[Study of spectrum preprocessing method when applying the characteristic spectrum linear inversion modeling to...

Area of Science:

• Geology
• Remote Sensing
• Spectroscopy

Context:

• Field mineral identification is critical for geological studies and mineral exploration.
• Characteristic spectrum linear inversion modeling (CSLM) offers rapid mineral identification but shows variability.
• Different spectral data types can lead to significant differences in CSLM results.

Purpose:

• To investigate the continuum based fast Fourier transform processing (CFFT) method and characteristic spectrum linear inversion modeling (CSLM).
• To determine optimal parameters for CFFT when applied to rock samples.
• To evaluate the effectiveness of CSLM using different spectral datasets, particularly denoised ASD spectra.

Summary:

• The study optimized the CFFT method for rock samples by setting the low-pass frequency to 150 Hz.
• It was found that denoising ASD spectra using the CFFT method significantly improved the accuracy of mineral information extraction via CSLM.
• This suggests a synergistic approach combining CFFT denoising with CSLM for enhanced field-based mineral identification.

Impact:

• Provides optimized parameters for CFFT in geological applications.
• Demonstrates the benefit of CFFT-based spectral denoising for improving CSLM accuracy in field mineral exploration.
• Enhances the reliability and efficiency of remote sensing geology studies.