High-Frequency Dependence of Acoustic Properties of Three Typical Sediments in the South China Sea

Wang, Jingqiang; Hou, Zhengyu; Li, Guanbao; Kan, Guangming; Liu, Baohua; Meng, Xianwei; Hua, Qingfeng; Sun, Liguang

doi:10.3390/jmse10091295

Cited by 5 publications

(11 citation statements)

References 20 publications

(35 reference statements)

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“…Generally, porosity decreases gradually with increasing particle size. Figure 7 illustrates the comparison between the empirical relation of sound speed of the seabed sediment [28], the sound speed predicted by the Biot-Stoll model, and the measured sound speed. It is evident that there is a significant discrepancy between the measured and predicted values of the Biot-Stoll model.…”

Section: Establish the Relationship Between Seabed Reflection Coeffic...mentioning

confidence: 99%

“…In 2022, Wang et al established the relationship between sound speed (V) and frequency (f) under different porosity (n) (n = 0.482, 0.550, 0.694), as depicted in Figure 8a. Then, based on the same method and the measured data of the original sample [28], we add some relationships between sound speed and frequency at different porosity (between 0.48 and 0.8); refer to Table 2. Given that the primary frequency of the sub-bottom profile in the study area is approximately 4 kHz, the relationship between the sound speed at 4 kHz and particle porosity can be further established by extracting the particle porosity value under the condition of 4 kHz (V = 2084n 2 − 3194n + 2655, R 2 = 0.9975), as shown in Figure 8b.…”

Section: Establish the Relationship Between Seabed Reflection Coeffic...mentioning

confidence: 99%

“…However, there are limitations in studying the acoustic characteristics of local substrates. In contrast, the sediment sound speed prediction formulas established for different sea areas have demonstrated high accuracy in their respective regions [26][27][28].…”

Section: Introductionmentioning

confidence: 98%

“…They analyzed the common properties of submarine sediments in this region. Wang et al performed a physical analysis of three fine-grained sediments in the South China Sea [28]. They measured the high-frequency acoustic characteristics of these sediments and compared their acoustic properties using the Biot-Stoll model.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 7. Comparison of the empirical relationship between the sound speed of the seabed sediments[28], the sound speed of the Biot-Stoll model and the measured sound speed[28] (sediment type is silty clay, particle porosity is 0.694).…”

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confidence: 99%

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Inversion of Sub-Bottom Profile Based on the Sediment Acoustic Empirical Relationship in the Northern South China Sea

Zhou,

Li,

Zheng

et al. 2024

Remote Sensing

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This study focuses on the inversion of sub-bottom profile (SBP) data in the northern South China Sea using an empirical relationship derived from sediment acoustic data. The sub-bottom profile is primarily utilized for various marine applications, such as geological mapping and resource exploration. In this research, we present a study conducted in the northern slope canyon of the South China Sea. Firstly, we obtained the seabed reflection coefficient from sub-bottom profiles obtained by the autonomous underwater vehicle (AUV) detection system. Secondly, we utilized the acoustic empirical relationship in the northern South China Sea to establish relationship equations between the seabed reflection coefficient and the porosity, density, and average particle size of the sediment at a main frequency of 4 kHz (the AUV shallow profile main frequency). Then, using these equations, we were able to invert the physical parameters such as porosity, density, and average particle size of the seabed surface sediments. Finally, the inverted results are compared and analyzed by using the sediment samples test data. The overall deviation rate of the inverted physical parameters is within the range of ±10% when compared. The inverted results closely match the measured values, accurately reflecting the dynamic changes in the physical properties of seabed surface sediments. Notably, the average grain size is a direct indicator of the sediment particles size with smaller particles found in deeper water. The variation characteristics of sediment physical parameters align well with the variation of sediment types in the canyon, which is consistent with changes in the water depth, topography, and hydrodynamic conditions of the area. This further demonstrates the reliability of the inversion results.

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Section: Establish the Relationship Between Seabed Reflection Coeffic...mentioning

confidence: 99%

Section: Establish the Relationship Between Seabed Reflection Coeffic...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Inversion of Sub-Bottom Profile Based on the Sediment Acoustic Empirical Relationship in the Northern South China Sea

Zhou,

Li,

Zheng

et al. 2024

Remote Sensing

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Fine Sand and Clay Sediment Acoustic Properties of the Novel Sediment Sample from the Arabian Sea: Experimental Investigations and Biot–Stoll Model Validation

Shaikh,

Huang,

Zhang

et al. 2024

China Ocean Eng

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Time difference auto‐extraction methods for in situ sound speed measurements in seafloor sediments

Hua,

Wang,

et al. 2024

Geophysical Prospecting

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The in situ acoustic measurement of seafloor sediment is an important technical means to obtain the acoustic parameters of seafloor. The time‐of‐flight method is commonly used to calculate the sound speed in seafloor sediment. Accurate identification of signal feature points is essential for determining travel time or travel time difference of acoustic signals. However, the precise identification of feature points, such as the take‐off point of the first wave of a sound wave signal, is challenging. The conventional manual identification method is inefficient and prone to errors. The development of a feature point auto‐identification method is imperative for accurately calculating the travel time of acoustic signals. In this study, we employed the cross‐correlation method, the level threshold method and the short window‐long window energy ratio method to extract the acoustic travel time differences and calculate the sound speeds in seawater and in seafloor sediment. We then analysed the effectiveness of these calculated results. The sound speeds in seawater obtained through the aforementioned methods were compared with the sound speeds measured using a sound velocity profiler. The comparison revealed that these processing methods exhibit a high level of accuracy. The sound speed results in sediments show that the programme‐based auto‐identification methods significantly reduce the standard deviation compared to the manual identification method. This study successfully assessed the processing accuracy of different methods and expanded the processing methods for in situ acoustic signals of seafloor sediments.

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High-Frequency Dependence of Acoustic Properties of Three Typical Sediments in the South China Sea

Cited by 5 publications

References 20 publications

Inversion of Sub-Bottom Profile Based on the Sediment Acoustic Empirical Relationship in the Northern South China Sea

Inversion of Sub-Bottom Profile Based on the Sediment Acoustic Empirical Relationship in the Northern South China Sea

Fine Sand and Clay Sediment Acoustic Properties of the Novel Sediment Sample from the Arabian Sea: Experimental Investigations and Biot–Stoll Model Validation

Time difference auto‐extraction methods for in situ sound speed measurements in seafloor sediments

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