Deep learning has increasingly been employed to identify minerals. However, deep learning can only be used to identify minerals in the distribution of the training set, while any mineral outside the spectrum of the training set is inevitably categorized erroneously within a predetermined class from the training set. To solve this problem, the study introduces the approach that amalgamates One-Class Support Vector Machines (OCSVM) with the ResNet architecture for the out-of-distribution mineral detection. Initially, ResNet undergoes training using a training set comprising well-defined minerals. Subsequently, the earlier layers of the trained ResNet are employed to extract the discriminative features of the mineral under consideration. These extracted mineral features then become the input for OCSVM. When OCSVM discerns the mineral in the training set's distribution, it triggers the subsequent layers within the trained ResNet, facilitating the accurate classification of the mineral into one of the predefined categories encompassing the known minerals. In the event OCSVM identifies the mineral out of the training set's distribution, it is unequivocally categorized as an unclassified or 'unknown' mineral. Empirical results substantiate the method's capability to identify out-of-distribution minerals while concurrently maintaining a commendably high accuracy rate for the classification of the 36 in-distribution minerals.

A series of Si-bearing MgAl₂O₄-spinels were synthesized at 1500-1650 °C and 3–6 GPa. These spinels had SiO₂ contents up to ~1.03 wt%, and showed a substitution mechanism of Si⁴⁺ + Mg²⁺ = 2Al³⁺. Unpolarized Raman spectra were collected from polished single grains, and displayed a set of well-defined Raman peaks at ~610, 823, 856 and 968 cm^-1 which had not been observed before. Aided with the Raman features of natural Si-free MgAl₂O₄-spinel, synthetic Si-free MgAl₂O₄-spinel, natural low quartz, synthetic coesite, synthetic stishovite and synthetic forsterite, we infer that these Raman peaks should belong to the SiO₄ groups. The relations between the Raman intensities and SiO₂ contents of the Si-bearing MgAl₂O₄-spinels suggest that at some P-T conditions some Si must adopt the M-site. Unlike the SiO₄ groups with very intense Raman signals, the SiO₆ groups are largely Raman-inactive. We have further found that the Si cations primarily appear on the T-site at P-T conditions ≤ ~3–4 GPa and 1500 °C, but attain a random distribution between the T-site and M-site at P-T conditions ≥ ~5–6 GPa and 1630–1650 °C. This Si-disordering process observed for the Si-bearing MgAl₂O₄-spinels hints that similar Si-disordering might happen to the (Mg,Fe)₂SiO₄-spinels (ringwoodite), the major phase in the lower part of the mantle transition zone of the Earth and the index mineral for the very strong shock stage experienced by extraterrestrial materials. The likely consequences have been explored.