Αποτελέσματα Αναζήτησης
31 Μαΐ 2024 · 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.
- The Effects of Sound Speed Profile to the Convergence Zone in Deep Water
The structure of a sound speed profile (SSP) in deep water...
- A Multi-Spatial Scale Ocean Sound Speed Prediction Method Based on Deep ...
As sound speed is a fundamental parameter of ocean acoustic...
- The Effects of Sound Speed Profile to the Convergence Zone in Deep Water
1 Αυγ 2018 · Sound Speed Profile (SSP) is the key factor affecting underwater acoustics and it is of great value to obtain SSP in near real-time. In this paper, the sea surface data were used to reconstruct the SSP with the single empirical orthogonal function regression (sEOF-r) method in a global scale.
1 Αυγ 2020 · This article introduces and evaluates a cost efficient method for reconstructing the full-depth SSP, which is to launch Expendable Bathythermograph (XBT) or Expendable Conductivity Temperature Depth (XCTD) underway, and jointly use the World Ocean Atlas 2018 (WOA2018).
Generally, oceanographic parameters include temperature, salinity (Salinometer, CTD), speed and direction of ocean currents (electromagnetic or ultrasonic current meters), wave height and periods...
15 Μαρ 2022 · The structure of a sound speed profile (SSP) in deep water causes refraction of sound rays and Convergence Zones (CZs) of high intensity where the rays focus at shallow depth. Study of sound field characteristics in the CZs has always been the focus of deep-water acoustics research.
22 Μαρ 2022 · Having quantified the sound speed variations in each target area and for multiple depth levels, we found that climate-change-induced sound speed variations are substantial, reaching up to 1.5% (approximately 20 m/s) and exceeding the SS seasonal variability.
2 ημέρες πριν · As sound speed is a fundamental parameter of ocean acoustic characteristics, its prediction is a central focus of underwater acoustics research. Traditional numerical and statistical forecasting methods often exhibit suboptimal performance under complex conditions, whereas deep learning approaches demonstrate promising results. However, these methodologies fall short in adequately addressing ...
