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Compressional and shear wave velocities of polycrystalline stishovite (SiO2) have been measured at simultaneous high pressures and temperatures up to 14.5 GPa and 800°C. By fitting velocities to the finite strain equations, the elastic moduli and density were determined to be KS0 = 306.6(46) GPa, KS′ = 4.92(10), ∂KS/∂T = −0.024(1) GPa/K, G0 = 229.0(34) GPa, G′ = 1.07(10), ∂G/∂T = −0.017(1) GPa/K, ρ0 = 4.287(2) g/cm3. Our modeling suggested that, in the eclogite, coesite‐stishovite transition can increase P and S wave velocities by 2.4% and 3.5%, respectively. A comparison between geophysical observations and our model shows that the coesite‐stishovite phase transition in the eclogite can potentially be responsible for the occurrence of the X discontinuity beneath Hawaii. In addition, our current results suggest an eclogite‐rich layer between 340 and 450 km depth beneath Hawaii. The eclogite concentration at the top and bottom of the layer is 41–55 vol% and >77 vol%, respectively. Plain Language Summary In this study, we investigated the elastic behavior of stishovite, a high‐pressure mineral found in subducted oceanic crust, under simultaneous high pressure and high temperature. By measuring compressional and shear wave velocities of polycrystalline stishovite at pressures up to 14.5 GPa and temperatures up to 800°C, we determined elastic modulus for stishovite. Using current data, we developed a model to predict seismic wave velocities changes in the subducted oceanic crust known as eclogite. According to our model, the coesite‐stishovite phase transition can lead to a 2.4% and 3.5% increase in P and S wave velocities of eclogite, respectively. In addition, we compared it with geophysical observations, particularly focusing on the X discontinuity beneath Hawaii. Our result indicates the presence of an eclogite‐rich layer beneath Hawaii, extending from 340 to 450 km in depth. The concentration of eclogite at the top and bottom of this layer varies, with values ranging from 41% to 55% at approximately 336 km and exceeding 77% at around 448 km depth. Key Points Direct measurement of P and S wave velocities of stishovite at mantle pressures and temperatures In the eclogite, coesite‐stishovite transition can result in seismically detectable first order increase in P and S velocities An eclogite‐rich layer model can interpret the seismic X‐discontinuity in Hawaii area

To solve the problem of SVP (Sound velocity profiles) representative error caused by the difficulty of obtaining continuous time series SVP in deep-sea operations, an SVP timing prediction method combining EMD (Empirical mode decomposition) and NARX (Nonlinear autoregressive neural network with external input) is proposed. To begin with, the time-series SVP are stratified according to different depths, and the time-series variation curves of sound velocity at different depths are obtained; Furthermore, the EMD is used to decompose the time series variation curve of sound velocity into multiple IMF (Intrinsic mode function) components, each component contains local characteristic signals of different time scales of the original signal. The NARX is used to establish a prediction model for each IMF components, and the prediction values of the sound velocity at different depths are obtained. The EMD-NARX, NARX and polynomial fitting model are analyzed and verified by Argo buoys data in the South China Sea, and the results of the experiment show that EMD-NARX improves the time series prediction accuracy of sound velocity by 32.24% and 65.15% compared with NARX and polynomial fitting, respectively, so EMD-NARX has a good prediction effect on the time series SVP of the deep sea.

