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Theoretically, propagating internal tides in the ocean may reflect at turning depths, where buoyancy frequencies equal tidal frequencies, before colliding with the air-sea interface or rugged bottom topography. Globally, the internal tide lower turning depths (ITLTDs) in the open ocean have been mapped; however, knowledge of the presence of ITLTDs in the South China Sea (SCS) is lacking. In this study, 2 125 high-quality temperature-salinity profiles (including 58 deep-sea hydrographic measurements with observational depths exceeding 3 000 m) are collected and analyzed to investigate the existence of ITLTDs in the SCS. Furthermore, the concept of the upper turning depth is first introduced in the context of internal tides, and internal tide upper turning depths (ITUTDs) are also investigated. ITLTDs are found to exist at several abyssal stations; these stations are distributed mostly in the southern part of the SCS basin, possibly due to the greater water depths there. Fewer locations show the presence of ITLTDs for K1 versus M2 tidal frequencies because of the lower tidal frequency. The distance between ITLTDs and the seafloor ranged from 270 m to more than 1 200 m, implying the possible existence of multiple internal wave evanescent regions in the abyssal bottom. ITUTDs of tens of meters are ubiquitous in the SCS; stations with the presence of ITUTDs are located mainly in the northeastern SCS due to the intensive observations there. However, the calculated ITUTDs have large uncertainties; they are sensitive to the selected bin values. The horizontal propagation directions of internal tides in the SCS change dramatically, and as a result, the estimated turning depths under the full Coriolis force definition are different compared to that under the traditional approximation.

Targeted observation is an appealing procedure for improving model predictions. However, studies on oceanic targeted observations have been largely based on modeling efforts, and there is a need for field validating operations. Here, we report the results of a field targeted observation that is designed based on the sensitive areas identified by the Conditional Nonlinear Optimal Perturbation approach to improve the 7th day thermal structure prediction in the Yellow Sea. By introducing the technique of cycle data assimilation and the new concept of time-varying sensitive areas, an observing strategy is designed and validated by a set of Observing System Simulation Experiments. Then, the impact of targeted observations was investigated by a choreographed field campaign in the summer of 2019. The results of the in-field Observing System Experiments show that, compared to conventional local data assimilation, conducting targeted observations in the sensitive areas can yield more benefit at the verification time. Furthermore, dynamic analysis demonstrates that the refinement of vertical thermal structures is mainly caused by the changes in the upstream horizontal temperature advection driven by the Yellow Sea Cold Water Mass circulation. This study highlights the effectiveness of targeted observations on reducing the forecast uncertainty in the ocean.

The sensitive area of targeted observations for short-term (7 d) prediction of vertical thermal structure (VTS) in summer in the Yellow Sea was investigated. We applied the Conditional Nonlinear Optimal Perturbation (CNOP) method and an adjoint-free algorithm with the Regional Ocean Modeling System (ROMS). We used vertical integration of CNOP-type temperature errors to locate the sensitive areas, where reduction of initial errors is expected to yield the greatest improvement in VTS prediction for the selected verification area. The identified sensitive areas were northeast-southwest orientated northeast to the verification area, which were possibly related to the southwestward background currents. Then, we performed a series of sensitivity experiments to evaluate the effectiveness of the identified sensitive areas. Results show that initial errors in the identified sensitive areas had the greatest negative effect on VTS prediction in the verification area compared to errors in other areas (e.g., the verification area and areas to its east and northeast). Moreover, removal of initial errors through deploying simulated observations in the identified sensitive areas led to more refined prediction than correction of initial conditions in the verification area itself. Our results suggest that implementation of targeted observation in the CNOP-based sensitive areas is an effective method to improve short-term prediction of VTS in summer in the Yellow Sea.

