characteristics are derived. Chen et al (2022) established a region-dependent parametric model for eddyinduced sound speed anomaly structure based on abundant Argo profiles. The parametric model can fast reconstruct the underwater sound speed field only using the satellite altimetry data.…”
Acoustic rays are modified while propagating through oceanic eddies. However, due to the lack of field synchronous observation, the impact of mesoscale eddy on the acoustic propagation is less clarified. To address the issue, an eddy-acoustic synchronous observation (EASO) field experiment for a mesoscale warm eddy was carried out in the slope of the South China Sea (SCS) in October, 2021. During the field experiment, a total of 105 conductivity-temperature-depth (CTD) stations, as well as a zonal acoustic survey line through the center of the warm eddy, were obtained. The vertical structures of temperature and salinity indicate that the warm eddy is surface-intensified with temperature and salinity cores confined within depths from 70 m to 200 m and 10 m to 70 m, respectively. The acoustic observation shows two obvious convergency zones (CZs) at about 39 km and 92 km in the eastern half acoustic line, and one convergency zones (CZ) at about 25 km in the western half acoustic line. By comparing with the none eddy circumstance, the respective impacts of the topography and warm eddy are quantitatively analyzed with a ray-tracing model. The results indicate that the topography shortens the horizontal span of the CZ by 11.4 km, while the warm eddy lengthens it by 1.7 km. Additionally, the warm eddy shallows the depth and broadens the width of the CZ by 32 m and 1.4 km, respectively. The anisotropy of 3D sound fields jointly influenced by the warm eddy and the local topography show that the distance differences of the first CZs in different horizontal directions can be as long as 31 km.
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