Research on Seabed Sediment Classification Based on the MSC-Transformer and Sub-Bottom Profiler

Wang, H.M.; Zhou, Qing; Wei, Shuo; Xue, X; Zhou, Xinghua; Zhang, Xiaobo

doi:10.3390/jmse11051074

Cited by 4 publications

(1 citation statement)

References 38 publications

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“…Luo et al compared the classification performance of deep and shallow CNN models for three types of sediment, namely stone, mud, and sand, and found that the shallow CNN model outperformed the deep CNN model while achieving excellent classification performance [22]. Wang et al conducted a study on sediment classification using MSC-Transformer and shallow formation profile data and achieved good results [23]. Additionally, Tegowski extracted features such as fractal dimension and spectral length of backscatter images as K-means clustering parameters for classification [24].…”

Section: Introductionmentioning

confidence: 99%

Classification of Marine Sediment in the Northern Slope of the South China Sea Based on Improved U-Net and K-Means Clustering Analysis

Zhou

Liu

et al. 2023

Remote Sensing

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The classification of marine sediment based on acoustic data is crucial for various applications such as marine resource exploitation, marine engineering construction, and marine ecological environment maintenance. It serves as a valuable alternative to limited geological sampling. However, the accuracy of sediment classification is limited due to constraints in acoustic data detection methods, data quality, and classification techniques. To address this issue, this study proposes an automatic classification method for marine sediment using an improved U-convolutional neural network and K-means clustering algorithm. In the coding part, a spatial pyramid pool layer is introduced to fuse low-dimensional feature data of different scales with the features of each level of the corresponding coding layer. This fusion method enhances the accuracy of the constructed relationship between the physical property parameters of the seabed bottom. The K-means clustering algorithm is optimized through selecting the point at the density center as the initial clustering center during the initial clustering center selection stage. This approach solves the sensitivity problem of the initial clustering center of K-means, improves the edge extraction effect of sediment types, and enhances the classification accuracy of sediment types. To validate the proposed method, an application test is conducted in the Northern Slope area of the South China Sea. The mean grain size of sediments in the study area is predicted using the improved U-Net neural network and the seafloor reflection intensity of the sub-bottom profile. Compared to the standard U-Net network results, the mean grain size prediction results show an increase of 4.9% and 2.8%, respectively. The sediment with the predicted mean grain size is then classified using the K-means clustering algorithm, resulting in the division of five sediment types: gravelly sand, sand, silty sand, sandy silt, and clayey silt. These classifications align well with the South China Sea sediment type map. The findings of this study not only provide an important supplement to existing marine sediment classification methods but also contribute significantly to understanding the sedimentary environment and processes in the Northern Slope of the South China Sea.

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Section: Introductionmentioning

confidence: 99%

Classification of Marine Sediment in the Northern Slope of the South China Sea Based on Improved U-Net and K-Means Clustering Analysis

Zhou

Liu

et al. 2023

Remote Sensing

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Sampling Guidance of Deep-Sea Surficial Sediment Using Acoustic Faces CNN-BLSTM Fusion

Qu,

Shan,

Zou

et al. 2024

IEEE Geosci. Remote Sensing Lett.

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Classification of Seafloor Sediment in Northern Slope of South China Sea based on Improved U-Net and K-Means Clustering Analysis

Zhou¹,

Li²,

Liu³

et al. 2023

Preprint

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The determination and classification of seafloor sediment types are crucial for the exploitation of marine resources, construction of marine engineering, and maintenance of marine ecological en-vironments. Automatic classification of seafloor sediment based on acoustic telemetry data is an important method to quickly understand the type of a large range of sediment. Currently, most studies on sediment classification are based on multi-beam backscattering intensity data, which is a relatively single data type. Besides, the low-dimensional details of standard U-Net gradually weaken in the propagation process, limiting the accuracy of sediment classification. Therefore, this study proposes an automatic classification method of seafloor sediment types based on an im-proved U-Net and K-means clustering algorithm, using multi-beam water depth, sub-bottom profile, and sample test data in the Northern Slope of the South China Sea. Six sediment types, including gravelly muddy sand, sand, silty sand, less muddy silt, muddy silt, and silty mud, were identified in the study area. Additionally, the study area was divided into four sedimentary en-vironment zones, including the shelf sedimentary area, the upper shelf slope break sedimentary area, the lower shelf slope break mixed sedimentary area, and the slope deposition area, based on the results of sediment classification and geological background. The sedimentary environment zones were found to be distributed along the trend of the shelf slope break line. The results of this study not only provide an important supplement to the existing classification methods of seafloor sediments but also contribute to the understanding of the sedimentary environment and process of the Northern Slope of the South China Sea.

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Research on Seabed Sediment Classification Based on the MSC-Transformer and Sub-Bottom Profiler

Cited by 4 publications

References 38 publications

Classification of Marine Sediment in the Northern Slope of the South China Sea Based on Improved U-Net and K-Means Clustering Analysis

Classification of Marine Sediment in the Northern Slope of the South China Sea Based on Improved U-Net and K-Means Clustering Analysis

Sampling Guidance of Deep-Sea Surficial Sediment Using Acoustic Faces CNN-BLSTM Fusion

Classification of Seafloor Sediment in Northern Slope of South China Sea based on Improved U-Net and K-Means Clustering Analysis

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