Mineral identification with the petrographic microscope

Experimental clay mineral-organic molecule complexes long have been known and some of them have been extensively studied by X-ray diffraction methods. The organic molecules are adsorbed onto the surfaces of the clay minerals, or intercalated between the silicate layers. Natural organo-clays also are widely recognized but generally have not been well characterized. Widely used techniques for clay mineral identification involve treatment of the sample with H2 O2 or other oxidant to destroy any associated organics. This generally simplifies and intensifies the XRD pattern of the clay residue, but helps little with the characterization of the original organoclay. Adequate techniques for the direct observation of synthetic and naturally occurring organoclays are yet to be developed.

Download Full-text

Unsupervised Identification of Targeted Spectra Applying Rank1-NMF and FCC Algorithms in Long-Wave Hyperspectral Infrared Imagery

Remote Sensing ◽

10.3390/rs13112125 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2125

Author(s):

Bardia Yousefi ◽

Clemente Ibarra-Castanedo ◽

Martin Chamberland ◽

Xavier P. V. Maldague ◽

Georges Beaudoin

Keyword(s):

Matched Filter ◽

Principal Component ◽

Hyperspectral Data ◽

Clustering Methods ◽

Spectral Angle Mapper ◽

Long Wave ◽

Clustering Approach ◽

Spectral Comparison ◽

Computational Simplicity ◽

Mineral Identification

Clustering methods unequivocally show considerable influence on many recent algorithms and play an important role in hyperspectral data analysis. Here, we challenge the clustering for mineral identification using two different strategies in hyperspectral long wave infrared (LWIR, 7.7–11.8 μm). For that, we compare two algorithms to perform the mineral identification in a unique dataset. The first algorithm uses spectral comparison techniques for all the pixel-spectra and creates RGB false color composites (FCC). Then, a color based clustering is used to group the regions (called FCC-clustering). The second algorithm clusters all the pixel-spectra to directly group the spectra. Then, the first rank of non-negative matrix factorization (NMF) extracts the representative of each cluster and compares results with the spectral library of JPL/NASA. These techniques give the comparison values as features which convert into RGB-FCC as the results (called clustering rank1-NMF). We applied K-means as clustering approach, which can be modified in any other similar clustering approach. The results of the clustering-rank1-NMF algorithm indicate significant computational efficiency (more than 20 times faster than the previous approach) and promising performance for mineral identification having up to 75.8% and 84.8% average accuracies for FCC-clustering and clustering-rank1 NMF algorithms (using spectral angle mapper (SAM)), respectively. Furthermore, several spectral comparison techniques are used also such as adaptive matched subspace detector (AMSD), orthogonal subspace projection (OSP) algorithm, principal component analysis (PCA), local matched filter (PLMF), SAM, and normalized cross correlation (NCC) for both algorithms and most of them show a similar range in accuracy. However, SAM and NCC are preferred due to their computational simplicity. Our algorithms strive to identify eleven different mineral grains (biotite, diopside, epidote, goethite, kyanite, scheelite, smithsonite, tourmaline, pyrope, olivine, and quartz).

Download Full-text