This paper focuses on the problem of transient signal detection using a single vector hydrophone in a horizontal isotropic acoustic field. The covariance matrix of the three sensor output quantities is analyzed in terms of its trace and determinant principal invariants, and the mathematical relationship between the signal-to-noise ratio and these invariants is established. Both the signal power and the ambient-noise power can be accurately estimated directly from the covariance matrix of the received data, and the signal-to-noise ratio can further be obtained. A signal power detector is proposed based on the theoretical analysis, and simulation results show that (i) it can detect signals with low signal-to-noise ratio and (ii) its performance is greatly improved compared with that of an energy detector under the condition of a nonstationary ambient-noise field. An experiment was performed to verify the theoretical analysis and the conclusions of the numerical simulation, and the data-processing results show that this method has good applicability in signal detection.

Targeted observation is an appealing procedure for improving model predictions through the assimilation of additional collected measurements. However, studies on targeted observations in the oceani...

Currently, traditional feature extraction algorithms have poor data expression and noise robustness. Moreover, traditional recognition methods are gradually falling behind the demand for increasing data, and struggle to extract deep features in targets. By considering the preceding issues, an integrated neural network has been created in this paper for underwater acoustic target recognition via feature fusion learning. Firstly, the short time Fourier transform (STFT) amplitude spectrum, STFT phase spectrum, and bispectrum feature of underwater acoustic signals are extracted and form the input for the network. They not only contain rich information about the target, but also have strong noise robustness. Secondly, an integrated neural network has been designed, which is trained with different features and contains three neural networks. Finally, in the softmax layer of the network, the shuffled frog leaping algorithm (SFLA) is utilized to train the weight coefficients of different networks. Experimental results of the measured data show that the integrated neural network method based on feature fusion has a higher recognition accuracy and stronger noise robustness.

Ambient noise is very important in the prediction system of a sonar performance, because it determines the detection ranges always in a passive sonar and usually in an active sonar. In the uncertainty issue for the sonar performance, it is necessary to know this factor’s statistical characteristics that are only obtained by data processing from the underwater ambient noise measurements. Broad-band ambient noise signals from 16 hydrophones were amplified and recorded for 2 min every 1 h. The results show that the ambient noise is essentially depth independent. The cross correlation of the ambient noise levels (1, 6 and 12 h average) with a wind speed is presented. It was found that the correlation is excellent on the upper frequency band and the noise levels correlate better with high wind speed than with low wind speed.

Sound propagation in a deep ocean two-axis underwater channel is often complex and difficult to simulate between surface channel and sound fixing and ranging (SOFAR) channel. The beam-displacement ray-mode (BDRM) theory is a normal mode method for propagation modeling in horizontally stratified shallow water. An improved method for computing the upper boundary reflection coefficient in the BDRM is proposed and applied to calculate the acoustic fields of a two-axis underwater channel. Transmission losses in the two-axis underwater channel are calculated in the new BDRM. The corresponding results are in good agreement with those from the Kraken code, and furthermore the computed speed of the new BDRM excels the other methods.

In order to improve the computing speed of ocean acoustic field using the Beam-Displacement Ray-Mode (BDRM) theory, a BDRM parallel computing model based on Compute Unified Device Architecture (CUDA) is designed by virtue of the powerful parallel computing ability of GPU and the character of BDRM theory. The emphasis is how to implement parallel computing of eigen value and eigen function in CUDA programming model. The results of simulation experiment show that the CPU elapsed time increases fast but the GPU elapsed time increases slow with the frequency of the sound source reaching higher. The speedup in blue-water is bigger than that in shallow-water under the same frequency of the sound source. The speedups are 7.84× and 33.36× respectively in shallow-water and blue-water when the frequency of the sound source is 1000Hz. The BDRM parallel computing model based on CUDA has higher computing efficiency than the BDRM serial computing model based on CPU under large scale operations. It could achieve the requirement of fast forecast of ocean acoustic field and engineering application.

The distance change ratio of target can be described by distance characteristic feature. A method for extracting distance characteristic feature from LOFAR spectrum striations has been introduced. Because sound field computation and image processing consume too much time, CUDA technology has been used on extraction of distance characteristic feature to solve the problem of low efficiency. The CUDA technology which is provided by NVIDIA corporation has acquired programmability to perform general purpose fast computation by running ten thousands of threads concurrently. The experimental results show that the CUDA is practical for the application. Distance characteristic feature of target can be extracted in real-time based on CUDA technology.

