Seabed Classification Using Multibeam Echosounder Measurement Data

Nitriansyah, R; Cahyono, Bambang Kun

doi:10.1088/1755-1315/1039/1/012045

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Researchers often use backscatter data to classify seafloor sediments in single frequency [10][11][12][13][14] and multifrequency [6,7,[15][16][17]. In addition, some researchers use bathymetric and backscatter data [18][19][20] and bathymetric and backscatter features [14,[21][22][23][24]. Currently, there are no studies that use bathymetric data and bathymetric differences for sediment classification.…”

Section: Introductionmentioning

confidence: 99%

Bathymetry and Bathymetric Differences Mbes Multifrequency for Seafloor Sediment Mapping

2024

GEOMATE

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Seafloor sediments have a significant role in the planning and development of coastal areas, especially port areas. Acoustic technology developing today, especially multifrequency Multibeam Echosounder (MBES), is expected to measure seafloor sediment and detect the type and distribution of seafloor sediments. The question posed in this study is how to improve the accuracy of sediment classification using multifrequency MBES. This study uses deep neural networks to classify seafloor sediments in the study area with input bathymetric and bathymetric differences and 74 in situ sediment samples (silt, clayey sand, silty sand, and sandy silt). Sediment classification results show that clayey sand dominates the sediment distribution in the Central and Eastern regions. On the other hand, sandy silt predominates in the western area (harbor pond). Classification of seafloor sediments in the study area has an accuracy of 41.9% (average) and a kappa coefficient of 21.9% (fair). The implication of the study is that bathymetric and bathymetric differences from multifrequency MBES produce a low sediment classification accuracy value of below 50%. Therefore, it needs to be re-evaluated in relation to bathymetric and bathymetric differences and the amount and distribution of sediment sample data needed to improve its accuracy.

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Section: Introductionmentioning

confidence: 99%

Bathymetry and Bathymetric Differences Mbes Multifrequency for Seafloor Sediment Mapping

2024

GEOMATE

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“…Due to the progress in Multibeam Echosounder (MBES) technology, it is now possible to obtain comprehensive data sets of the physical structure across extensive areas of the seafloor [5]. A Multibeam echosounder exploits the acoustic waves emitted to the ocean floor through a transducer and records the reflections of those waves using a receiver [6]. The intensity of the reflected acoustic wave depends on the material of the seafloor, such as sediments, rock, coral reef, and seafloor roughness [7].…”

Section: Introductionmentioning

confidence: 99%

Dense Neural Network for Classification of Seafloor Sediment using Backscatter Mosaic Feature

Khomsin,

Guruh Pratomo,

Aldila Syariz

et al. 2024

BIO Web Conf.

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Water transportation plays a vital role in global economic activities, facilitating more than 85% of international trade and serving as a cost-effective and essential means to fulfill the demand for goods and services. Similarly, the Benoa Port, situated in the southern part of Denpasar City, operates in the same manner. By utilizing Multibeam Echo Sounder (MBES) backscatter data, backscatter mosaics can be generated to identify various seafloor sediment types, which consist of rock fragments, minerals, and organic materials. The characteristics of these sediments, such as grain size, density, composition, and others, can be observed. To improve the classification of sediments, the integration of backscatter data and backscatter features, such as ASM (Angular Second Moment), Energy, Contrast, and Correlation, can be employed. Supervised classification models like Dense Neural Network (DNN) can be utilized to accurately determine the types of seafloor sediments. The application of DNN modeling resulted in a training accuracy rate of 88% and a testing accuracy rate of 100%. The accuracy results delineated six distinct sediment types. Notably, sandy silt exhibited the highest distribution, accounting for 49.30%, whereas soft clayey silt registered the lowest distribution at 0.53%, as determined by their respective spatial prevalence.

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3D UHR seismic and back-scattering analysis for seabed and ultra-shallow subsurface classification

Ha,

Shin,

Lim

et al. 2024

Acta Geophys.

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Recently, the seabed classification method based on back-scattering data of multi-beam echo-sounder (MBES) is widely used to analyze the distribution of seabed sediment. Although various analysis methods for seabed classification using multi-spectral MBES have been developed, they are limited in securing penetration depth to consider the characteristics of the shallow subsurface structure. In this study, the seabed and ultra-shallow subsurface classification was performed by comparative analysis of box corer sampling, back-scattering, and 2D/3D ultra-high-resolution (UHR) seismic data obtained from Yeongil Bay, South Korea. We proposed a process for seismic ultra-shallow subsurface classification by the segmentation of the primary seabed reflection wavelet and the amplitude analysis. The seabed-reflected amplitude and back-scattering intensity showed similar mapping trends in the relatively homogeneous and thick surface sediment. On the other hand, it was confirmed that back-scattering data and seabed-reflected amplitude show different patterns when the subsurface structure is related to the seabed surface. It is presumed that because seismic data containing relatively low-frequency components have a deeper penetration depth than MBES, they contain more characteristics of the ultra-shallow subsurface than back-scattering data. These were determined that back-scattering has advantages in representing acoustic anomaly distribution by surface sediment type, and seabed-reflected amplitude is advantageous for representing sediment type by ultra-shallow subsurface. In particular, these results were well shown when the surface sediment thinly covered the rocky bottom. Therefore, it is necessary not only to analyze the back-scattering of MBES but also the ultra-shallow subsurface features through seismic data for valid seabed classification.

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Seabed Classification Using Multibeam Echosounder Measurement Data

Cited by 3 publications

References 5 publications

Bathymetry and Bathymetric Differences Mbes Multifrequency for Seafloor Sediment Mapping

Bathymetry and Bathymetric Differences Mbes Multifrequency for Seafloor Sediment Mapping

Dense Neural Network for Classification of Seafloor Sediment using Backscatter Mosaic Feature

3D UHR seismic and back-scattering analysis for seabed and ultra-shallow subsurface classification

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