The scarcity of in-situ ocean observations poses a challenge for real-time information acquisition in the ocean. Among the crucial hydroacoustic environmental parameters, ocean sound velocity exhibits significant spatial and temporal variability and it is highly relevant to oceanic research. In this study, we propose a new data-driven approach, leveraging deep learning techniques, for the prediction of sound velocity fields (SVFs). Our novel spatiotemporal prediction model, ST-LSTM-SA, combines Spatiotemporal Long Short-Term Memory (ST-LSTM) with a self-attention mechanism to enable accurate and real-time prediction of SVFs. To circumvent the limited amount of observational data, we employ transfer learning by first training the model using reanalysis datasets, followed by fine-tuning it using in-situ analysis data to obtain the final prediction model. By utilizing the historical 12-month SVFs as input, our model predicts the SVFs for the subsequent three months. We compare the performance of five models: Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Convolutional LSTM (ConvLSTM), ST-LSTM, and our proposed ST-LSTM-SA model in a test experiment spanning 2019 to 2022. Our results demonstrate that the ST-LSTM-SA model significantly improves the prediction accuracy and stability of sound velocity in both temporal and spatial dimensions. The ST-LSTM-SA model not only accurately predicts the ocean sound velocity field (SVF), but also provides valuable insights for spatiotemporal prediction of other oceanic environmental variables.

The accuracy of GNSS/Acoustic seafloor geodetic calibration is greatly influenced by the temporal variation of sound velocity, especially in the vertical direction. Aiming at correcting of the unknown parameters related to both the positions and the sound velocity, this paper proposes a step-by-step inversion scheme based on moving survey data. The proposed method firstly estimates the horizontal normalized travel time delay with sound ray tracing strategy and then computes the horizontal position with circle line observations. We reconstructed an inversion scheme for extracting the surface sound velocity disturbance (SSVD) and corrected the vertical position from cross line data. The SSVD is decomposed into a sum of different period disturbances, and a new SSVD is reconstructed by combining the long period disturbance and short period disturbance. The proposed algorithm is verified by the South China Sea experiment for GNSS/Acoustic seafloor geodetic calibration. The results demonstrate that the new method can take the effects of sound velocity variation into consideration and improve the precision of the vertical position, which is superior to the least squares (LS), the single-difference LS for seafloor geodetic calibration.

A series of (MS)1+x(TiS2)2 (M = Pb, Bi, Sn) misfit layer compounds are proposed as bulk thermoelectric materials. They are composed of alternating rock-salt-type MS layers and paired trigonal anti-prismatic TiS2 layers with a van der Waals gap. This naturally modulated structure shows low lattice thermal conductivity close to or even lower than the predicted minimum thermal conductivity. Measurement of sound velocities shows that the ultra-low thermal conductivity partially originates from the softening of the transverse modes of lattice wave due to weak interlayer bonding. Combined with a high power factor, the misfit layer compounds show a relatively high ZT value of 0.28~0.37 at 700 K.

Underwater localization is one of the key techniques for positioning, navigation, timing (PNT) services that could be widely applied in disaster warning, underwater rescues and resource exploration. One of the reasons why it is difficult to achieve accurate positioning for underwater targets is due to the influence of uneven distribution of underwater sound velocity. The current sound-line correction positioning method mainly aims at scenarios with known target depth. However, for nodes that are non-cooperative nodes or lack depth information, sound-line tracking strategies cannot work well due to non-unique positional solutions. To solve this problem, we propose an iterative ray tracing 3D underwater localization (IRTUL) method for stratification compensation. To demonstrate the feasibility of fast stratification compensation, we first derive the signal path as a function of initial |grazing angle, and then prove that the signal propagation time and horizontal propagation distance are monotonic functions of the initial grazing angle, which guarantees the fast achievement of ray tracing. Simulation results indicate that IRTUL has the most significant correction effect in the depth direction, and the average accuracy has been improved by about 3 m compared to a localization model with constant sound velocity.