Striations image generated in target localization algorithm using a guide source (GTL algorithm) is always blurred and indistinct because of noise pollution. Range estimation can not be realized. In order to achieve the distance accurate localization in the low signal to noise (SNR), image de-noising method is proposed. After de-noising, radon transform has been used. Wavelet threshold de-noising method is proposed in this paper. Because the hard and soft threshold functions have disadvantages, a new method of threshold function is introduced. The simulation results show that the distance localization efficiency of the GTL algorithm has been improved by using the wavelet de-noising method.

The thermocline has an important influence on sound propagation or underwater acoustic detection in shallow water. The beam-displacement ray-mode theory is used to analyze the effect of thermocline on underwater sound propagation. The peculiarity of this method is that the boundary effects on the sound field can be expressed by the equivalent boundary reflection coefficient. An improved new method for computing the upper boundary reflection coefficient in the theory is proposed based on the generalized phase integral eigen function and an univocal formulation of upper boundary reflection coefficient is deduced. An ocean environment model of thermocline is established. The effect of thermocline’s depth, thickness and intensity on sound transmission loss is discussed. The research result shows that thermocline has an important effect on sound transmission loss. It is of practical guiding significance in sonar detection and underwater warfare.

The beam-displacement ray-mode theory is a normal mode method for propagation modeling in common horizontally stratified shallow water. Computation of the reflection coefficient for unbounded media is the key of the theory. In this paper, on the basis of the WKBZ eigenfunction, a kind of algorithm for computing the reflection coefficient for unbounded media is proposed. And the algorithm is universal to any sound speed profile with complex surface layer. Numerical simulations show that the beam-displacement ray-mode theory results with this algorithm are in good agreement with the KRAKEN normal mode results, and computational speed of this algorithm excels that of the other methods.

Automatic target recognition (machine recognition) finds wide application in many fields, which is of important value in the civil and military application. Feature extraction of target information is researched such as the demodulation line spectrum feature, power spectrum feature and low-frequency line spectrum feature of ship radiated-noise, which is key technique of ship target recognition. Furthermore, software design question of the ship target recognition system is analysed.

A RBF (Back-Propogation) neural networks based feature is applied to the target recognition, which aims at only recognition of the target feature and searches the hyperplane of the local space taking the target feature as center. The classifier integrates the target feature with RBF ANNs. It evaluates importance of each sample to the target feature by using expected output of the dynamic ANNs training process. Experiment results show that it is more robust than the traditional method.

A dynamic support vector machine method is applied to the target recognition using noise power spectrum. It searches the optimal separating hyperplane of the local space taking the target feature as center. To show better importance of each sample to the target feature, the penalty function is measured by using the distance between the target feature and each training sample.

Computational ocean acoustics is an important aspect of oceanographic physics. In this paper, a new ocean acoustic model is developed in computational oceanographic physics, which is called ray-mode-parabolic equation (RMPE) theory. The RMPE solution is expressed in terms of the normal modes in vertical direction and the mode coefficients in horizontal direction. The model is based on the beam-displacement ray- mode (BDRM) theory and the parabolic equation (PE) method. By using the BDRM theory, the normal mode analysis can be processed efficiently and rapidly. The PE method is used to solve the wave equations for mode coefficients. The numerical simulations on sound propagation and the comparison with experimental data from a sloping-bottom area in South China Sea are discussed. The results show that it is efficient and practical to apply this model to solving problems involing underwater sound propagation in range-dependent ocean.

An underwater acoustic target recognition system is researched. According to characteristic of the ship radiated-noise demodulation line spectrum feature and its training sample set, the target recognition system adopts four methods including expert system, neighbor method, SVM and RBF ANNs. And the target recognition system makes use of advantage of the four methods. Experiment results show that it has better recognition effect.