Reconstruction Method of Underwater Temperature Field Based on Self-Organizing Neural Network and Empirical Orthogonal Function

Abstract

The invention relates to an underwater temperature field reconstruction method based on a self-organizing neural network and an empirical orthogonal function, which establishes multi-dimensional information such as empirical orthogonal function coefficients, position information, time information, sea surface height, and sea surface temperature corresponding to the temperature profile. The Euclidean distance between the known information and the self-organizing feature map unit is used to judge the best matching unit, so as to obtain the empirical orthogonal function coefficients to be inverted. Based on a large amount of data information, the characteristic mapping network of sea surface parameters and water body temperature profiles can be established, which can realize the nonlinear mapping of sea surface parameters to water body profiles. Implementation effect, the underwater temperature field reconstruction method based on self-organizing neural network and empirical orthogonal function has superior performance and good robustness. It is small and easy to implement, and is suitable for quasi-real-time acquisition of marine environmental parameters in key sea areas by using satellite remote sensing data.

Description

technical field [0001] The invention belongs to the fields of marine physics, marine engineering, underwater acoustic engineering, etc., and relates to an underwater temperature field reconstruction method based on self-organizing neural network and empirical orthogonal function, which is suitable for underwater temperature field reconstruction using satellite remote sensing data . Background technique [0002] Although a variety of underwater temperature field reconstruction methods have been used in engineering practice, such as temperature profile depth-by-depth layer regression methods, empirical function regression methods, etc., the underwater temperature field reconstruction in deep sea complex waters still faces serious challenges. technical challenge. The root cause is that the existing temperature profile reconstruction methods have certain defects in the face of strong nonlinear processes in complex marine environments. The specific analysis is as follows: [00...

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Problems solved by technology

Similar to the above method, in sea areas with frequent eddies and strong fronts, there is usually no significant correlation between sea surface remote sensing parameters and empirical orthogonal function coefficients, which will cause significant errors in temperature profile reconstruction

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