Identification method of seabed landform types based on depth distribution characteristics

Abstract

The invention discloses a seabed landform type identification method based on depth distribution characteristics. The method comprises the following steps: 1) calculating depth distribution characteristics according to seabed depth measurement data, including skewness, kurtosis, seabed depth standard deviation, seabed depth difference entropy, seabed roughness and seabed depth variation coefficient; 2) using depth distribution characteristics as fuzzy As discriminant variables, a membership degree function set is established for standard landform types; 3) The principle of maximum membership degree is used to identify landform types. The invention has the advantages of simple method, small amount of calculation, high recognition accuracy, manpower saving and the like. The invention is applicable to the type identification of seabed topography.

Description

Technical field The invention relates to the technical fields of marine surveying and mapping, marine engineering, marine oil and gas resources and the like, and in particular to a method for identifying types of seabed landforms based on depth distribution characteristics. Background technique Landform is the general term for various undulations on the earth's surface. During the formation of the crust, the seabed landform is formed by internal and external forces such as translation, subduction, folds, faults, earthquakes, volcanic activity, tidal currents, ocean currents, turbidity currents, and biological processes. The types of submarine topography include ancient river channels, scour troughs, submarine canyons, deep water channels, seamounts, carbonate platforms, cliffs, landslides, etc. The research on the types of submarine topography is closely related to the research on the spatial distribution, internal structure, formation and evolution of the submarine topography...

AI Technical Summary

Problems solved by technology

This method can make full use of the knowledge of technicians and has good flexibility, but it requires technicians to have rich geological knowledge and experience in observation and judgment, which has great subjectivity, poor timeliness, and high labor intensity.

Especially due to the massive level of seabed data, it is far from being able to undertake the processing tasks of massive data only relying on the artificial ability of technicians

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