The presence of carbonates in inclusions in diamonds coming from depths exceeding 670 km are obvious evidence that carbonates exist in the Earth’s lower mantle. However, their range of stability, crystal structures and the thermodynamic conditions of the decarbonation processes remain poorly constrained. Here we investigate the behaviour of pure iron carbonate at pressures over 100 GPa and temperatures over 2,500 K using single-crystal X-ray diffraction and Mössbauer spectroscopy in laser-heated diamond anvil cells. On heating to temperatures of the Earth’s geotherm at pressures to ∼50 GPa FeCO3 partially dissociates to form various iron oxides. At higher pressures FeCO3 forms two new structures—tetrairon(III) orthocarbonate Fe43+C3O12, and diiron(II) diiron(III) tetracarbonate Fe22+Fe23+C4O13, both phases containing CO4 tetrahedra. Fe4C4O13 is stable at conditions along the entire geotherm to depths of at least 2,500 km, thus demonstrating that self-oxidation-reduction reactions can preserve carbonates in the Earth’s lower mantle.

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Iron oxides are fundamentally important compounds for basic and applied sciences as well as in numerous industrial applications. In this work we report the synthesis and investigation of a new binary iron oxide with the hitherto unknown stoichiometry of Fe7O9. This new oxide was synthesized at high-pressure high-temperature (HP-HT) conditions, and its black single crystals were successfully recovered at ambient conditions. By means of single crystal X-ray diffraction we determined that Fe7O9 adopts a monoclinic C2/m lattice with the most distorted crystal structure among the binary iron oxides known to date. The synthesis of Fe7O9 opens a new portal to exotic iron-rich (M,Fe)7O9 oxides with unusual stoichiometry and distorted crystal structures. Moreover, the crystal structure and phase relations of such new iron oxide groups may provide new insight into the cycling of volatiles in the Earth’s interior.

The description of rocks is one of the most time-consuming tasks in the everyday work of a geologist, especially when very accurate description is required. We here present a method that reduces the time needed for accurate description of rocks, enabling the geologist to work more efficiently. We describe the application of methods based on color distribution analysis and feature extraction. Then we focus on a new approach, used by us, which is based on convolutional neural networks. We used several well-known neural network architectures (AlexNet, VGG, GoogLeNet, ResNet) and made a comparison of their performance. The precision of the algorithms is up to 95% on the validation set with GoogLeNet architecture. The best of the proposed algorithms can describe 50 m of full-size core in one minute.