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Pockmarks are circular or elliptical depressions in the seabed that are geomorphologically similar but otherwise distinct from volcanic craters. Pockmarks are found at various depths in the ocean including on the continental shelf, the continental slope and the deep-sea plain. This paper presents data covering a large-scale pockmark group in the middle part of the shallow sea sedimentary plain of the North Yellow Sea at a water depth of 51–58 m. The distribution of pockmarks within this group is very dense, and pockmark diameters range from several hundred meters to several kilometers. Based on multibeam echo sounder data, a high-resolution image of the pockmark area is obtained by efficient image processing methods. The textural features of the seabed surface layer are then described and analyzed. Multibeam echo sounder backscatter data are combined with seabed sediment sampling data to develop a statistical model linking seabed backscatter sonar strength and sediment grain-size characteristics. Additionally, an improved BP neural network is used to realize rapid and automatic classification and identification of seabed sediment types. Fine-scale processing analysis of sonar images and acoustic classification of seabed sediments as presented in this paper are useful for determining seabed surface textures and local sediment distribution laws. This study thus provides a basis for further study of the formation and evolution of seabed pockmarks. [ABSTRACT FROM AUTHOR]

Seabed sediment classification using acoustic remote sensing technique is an attractive approach due to its high coverage capabilities and limited costs compared to taking samples of the seafloor. This paper focuses on backscatter intensity correction, sonar image quality improvement, and classifier construction, which aims to improve the accuracy of seabed sediment classification. The details are as follows. 1) A series of multibeam echosounder backscatter intensity correction model is constructed, including time-varying gains (TVG), transmission loss, actual area of insonification, source level, transmitting and receiving beam patterns, specular area correction, etc., to obtain accurate intensity values that accurately reflect seabed sediment types. 2) The pulse coupled neural network (PCNN) image enhancement model is established to improve the quality of sonar images, and 40 dimensional features are included to enrich the intensity description. 3) Selecting optimal random forest (SORF) seabed sediment automatic classification models that can select the input feature vectors and optimize the model parameters automatically are established. 4) Taking multibeam backscatter intensity data collected in Jiaozhou Bay as an example, the effectiveness and advantages of SORF are verified by comparing with support vector machine (SVM) and random forest (RF) classifiers. [ABSTRACT FROM AUTHOR]