The atomic structure and energy spectrum of Ga(As,P)/GaP heterostructures were studied. It was shown that the deposition of GaAs of the same nominal thickness leads to the formation of pseudomorphic GaAs/GaP quantum wells (QW), fully relaxed GaAs/GaP self-assembled quantum dots (SAQDs), or pseudomorphic GaAsP/GaP SAQDs depending on the growth temperature. We demonstrate that the atomic structure of Ga(As,P)/GaP heterostructures is ruled by the temperature dependence of adatom diffusion rate and GaAs-GaP intermixing. The band alignment of pseudomorphic GaAs/GaP QW and GaAsP/GaP SAQDs is shown to be of type II, in contrast to that of fully relaxed GaAs/GaP SAQDs, which have the band alignment of type I with the lowest electronic states at the indirect L valley of the GaAs conduction band.
The article presents the methodology of petrographic thin section analysis, combining the algorithms of image processing and statistical learning. The methodology includes the structural description of thin sections and rock classification based on images obtained from polarized optical microscope. To evaluate the properties of structural objects in thin section (grain, cement, voids, cleavage), first they are segmented by watershed method with advanced noise reduction, preserving the boundaries of grains.
Analysis of segmentation for test thin sections showed a fairly accurate contouring of mineral grains which makes possible automatically carry out the calculation of their key features (size, perimeter, contour features, elongation, orientation, etc.). The paper presents an example of particle size analysis – definition of grains size class. The roundness and rugosity coefficients of grains are estimated also. Statistical analysis of templates for manual determination of roundness and rugosity coefficients revealed drawback of examined templates in terms statistical accuracy (high dispersion of coefficient for all grain within one template, outliers presence).
In the frame of classification problem the feature importance analysis and clustering of non-correctly segmented grains are handled. The classifier for rock type definition (sandstone, limestone, dolomite) is trained with decision tree method, while the classifier of mineral composition of sandstones (greywackes, arkose) is learnt with "random forest" method. Both classifiers are learnt in the feature space generated from segmented grains and their evaluated properties.
As a result, we proved the possibility to conduct automatic quantitative and qualitative analysis of thin sections applying image processing and statistical learning methods.
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