Marine researchers rely heavily on ocean sound velocity, a crucial hydroacoustic environmental metric that exhibits large geographical and temporal changes. Nowadays, spatio-temporal series prediction algorithms are emerging, but their prediction accuracy requires improvement. Moreover, in terms of ocean sound speed, most of these models predict an ocean sound speed profile (SSP) at a single coordinate position, and only a few predict multi-spatial-scale SSPs. Hence, this paper proposes a new data-driven method called STA-Conv-LSTM that combines convolutional long short-term memory (Conv-LSTM) and spatio-temporal attention (STA) to predict SSPs. We used a 234-month dataset of monthly mean sound speeds in the eastern Pacific Ocean from January 2004 to June 2023 to train the prediction model. We found that using 24 months of SSPs as the inputs to predict the SSPs of the following month yielded the highest accuracy. The results demonstrate that STA-Conv-LSTM can achieve predictions with an accuracy of more than 95% for both single-point and three-dimensional scenarios. We compared it against recurrent neural network, LSTM, and Conv-LSTM models with optimal parameter settings to demonstrate the model’s superiority. With a fitting accuracy of 95.12% and the lowest root-mean-squared error of 0.8978, STA-Conv-LSTM clearly outperformed the competition with respect to prediction accuracy and stability. This model not only predicts SSPs well but also will improve the spatial and temporal forecasts of other marine environmental factors.

A prediction model of the sound velocity and sound attenuation of underwater cobalt-rich crusts (CRCs) was established to solve the problem that it is difficult to predict the sound velocity in thickness measurements of cobalt-rich crusts. Based on Biot theory and BISQ theory, a simplified Biot and BISQ model was proposed for the prediction of the sound velocity and sound attenuation of CRCs by using the Kozeny–Carman (KC) equation. The models could calculate the sound velocity and attenuation by the porosity and detection frequency. Based on the physical and mechanical properties of CRCs, a similarity model of the sound velocity and sound attenuation of CRCs was made by using the similarity theory to solve the problem that it is difficult to measure the acoustic propagation characteristics of CRCs. The sound velocity and sound attenuation of CRC similarity models with different porosities were measured by an underwater transmission experiment and the results of the simplified model calculation and experimental measurements were compared. The results showed that the simplified Biot model was suitable for the CRC sound velocity prediction and the simplified BISQ model was suitable for the CRC sound attenuation prediction, which had a high prediction accuracy.

Underwater sound speed plays a vital role in maritime safety. Based on the acousto-optic self-interference effect, we proposed a new method to measure underwater sound speed utilizing Raman–Nath diffraction, generated by the acousto-optic effect between an optical frequency comb and pulsed chirp signal. When the pulsed chirp travels between the measurement and reference arm in the experimental setup that we constructed, the same signal resulting from acousto-optic self-interference is produced. The time gap between the two identical signals represents the time interval. Thus, we can determine the time-of-flight using cross-correlation. The optical path difference between the two arms is double the flight distance of ultrasonic waves and can easily be obtained using femtosecond laser interferometry. The time gap and the distance can be used to measure sound speed. The experimental results show that the chirp signal improves the signal-to-noise ratio and expands the applicable time-of-flight algorithm. The waveform pulse width after cross-correlation is 1.5 μs, compared with 40 μs before. The time-of-flight uncertainty can achieve 1.03 ns compared to 8.6 ns before. Uncertainty of sound velocity can achieve 0.026 m/s.

Iron in ferropericlase experiences a spin crossover from a high spin to a low spin under lower mantle conditions, which generates anomalies in many properties such as the heat capacity and sound velocity. In this study, the effect of the spin crossover on thermal conductivity was evaluated by considering the effects of the spin crossover on P wave velocity and heat capacity at constant volume but ignoring the effect on the mean free path. The spin crossover completely changes the conventional pressure and temperature dependences of the thermal conductivity. The spin crossover can significantly reduce the thermal conductivity of ferropericlase. The pressure dependence of the thermal conductivity of ferropericlase will show a double-valley feature across the spin-crossover region at the appropriate temperature (e.g., 1000 K). In contrast to the conventional decrease in the thermal conductivity with temperature, the thermal conductivity of ferropericlase in the Earth's D\" layer may increase with temperature in some temperature regions. The unusual effect of spin crossover on the thermal conductivity can be expected in other minerals with spin crossover. The spin crossover effect needs serious consideration when estimating the thermal conductivity at the core-mantle boundary.