Sound velocity and related properties of seafloor sediments in the Bering Sea and Chukchi Sea

Meng, Xianwei; Li, Guanbao; Han, Guoyong; Kan, Guangming

doi:10.1007/s13131-015-0669-9

Cited by 11 publications

(17 citation statements)

References 17 publications

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“…A two-layer profile of temperature and salinity is often present, with a cold (~ − 2° C) relatively fresh upper layer during periods with ice cover, saltier relatively warmer underlying water column, and a weak thermocline 51 , 52 . The seafloor consists of relatively thick fine silt, sand, and clay, especially on the shallow shelf 53 .…”

Section: Methodsmentioning

confidence: 99%

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The influence of sea ice on the detection of bowhead whale calls

Jones

Hildebrand

Thayre

et al. 2022

Sci Rep

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Bowhead whales (Balaena mysticetus) face threats from diminishing sea ice and increasing anthropogenic activities in the Arctic. Passive acoustic monitoring is the most effective means for monitoring their distribution and population trends, based on the detection of their calls. Passive acoustic monitoring, however, is influenced by the sound propagation environment and ambient noise levels, which impact call detection probability. Modeling and simulations were used to estimate detection probability for bowhead whale frequency-modulated calls in the 80–180 Hz frequency band with and without sea ice cover and under various noise conditions. Sound transmission loss for bowhead calls is substantially greater during ice-covered conditions than during open-water conditions, making call detection ~ 3 times more likely in open-water. Estimates of daily acoustic detection probability were used to compensate acoustic detections for sound propagation and noise effects in two recording datasets in the northeast Chukchi Sea, on the outer shelf and continental slope, collected between 2012 and 2013. The compensated acoustic density suggests a decrease in whale presence with the retreat of sea ice at these recording sites. These results highlight the importance of accounting for effects of the environment on ambient noise and acoustic propagation when interpreting results of passive acoustic monitoring.

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Section: Methodsmentioning

confidence: 99%

“…Bathymetric data were extracted from the International Bathymetric Chart of the Arctic Ocean 57 at a spatial resolution of 1 km and we adopted seafloor acoustic properties and a sub-bottom sound speed profile from previous studies on the Chukchi Shelf 53 , 58 .…”

Section: Methodsmentioning

confidence: 99%

The influence of sea ice on the detection of bowhead whale calls

Jones

Hildebrand

Thayre

et al. 2022

Sci Rep

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“…The sound speed and physical mechanical properties of the collected sediment samples were measured at the Seafloor Acoustic Laboratory in First Institute of Oceanography, Ministry of Natural Resources with a measurement frequency of 100 kHz, using the longitudinal transmission method. The measurement equipment includes a seafloor sediment cylindrical sample sound speed measurement platform, an integrated sediment acoustic measurement system and acoustic transducers [15]. The formula for calculating seafloor sediment sound speed is as follows:…”

Section: Data Sourcementioning

confidence: 99%

“…Furthermore, the research of seafloor sediment acoustic properties represents the forefront and focal points of marine acoustics, marine geology and marine geophysics. In general, technologies for obtaining the acoustic properties of seafloor sediments mainly include the in situ measurement technique, laboratorial measurement technique, acoustic inversion technique and model-based prediction technique [12][13][14][15]. The in situ measurement technique, laboratorial measurement technique and acoustic inversion technique all usually use instruments and equipment to obtain the seafloor sediment acoustic properties on site at sea or in the laboratory.…”

Section: Introductionmentioning

confidence: 99%

Prediction Model of the Sound Speed of Seafloor Sediments on the Continental Shelf of the East China Sea Based on Empirical Equations

Kan,

Lu,

Meng

et al. 2023

JMSE

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Based on the data of the acoustic and physical properties of seafloor sediments obtained on the continental shelf area of the East China Sea (ECS), prediction equations of the sediment sound speed based on single and dual parameters were established, and the correlation of the sound speed with physical parameters was discussed. The results show that the sediment sound speed (c) is strongly correlated with water content (w), density (ρ), void ratio (e), mean grain size (Mz) and median grain size (Md), and the coefficient of determination R2 of the empirical regression equation is generally greater than 0.80, while the empirical regression equation coefficient of determination R2 of the compression coefficient (a) and compression modulus (E) are slightly lower, with 0.79 and 0.73, respectively. The coefficients of determination of the dual parameter regression equations between sediment sound speed with physical property parameters are generally higher than those of the single parameter equations, which are all higher than 0.90 indicating better prediction performance. The sensitivity of the physical parameters to the sound speed was analyzed, and the result shows that the sequence of sensitivity from high to low influence on sediment sound speed is void ratio, density, compressibility coefficient, median grain size and mean grain size. The prediction equations established in this study are a good extension and supplement to marine geoacoustic models and is of great significance for obtaining the acoustic properties of the seafloor sediments on the shelf of the East China Sea.

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“…The uni-model grain size distribution was established [7], which assumed that the porosity of an unconsolidated sediment is dependent on the grain size. In the case of coarse particles, the large pore space between aggregates increases the porosity, resulting in a high water content and low density [26]. Some studies have established the quadratic equations to predict the sound speed of the sediments based on the physical properties measured in different sedimentary environments [29].…”

Section: Introductionmentioning

confidence: 99%

A New Compressional Wave Speed Inversion Method Based on Granularity Parameters

Wang

Hou

et al. 2019

IEEE Access

Self Cite

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How to improve the prediction accuracy of compressional wave speed has always been one of the basic research subjects in geoacoustics study field. Due to the stability of granularity, whether in the laboratory or in the seabed environment, the regression relationship between compressional wave speed and granularity is an important sound speed inversion method. Machine Learning (ML) provides a new solution for more efficient sound speed prediction systems. In this study, two ML algorithm, Random forest (RF) and Support Vector Regression (SVR), combined with nine granularity parameters (mean grain size, median grain size, skewness, kurtosis, sorting coefficient, gravel, sand, silt, and clay content respectively.) to analysis the effect of granularity property on sound speed. As a result, the sound speed-granularity predictive models were established, and the sound speed accuracy obtained based on the predictive models are higher than that of the regression equations, and the RF model has a higher accuracy than the SVR model. Based on the RF predictive model, the feature selection was conducted and the results show that the most influential parameter of granularity is mean grain size. Furthermore, the RF model can also predict the sound speed with high precision in the absence of partial parameters, which can be a useful tool for ocean engineering and seismic inversion.INDEX TERMS Geoacoustic inversion, granularity, machine learning, random forest, sound speed.

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Sound velocity and related properties of seafloor sediments in the Bering Sea and Chukchi Sea

Cited by 11 publications

References 17 publications

The influence of sea ice on the detection of bowhead whale calls

The influence of sea ice on the detection of bowhead whale calls

Prediction Model of the Sound Speed of Seafloor Sediments on the Continental Shelf of the East China Sea Based on Empirical Equations

A New Compressional Wave Speed Inversion Method Based on Granularity Parameters

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