Physical and geoacoustic properties of surface sediments off eastern Geoje Island, South Sea of Korea

Kim, Gil Young; Kim, Dae Choul; Yoo, Dong Geun; Shin, Bo Kyoung

doi:10.1016/j.quaint.2009.07.028

Cited by 34 publications

(9 citation statements)

References 17 publications

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“…Moreover, these parameters are vital for the theory of wave propagation in unconsolidated seafloor sediment [9][10][11][12][13][14][15][16]. In addition, these parameters can be used to evaluate the geological events of marine sedimentary environments and control seismoacoustic propagation in the ocean [17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

“…In recent decades, extensive studies have been conducted to reveal the relationships between compression waves and physical properties and textural parameters relative to various regions, such as the North Pacific [17], Yellow Sea of China [2,8,[24][25][26], South China Sea (SCS) [7,14,15,23,27,28], South Sea of Korea [12,19,20,22,30], West coast of India [31], and West coast of Scotland [32]. These relationships show that the density, porosity, and mean grain size play a central role in determining the compression wave speed of seafloor sediment [33,34].…”

Section: Introductionmentioning

confidence: 99%

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Effects of physical properties on the compression wave speed of seafloor sediment in the South China Sea: Comparisons between theoretical models and measured data

Tian

Chen²,

Mo³

et al. 2023

Front. Phys.

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The compression wave speed and physical properties of seafloor sediments are significant in marine engineering and marine acoustics. Although most regression equations can be satisfactorily fitted to the data and provide a useful predictive method, there is little insight into the influence of physical properties on the compression wave speed of seafloor sediment in the South China Sea (SCS). To characterize the compression wave speed and physical properties of seafloor sediments, the compression wave speed, porosity, density, and mean grain size were measured and calculated for forty-two samples collected from the South China Sea. The results show that the RMS roughness of seafloor sediment in the South China Sea ranges from 0.2 to 15 μm, and the porosity and density are determined by the RMS roughness and mean grain size. The relationships between the compression wave speed and physical properties of seafloor sediments are consistent with the intergranular friction model. When we have the physical parameters but no compression wave speed, therefore, the intergranular friction model can be used to predict the compression wave speed of seafloor sediment. Compared with the Wood model curve, we also reveal that the intergranular friction of seafloor sediment is an important parameter in determining the compression wave speed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effects of physical properties on the compression wave speed of seafloor sediment in the South China Sea: Comparisons between theoretical models and measured data

Tian

Chen²,

Mo³

et al. 2023

Front. Phys.

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“…Geoacoustic models based on physical properties have been established to study the sediment-geoacoustic relationships (Hamilton, 1980;Huang et al, 2018;Kim et al, 2011;Wang et al, 2016;Zhou et al, 2009). However, there is variability of sediments properties, especially for sediments in different regions with different properties.…”

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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“…The Coastal Benthic Boundary Layer project measured the top 40 cm of sediment and the results indicated that the sound speed and attenuation exhibited vertical and lateral variability [6]. Kim et al [22] classified the seafloor of the South Sea shelf into four areas based on the distribution of grain size, and established the prediction equation of sound speed. Yang and Seong [38] presented the sound speed and attenuation data as a function of frequency and grain size distribution.…”

Section: Introductionmentioning

confidence: 99%

A New Compressional Wave Speed Inversion Method Based on Granularity Parameters

Wang

Hou

et al. 2019

IEEE Access

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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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Physical and geoacoustic properties of surface sediments off eastern Geoje Island, South Sea of Korea

Cited by 34 publications

References 17 publications

Effects of physical properties on the compression wave speed of seafloor sediment in the South China Sea: Comparisons between theoretical models and measured data

Effects of physical properties on the compression wave speed of seafloor sediment in the South China Sea: Comparisons between theoretical models and measured data

Sound Velocity Predictive Model Based on Physical Properties

A New Compressional Wave Speed Inversion Method Based on Granularity Parameters

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