Estimation of lithium content in rock debris based on spectral feature coefficients DOI Creative Commons
Guo Jiang, Xi Chen, Xi Chen

et al.

Ore Geology Reviews, Journal Year: 2024, Volume and Issue: 171, P. 106167 - 106167

Published: July 14, 2024

Hyperspectral remote sensing is a fast and non-destructive technology for identifying geological information, many successful cases have been achieved in mineral identification estimation of soil heavy metal content. However, there fewer studies on the application this to rare metals, especially detection lithium (Li) resources. Whether hyperspectral process can effectively identify Li anomalies significant expanding exploration To end, study explores potential techniques elemental content by collecting rock debris samples field extracting spectral feature coefficients using Gaussian Mixture Model (GMM). The results show that (1) parameter extraction technique based GMM quickly accurately extract absorption parameter. (2) Compared with reflectance, improve correlation content, constructed model more effective. (3) full-width at half maximum (FWHM) 1.93 μm most effective, determination (R2), relative root mean squared error (RRMSE), ratio performance deviation (RPD) 0.61, 0.516 1.601, respectively, which are significantly better than reflectance model. above use estimate debris, provides technical reference regional airborne support improving efficiency resources narrowing focus investigation.

Language: Английский

Estimation of lithium content in rock debris based on spectral feature coefficients DOI Creative Commons
Guo Jiang, Xi Chen, Xi Chen

et al.

Ore Geology Reviews, Journal Year: 2024, Volume and Issue: 171, P. 106167 - 106167

Published: July 14, 2024

Hyperspectral remote sensing is a fast and non-destructive technology for identifying geological information, many successful cases have been achieved in mineral identification estimation of soil heavy metal content. However, there fewer studies on the application this to rare metals, especially detection lithium (Li) resources. Whether hyperspectral process can effectively identify Li anomalies significant expanding exploration To end, study explores potential techniques elemental content by collecting rock debris samples field extracting spectral feature coefficients using Gaussian Mixture Model (GMM). The results show that (1) parameter extraction technique based GMM quickly accurately extract absorption parameter. (2) Compared with reflectance, improve correlation content, constructed model more effective. (3) full-width at half maximum (FWHM) 1.93 μm most effective, determination (R2), relative root mean squared error (RRMSE), ratio performance deviation (RPD) 0.61, 0.516 1.601, respectively, which are significantly better than reflectance model. above use estimate debris, provides technical reference regional airborne support improving efficiency resources narrowing focus investigation.

Language: Английский

Citations

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