• The Journal of the Acoustical Society of Korea
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• v.28 no.3
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• pp.165-173
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• 2009

The physical properties of a seafloor sediment have been used as a basic data for the ocean survey. Conventional methods such as a coring, a drilling, and a grabbing have been used to explore the physical properties but these methods have a number of shortcomings as it is time consuming, expensive and spatially limited. To overcome these limitations, seafloor sediment classification using acoustic signals has been studied actively. In this paper, we obtained the backscattered signal from the seafloor sediment using an echo sounder which is one kind of seafloor topography equipment. Nakagami probability density function of the backscattered signals from the seafloor sediment was computed and a Nakagami parameter was compared with the physical properties of the seafloor sediment. We have confirmed that Nakagami parameter, m is correlated with the physical properties of a seafloor sediment. This study will be utilized as a basic data of the seafloor sediment research.

• Ocean Systems Engineering
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• v.14 no.2
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• pp.101-114
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• 2024

Seafloor sediment mapping is an essential research topic in shallow coastal waters, especially in port development, benthic habitat mapping, and underwater communications. The seafloor sediments can be interpreted by collecting sediment samples directly in the field using a grab sampler or corer. Another method is optical, especially using underwater cameras and videos. Both methods each have weaknesses in terms of area coverage (mechanic) and accurate positioning (optic). The latest technology used to overcome it is the acoustic method (echosounder) with Global Navigation Satellite System (GNSS) Real Time Kinematic (RTK) positioning. Therefore, in this study will propose the classification of seafloor sediments in coastal waters using acoustic method that is Multibeam Echosounder (MBES) multi-frequency with five frequency (200 kHz, 250 kHz, 300 kHz, 350 kHz, and 400 kHz). In this study, the deep neural network (DNN) used the bathymetric multi frequency, bathymetric difference inters frequencies, and bathymetric features from 5 (five) frequencies as input layer and 4 (four) sediment types in 74 (seventy-four) sample sediment as output layer to make a seafloor sediment map. Results of sediment mapping using the DNN method show an overall accuracy of 71.6% (significant) and a kappa coefficient of 0.59 (moderate). The distribution of seafloor sediment in the study area is mainly silt (41.6%), followed by clayey sand (36.6%), sandy silt (14.2%), and silty sand (7.5%).

• Korean Journal of Fisheries and Aquatic Sciences
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• v.51 no.3
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• pp.321-327
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• 2018

To classify seafloor sediments using a probabilistic neural network (PNN), the frequency-dependent characteristics of broadband acoustic scattering, which make it possible to qualitatively categorize seabed type, were collected from three different geographical areas in Korea. The echo data samples from three types of seafloor sediment were measured using a chirp sonar system operating over a frequency range of 20-220 kHz. The spectrum amplitudes for frequency responses of 35-75 kHz were fed into the PNN as input feature parameters. The PNN algorithm could successfully identify three seabed types: mud, mud/shell and concrete sediments. The percentage probabilities of the three seabed types being correctly classified were 86% for mud, 66% for mud/shell and 72% for concrete sediment.

• The Journal of the Acoustical Society of Korea
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• v.23 no.4
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• pp.296-302
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• 2004

Acoustic experiments are performed for a seafloor classification from 19 May to 25 May 2003. The six different sites of bottom composition are settled and the bottom reflection losses with frequencies (30, 50, 80. 100, 120 kHz) are measured. Sediment samples were collected using gravity core and the sample was extracted for grain size analysis. The fuzzy logic is used to classify the seabed. In the fuzzy logic. Bottom 1083 model of frequency dependence is used as the input membership functions and the output membership functions are composed of the Wentworth grain size of the bottom. The possibility of the seafloor classification is verified comparing the inversed mean grain size using fuzzy logic with the results of the coring.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.35 no.3
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• pp.273-281
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• 2002

Determination of sediment type is generally based on ground truthing. This method, however, provides information only for the limited sites. Recent developments of remote classification of seafloor sediments made it possible to obtain continuous profiles of sediment types. QTC View system, which is an acoustic instrument providing digital real-time seabed classification, was used to classify seafloor sediment types in the Suyoung Bay, Pusan. QTC View was connected to 50 kHz echo sounder, All parameters of QTC View and echo sounder are uniformly kept during survey. By ground truthing, the sediments are classified into seven types, such as slightly gravelly sand, slightly gravelly sandy mud, gravelly muddy sand, clayey sand, sandy mud, slightly gravelly muddy sand, and rocky bottom. By the first remote classification using QTC View, four sediment types are clearly identified, such as slightly gravelly sand, gravelly mud, slightly gravelly muddy sand, and rocky bottom. These are similar to the result of the second survey. Also the result of remote classification matches well with that of ground truthing, but for sediment type determined by minor component. Therefore, QTC View can effectively be used for remote classification of seafloor sediments.

• Journal of the Korean Society for Marine Environment & Energy
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• v.19 no.2
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• pp.137-143
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• 2016

The sedimentary environment and sediment were surveyed at the West-Northern site of Chagwi-do nearby Jeju Island for the design of the embedded suction anchor system of 10 MW-class floating wave-offshore wind hybrid power generation system. According to the classification scheme of Chough et al.[2002], the echo type of the seismic profiles using the chirp III was classified. As a results, the center and west-northern area of survey site were proved to be type I-3 where subbottom layer with thickness 5~15 m exists under the flat seafloor. On the other hands, the east-southern area were regarded to be type I-1, I-2 and III-1 where seafloor reflection is much stronger than type I-3. Also, the physical tests (unit weight, moisture content, grain size, liquid limit, specific gravity) were performed with samples taken from 8 fixed locations. It is found that the sand (SP), the sand blended with silt (SM) and the mixture of SP-SM are distributed uniformly on the survey area.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.3 no.3
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• pp.158-164
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• 1998

We introduce a statistical scheme to classify seabed from acoustic profiling data acquired using Chirp sonar system. The classification is based on grouping of signal traces by similarity index, which is computed using the K-L (Karhunen-Lo$\grave{e}$ve) transform of the Chirp profiling data. The similarity index represents the degree of coherence of bottom-reflected signals in consecutive traces, hence indicating the acoustic roughness of the seabed. The results of this study show that similarity index is a function of homogeneity, grain size of sediments and bottom hardness. The similarity index ranges from 0 to 1 for various types of seabed material. It increases in accordance with the homogeneity and softness of bottom sediments, whereas it is inversely proportional to the grain size of sediments. As a real data example, we classified the seabed off Cheju Island, Korea based on the similarity index and compared the result with side-scan sonar data and sediment samples. The comparison shows that the classification of seabed by the similarity index is in good agreement with the real sedimentary facies and can delineate acoustic response of the seabed in more detail. Therefore, this study presents an effective method for geoacoustic modeling to classify the seafloor directly from acoustic data.