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Method for classifying types of mixed seabed sediment based on multi-beam sonar technology

A multi-beam, substrate technology, applied in material analysis using sonic/ultrasonic/infrasonic waves, neural learning methods, analyzing materials, etc. Effect

Inactive Publication Date: 2011-06-29
唐秋华
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  • Application Information

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Problems solved by technology

At present, researchers at home and abroad have done a lot of useful work on multi-beam substrate classification, but there are problems such as low classification accuracy and few classification levels (mainly for single substrate type classification)

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  • Method for classifying types of mixed seabed sediment based on multi-beam sonar technology
  • Method for classifying types of mixed seabed sediment based on multi-beam sonar technology

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Embodiment Construction

[0008] 1. Improved multi-beam backscatter intensity data correction model

[0009] The original multi-beam backscattering intensity cannot directly reflect the real seabed substrate characteristics and must be corrected. Focus on the analysis of the impact of the two factors of terrain fluctuation and reflection signal on the backscattering intensity, improve the current multi-beam backscattering intensity data correction model, and obtain the backscattering intensity value that can truly reflect the characteristics of the seabed bottom. In view of the previous studies on data correction and processing, the following two improvements are mainly made:

[0010] a. Vertical track and three-dimensional terrain relief correction along the track: The actual seabed topography correction is not only affected by the inclination in the direction of the vertical track, but also the inclination in the direction of the track, and the calculation model is relatively complicated. In order t...

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Abstract

The invention provides a method for classifying types of mixed seabed sediment based on a multi-beam sonar technology, and belongs to the fields of marine acoustic remote sensing detection and recognition. The method comprises the following steps: according to backscatter strength data obtained by a multi-beam sonar system, improving the existing multi-beam backscatter strength data correction model by analyzing influence on backscatter strength due to factors such as submarine topographical features, reflected signals at a central wave bundle area and the like; on the basis of the improved model, systematically seeking relationship between the submarine backscatter strength and sediment types and characteristics in details in combination with real submarine sediment sample data obtained through submarine geological sampling; and recognizing the types of the mixed seabed sediment rapidly, accurately and automatically by a neural network classification method. The method provided by the invention has the advantages of strong practicability and strong generality, and is mainly used for classifying and recognizing the types of the mixed seabed sediment.

Description

technical field [0001] The invention relates to a method for classifying and identifying mixed bottom types of seabed by using multi-beam sonar technology. Background technique [0002] Submarine sediment type is an important marine environmental parameter, and the distribution of sediment type has important scientific and practical significance for marine scientific research, ocean engineering, and national defense construction. The traditional geological sampling method has the disadvantages of heavy equipment, time-consuming and labor-intensive work in analyzing the characteristics of the sediment and determining the type of the sediment; moreover, the traditional sampling method is usually discretely sampled according to a certain grid, and the interpolation and extension of the data are used to understand the area. seabed sediment properties, and are classified, so their representation is limited and their reliability often insufficient. [0003] Multi-beam sonar techn...

Claims

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Application Information

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IPC IPC(8): G01N29/00G06N3/08
Inventor 唐秋华周兴华丁继胜吴永亭陈义兰
Owner 唐秋华
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