Wen-yu ZHAI, Lei CHEN, Yi-xuan XU, Xiang-yu KONG. Analysis of Impact Factors and Applications by Using Spectral Absorption Depth for Quantitative Inversion of Carbonate Mineral[J]. Spectroscopy and Spectral Analysis, 2021, 41(7): 2226
- Spectroscopy and Spectral Analysis
- Vol. 41, Issue 7, 2226 (2021)
Fig. 1. The spectra of calcite and three spectra with different feature types around 2.33 μm
Fig. 2. The grey image of band 207 (Wavelength 2.33 μm)
Fig. 3. To mix with calcite under variable abundance
(a): galena4; (b): mordeni1; (c): czoisite
(a): galena4; (b): mordeni1; (c): czoisite
Fig. 4. The spectral curve after continuum removal processing
(a): galena4; (b): mordeni1; (c): czoisite
(a): galena4; (b): mordeni1; (c): czoisite
Fig. 5. Three endmember spectra and different reflectivity
(a): None feature; (b): Reflection feature; (c): Absorb feature
(a): None feature; (b): Reflection feature; (c): Absorb feature
Fig. 6. Continuum removed curve of spectra with different reflectivity under different features
(a): None feature; (b): Reflection feature; (c): Absorb feature
(a): None feature; (b): Reflection feature; (c): Absorb feature
Fig. 7. CRBD change with reflectivity and abundance fractions under different features
(a): None feature; (b): Reflection feature; (c): Absorb feature
(a): None feature; (b): Reflection feature; (c): Absorb feature
Fig. 8. CRBD variation of mixed spectra by three endmembers under different spectral abundance
(a): CRBD of mixed spectra by three endmembers; (b): Linear interpolation of CRBD of three endmembers; (c): Difference of (a) and (b)
(a): CRBD of mixed spectra by three endmembers; (b): Linear interpolation of CRBD of three endmembers; (c): Difference of (a) and (b)
Fig. 9. CRBD change with carbonate mineral abundance and corresponding fitting equation
(a): CRBD variation with carbonate mineral abundance; (b): Fitting equation of CRBD variation boundary
(a): CRBD variation with carbonate mineral abundance; (b): Fitting equation of CRBD variation boundary
Fig. 10. The inversion results by the fitting equations
(a): Inversion by applying the high-value variation fitting equation; (b): Inversion by applying linear changing trend to image data;(c): Inversion by applying the low-value variation fitting equation
(a): Inversion by applying the high-value variation fitting equation; (b): Inversion by applying linear changing trend to image data;(c): Inversion by applying the low-value variation fitting equation
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Table 1. Group classification by spectral feature for cuprite image data
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