Inversion method of absolute oil film thickness of crude oil based on self-expanding convolutional neural network

Abstract

The invention provides a crude oil film absolute thickness inversion method based on a self-expanding convolutional neural network. The measured spectral data is screened to obtain real spectral characteristic data; the real spectral characteristic data is input into a confrontation generation network to generate self-expanding sample data; The integrated neural network is used to extract features from the self-expanded sample data, and to invert the absolute thickness of the crude oil film. The method screens the measured spectral data, removes the bands with poor separability, and is more conducive to the accurate quantitative inversion of crude oil film thickness; the data is expanded by the adversarial generation network, so that only a small amount of measured data can be generated based on the model. A large amount of high imitation data enriches the generalization of the model and enhances the robustness of the model; the convolution process of the convolutional neural network can fully learn the spectral information, avoid the loss of information, thereby improving the inversion accuracy of the absolute thickness of the crude oil film .

Description

technical field [0001] The invention relates to the field of ocean detection, in particular to a method for retrieving the absolute thickness of crude oil film based on a self-expanding convolutional neural network. Background technique [0002] Oil spill is a maritime emergency caused by oil leakage during offshore oil exploration, development, and transportation. It has been listed by the American Academy of Sciences as one of the 32 scientific issues to be solved before 2030. In recent years, marine oil spill disasters have occurred frequently, seriously affecting the sustainable development of the marine ecological environment and marine resources. The amount of oil spilled on the sea surface is an important indicator for evaluating the threat level of oil spill accidents at sea and determining the level of oil spill accidents. It is also an important basis for accountability for pollution compensation. [0003] Accurate acquisition of oil spill area, oil film thickness...

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Problems solved by technology

[0006] In view of this, the present invention provides a crude oil film absolute thickness inversion method based on a self-expanding convolutional neural network to solve the problem that the method for measuring the absolute thickness of the oil film in the prior art is limited to insufficient experimental data, resulting in low measurement accuracy.

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