Hyperspectral inversion method of content of deposited rare earth La element

Abstract

The invention discloses a hyperspectral inversion method of the content of a deposited rare earth La element. In order to realize the hyperspectral inversion of the content of the deposited rare earthLa element, after a variety of spectral transformation parameters (S-G smoothing, differential, reciprocal, continuum removal and the like) are processed, a GA-ELM (Genetic Algorithm Optimized Extreme Learning Machine) mathematical model established by a variable importance evaluation (CARS, SPA, RF) characteristic variable selection method of a measured value of the content of the La element isstudied and comapred by using reflectivity second-order differential with prominent spectral characteristic details. By adopting the hyperspectral inversion method disclosed by the invention, the content of the La element can be better detected. By adopting the hyperspectral inversion method disclosed by the invention, a new test method can be provided for the hyperspectral quantitative and fast inversion research of the deposited rare earth La element on a "point", and meanwhile, a scientific and technical basis is provided for the college implementation of the quantitative inversion evaluation of regional area hyperspectral rare earth resources.

Description

technical field [0001] The invention relates to the technical field of hyperspectral analysis of soil / rock components, and more specifically, relates to a hyperspectral inversion method for the content of La element in sedimentary rare earths. Background technique [0002] Rare earths, as the country's key mineral resources, occupy an important strategic position in Industry 4.0 and the fourth scientific and technological revolution. They can play a "key role" in strategic emerging industries, and they are often "irreplaceable" in reality. a research hotspot. Therefore, the rapid non-destructive detection of rare earth elements is of great significance to speed up the rapid screening and evaluation of rare earth resources and find out the status quo of rare earth resources. [0003] Investigations and studies in recent years have preliminarily found out the status of rare earth resources in key provinces, and achieved a leap from strategic research to strategic reconnaissan...

AI Technical Summary

Problems solved by technology

However, there are no relevant reports on the use of machine learning methods in the hyperspectral inversion modeling of sedimentary rare earth elements.

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