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.

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.

Due to the limited number of deep sound speed profile (SSP) samples, the existing wide-area SSP inversion methods cannot estimate the full-depth SSP. In this paper, the full-depth SSP inversion is achieved by adding physical mechanism constraints to the neural network inversion algorithm. A dimensionality reduction approach for SSP perturbation, based on the hydrodynamic mechanism of seawater, is proposed. Constrained by the characteristics of ocean stratification, a self-organizing map is employed to invert the depth of the sound channel axis and reconstruct the SSP from the sea surface to the sound channel axis. The SSP from the sound channel axis to the seabed is reconstructed by integrating the characteristics of the sound channel axis and the sound speed gradient characteristics of the deep sea isothermal layer. The efficacy of the method was validated by the Argo data from the South China Sea. The average root mean square error of the reconstructed full-depth SSP is 2.85 m/s. Additionally, the average error of transmission loss prediction within 50 km is 2.50 dB. The proposed method is capable of furnishing effective full-depth SSP information without the necessity of any in situ measurements, thereby meeting the requirements of certain underwater acoustic applications.

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.

The geoacoustic parameter acquisition in the deep sea is of great significance to the research of ocean acoustics. This paper found that the interference structure of the shadow zone induced by the reflection of the high-speed sediment layer could be simply described by the grazing angle of the surface-bottom reflection from the theory of ray acoustics, when the source and receiver depth makes the grazing angle of the surface-bottom reflection consistent with that of the bottom-surface reflection. On this basis, a geoacoustic parameter inversion method by spatial position matching of interference fringes in the shadow zone was proposed, and an interference fringe extraction method was designed based on the maximum between-class variance algorithm in this paper. After extracting the results by the stripe coordinates in the simulation environment, the density was obtained by assuming the base sound speed as an empirical value and combining with Hamilton’s empirical formula, and the sediment sound speed and thickness were inverted by the grid search method. Those inversion results were compared with the multi-dimensional inversion results of the genetic algorithm. The simulation results showed that the fringe extraction method proposed in this paper could effectively extract the interference fringes formed by the reflection of the high-speed sediment in the shadow zone, and compared with the multi-dimensional optimization process, the relatively accurate inversion results of the sound speed and thickness of high-speed sediment could be obtained more effectively and quickly by taking the spatial position of the interference fringe as the cost function of the matching parameter combined with the grid search method in this paper.

Monitoring three-dimensional typhoon-induced perturbations is critical for ocean early warning systems; however, adverse sea conditions hinder in-situ observations. This study aims to validate the capability of mirror ocean acoustic tomography for sensing key environmental parameters during typhoons. Based on high-resolution ocean model simulations, three-dimensional environmental fields during the passage of Typhoons Trami and Yinxing in the South China Sea were reconstructed, and simulation accuracy was verified against moored buoy data. A quantitative analysis of 287 in-situ typhoon passage datasets from the World Ocean Database revealed characteristic responses in the Northwest Pacific: surface sound speed exhibited significant negative anomalies (averaging 1–2 m/s), while sonic layer depth substantially increased (reaching a maximum of 10 m). To achieve high-resolution inversion under such conditions, we developed a tomographic inversion method that employs Empirical Orthogonal Functions for vertical parameterization. For the horizontal representation, we combined Fourier basis functions with a grid discretization approach. Tikhonov regularization was applied to reconstruct three-dimensional current and sound speed fields. The results demonstrate that errors in inverted cross-sectional current velocities were below 0.04 m/s (Trami) and 0.07 m/s (Yinxing) while sound speed errors remained under 0.5 m/s and 1 m/s respectively; additionally, the core regional horizontal inversion errors were constrained below 0.1 m/s for current velocity and 1 m/s for sound speed. Furthermore, power spectrum analysis of the inverted currents successfully captured near-inertial oscillation signals, and the inversion of mean sound speed confirmed typhoon-induced reduction trends. This methodology provides a robust framework for monitoring three-dimensional dynamic environments in typhoon-affected seas and holds significant value for evaluating the performance of mesoscale and small-scale underwater acoustic detection systems.