The Effects of Temperature, Salinity and Pressure on Sound Speed Structure in the Coastal Waters of Babolsar

2014 ◽  
Vol 2 (2) ◽  
pp. 19
Author(s):  
Siamak Jamshidi ◽  
Mahsa Farzadnia ◽  
Mohammad Motevalli
Keyword(s):  
Coastal Waters ◽  
Sound Speed ◽  
Effects Of Temperature ◽  
Sound Speed Structure

Sound speed variation in the coastal waters off Cochin and signature of subsurface maxima

2021 ◽  
Author(s):  
Abdul Azeez S ◽  
Revichandran C ◽  
Muraleedharan K. R ◽  
Sebin John ◽  
Seena G ◽  
...  
Keyword(s):  
Coastal Waters ◽  
Sound Speed ◽  
Speed Variation

Effects of temperature, salinity and their interaction on growth of toxic Ostreopsis sp. 1 and Ostreopsis sp. 6 (Dinophyceae) isolated from Japanese coastal waters

2013 ◽  
Vol 79 (2) ◽  
pp. 285-291 ◽  
Author(s):  
Yuko Tanimoto ◽  
Haruo Yamaguchi ◽  
Takamichi Yoshimatsu ◽  
Shinya Sato ◽  
Masao Adachi
Keyword(s):  
Coastal Waters ◽  
Effects Of Temperature

Observations of thermohaline sound speed structure in the Beaufort Sea in the summer of 2015

2015 ◽  
Vol 138 (3) ◽  
pp. 1743-1743
Author(s):  
Dominic DiMaggio ◽  
Annalise Pearson ◽  
John A. Colosi
Keyword(s):  
Sound Speed ◽  
Beaufort Sea ◽  
Sound Speed Structure

Correction for effects of temperature and pressure on sound speed in shallow seafloor sediments

2019 ◽  
Vol 37 (10) ◽  
pp. 1217-1226 ◽  
Author(s):  
Guangming Kan ◽  
Dapeng Zou ◽  
Baohua Liu ◽  
Jingqiang Wang ◽  
Xiangmei Meng ◽  
...  
Keyword(s):  
Sound Speed ◽  
Effects Of Temperature ◽  
Temperature And Pressure ◽  
Seafloor Sediments

scholarly journals GARPOS: Analysis Software for the GNSS‐A Seafloor Positioning With Simultaneous Estimation of Sound Speed Structure

2020 ◽  
Vol 8 ◽  
Author(s):  
Shun-ichi Watanabe ◽  
Tadashi Ishikawa ◽  
Yusuke Yokota ◽  
Yuto Nakamura
Keyword(s):  
Sound Speed ◽  
Empirical Bayes ◽  
Information Criterion ◽  
Satellite System ◽  
Observation Equation ◽  
Simultaneous Estimation ◽  
Acoustic Ranging ◽  
Observation Network ◽  
Sound Speed Structure ◽  
Global Navigation Satellite

Global Navigation Satellite System–Acoustic ranging combined seafloor geodetic technique (GNSS-A) has extended the geodetic observation network into the ocean. The key issue for analyzing the GNSS-A data is how to correct the effect of sound speed variation in the seawater. We constructed a generalized observation equation and developed a method to directly extract the gradient sound speed structure by introducing appropriate statistical properties in the observation equation, especially the data correlation term. In the proposed scheme, we calculate the posterior probability based on the empirical Bayes approach using the Akaike’s Bayesian Information Criterion for model selection. This approach enabled us to suppress the overfitting of sound speed variables and thus to extract simpler sound speed field and stable seafloor positions from the GNSS-A dataset. The proposed procedure is implemented in the Python-based software “GARPOS” (GNSS-Acoustic Ranging combined POsitioning Solver).

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