Correlations between the in situ acoustic properties and geotechnical parameters of sediments in the Yellow Sea, China

Liu, Baohua; Han, Tongcheng; Kan, Guangming; Li, Guanbao

doi:10.1016/j.jseaes.2013.07.040

Cited by 45 publications

(21 citation statements)

References 26 publications

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“…This conclusion is consistent with predecessors' research results (Anderson 1974;Hamilton 1980;Orsi and Dunn 1990;Liu et al 2013), and they both believe that porosity is the most important factor for sound speed.…”

Section: Discussionsupporting

confidence: 91%

“…The empirical approach has significant advantages, thus empirical regressions are the preferred means of relating acoustic and physical properties (Jackson and Richardson 2007). Regressions among sediment physical properties (porosity, bulk density, mean grain size) and acoustic properties (sound speed and attenuation) have been developed by numerous authors (Hamilton 1970(Hamilton , 1971(Hamilton , 1980Anderson 1974;Hamilton and Bachman 1982;Briggs and Richardson 1997;Richardson et al 2002;Lu et al 2008;Kan et al 2011;Liu et al 2013). Many of these regressions are single parameter equations, which ignore the interactions of each parameter.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of the influences of physical parameters on sediment sound speed

Hou

Guo

Wang

et al. 2016

Marine Georesources & Geotechnology

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Based on the core samples collected in different geological tectonic units in the southern South China Sea, the double parameter equations between sediment sound speed and physical parameters were developed. According to the double parameter equations, the influences of physical parameters (porosity, mean grain size, wet bulk density, and clay content) on sediment Downloaded by [University of Lethbridge] at 11:47 22 June 2016 3sound speed were analyzed. In order to analyze the sensitivity of each parameter in the double parameter equations, the error norm method was introduced. The most influencing physical parameters with relative order were analyzed using the error norm method. These results show that porosity has a major influence on the sound speed, and the most influential physical parameters on sound speed are as follows: porosity > wet bulk density > clay content > mean grain size.

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Analysis of the influences of physical parameters on sediment sound speed

Hou

Guo

Wang

et al. 2016

Marine Georesources & Geotechnology

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“…How to obtain sediment sound velocity quickly and accurately is a key issue in geoacoustic research. Typically, sampling or in situ measurements, and established geoacoustic models or empirical equations, are the main methods to obtain the geoacoustic properties of the sediments (Gorgas et al, 2003;Liu et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Sound Velocity Predictive Model Based on Physical Properties

Hou

Wang

Chen

et al. 2019

Earth and Space Science

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The correlation between sediment sound velocity (V) and physical properties has been studied for 60 years using empirical equations, and it has been found difficult to predict V accurately. Random Forest (RF) is a scientific discipline and a method of data analysis that automates analytical model building. Here we present the implementation of RF algorithm in V prediction and sediment classification. The databases were from previously collected data in the northern South China Sea. The goal of this study is to establish a predictive model based on RF using multiple physical properties (mean grain size, porosity, wet bulk density, and water content). Compared to empirical equations, the average error of RF velocity is only 0.95%, ranging from 0.03 to 2.73%, indicating that the RF algorithm has improved the accuracy of V prediction. We also used mean decrease impurity importance to evaluate the importance of a variable and found that the most important feature in the predictive model is mean grain size. We also used the RF as a potentially useful tool for sediment classification. The classification model has up to 75% accuracy in the dataset. Multiple features, such as physical properties, sedimentary environment, and sediment source, affect the geoacoustic properties of sediments. The next goal is to use multiple features to improve the model and further improve the accuracy of sound velocity prediction and sediment classification.

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“…Lu et al [16,17] conducted experiments in the southeastern coastal areas of China (including the South China Sea, Taiwan Strait, and continental shelf of the East China Sea) and established empirical equations for the sound velocity of seafloor sediments in these study areas. Kan et al [18] and Liu et al [19] used an in situ system to study sediment acoustics in the Yellow Sea, and established empirical equations for the relationships between sediment characteristics and acoustic properties there.…”

Section: Introductionmentioning

confidence: 99%

Seafloor Sediment Study from South China Sea: Acoustic & Physical Property Relationship

et al. 2015

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Seafloor sediments of different geographical areas in the southern South China Sea (continental shelf, continental slope, and Okinawa Trough) were gravity cored at 21 locations. Sound velocities (V) of the samples were measured at 15-cm increments immediately upon retrieval, and porosity, wet bulk density, and mean grain size were measured later in the laboratory. Empirical equations from previous studies were applied to predict V of sediment samples from the measured physical properties and it was found that the sound velocities derived from the existing equations did not closely match the measured sound velocities. Therefore empirical equations were reconstructed based on the measured data that represent the relationships between physical and acoustic properties of the different geographical area in the study area. Possible explanations for the discrepancies between the measured data and those of previous studies were investigated and found that physical properties, sediment types, geographical area, etc. are important factors that influence sound velocity. The empirical equations of this report should be preferred for prediction of sediment sound velocity for high-frequency acoustic experiments.

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Correlations between the in situ acoustic properties and geotechnical parameters of sediments in the Yellow Sea, China

Cited by 45 publications

References 26 publications

Analysis of the influences of physical parameters on sediment sound speed

Analysis of the influences of physical parameters on sediment sound speed

Sound Velocity Predictive Model Based on Physical Properties

Seafloor Sediment Study from South China Sea: Acoustic & Physical Property Relationship

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