The GEOROC database includes helpful compilations of mineral compositions aggregated from measurements reported in decades worth of publications, but it can be challenging to consistently filter mislabeled, inaccurate, or incomplete mineral compositions. MIST (Mineral Identification by Stoichiometry) is a stoichiometry-based computational algorithm that identifies geochemical observations with normalized elemental ratios matching natural minerals. The stoichiometric filters that were manually coded in MIST for over 240 mineral species are based on reported mineral formulas and well-documented examples of mineral chemistry reported in RRUFF and associated databases, typically including a ~5-10% tolerance in stoichiometric ratios based on measurement errors, vacancies, and substitutions. The MIST model can therefore efficiently filter the GEOROC mineral compilation files to recognize compositions whose normalized oxides match the labeled mineral stoichiometry. Furthermore, the MIST output includes results of intermediate data manipulation steps, a detailed stoichiometric formula for each input composition, and consistently calculated mineral endmembers such as Fo, En, Ws, and Fs. MIST is agnostic to the instrument used to collect oxide data. Because MIST uses normalized oxides, it cannot distinguish between some mineral species, where applicable, they are reported as a group (e.g., gypsum/bassanite/anhydrite). MIST can only recognize minerals encoded in the algorithm, so other real but less common minerals will not be recognized. The full list of minerals MIST can recognize, along with more details of the algorithm and results pages, are published in Siebach et al. (https://doi.org/10.1016/j.cageo.2025.106021).
This dataset includes fifteen of the Compiled Mineral files published by GEOROC in 12-2024 including the MIST results (whether or not a species was confirmed by MIST). Prior to running the data through MIST, all files were filtered to only include mineral compositions that included major oxides (e.g., silicate mineral compositions where SiO2 > 0 wt%). Furthermore, all variations of reported Fe were collapsed into a single column representing FeOT.
Metadata is preserved from the original compiled GEOROC files, so users may add additional filters as appropriate for different purposes. Results have not been filtered for reported sum of total oxides, but doing so can help identify particular mineral species (e.g., separate gypsum from bassanite). An additional file preserves the full reference information for each mineral compilation.
We suggest using the compositions that MIST identifies as stoichiometrically consistent with a mineral species as a standardized filter on the GEOROC datasets prior to utilizing the data in machine learning models or similar applications. These may also be helpful any time a user would like standardized formulas or mineral endmember information for these mineral compilations.
This dataset presents the raw data of an experimental series of analogue models performed to investigate the influence of inherited brittle fabrics on narrow continental rifting. This model series was performed to test the influence of brittle pre-existing fabrics on the rifting deformation by cutting the brittle layer at different orientations with respect to the extension direction. An overview of the experimental series is shown in Table 1. In this dataset we provide four different types of data, that can serve as supporting material and for further analysis: 1) The top-view photos, taken at different steps and showing the deformation process of each model; they can be used to interpret the geometrical characteristics of rift-related faults; 2) Digital Elevation Models (DEMs) used to reconstruct the 3D deformation of the performed analogue models, allowing for quantitative analysis of the fault pattern. 3) Short movies built from top-view photos which help to visualize the evolution of model deformation; 4) line-drawing of fault and fracture patters to be used for fault statistical quantification. Further details on the modelling strategy and setup can be found in Corti (2012), Maestrelli et al. (2020), Molnar et al. (2020), Philippon et al. (2015), Zwaan et al. (2021) and in the publication associated with this dataset. Materials used for these analogue models were described in Montanari et al. (2017) Del Ventisette et al. (2019) and Zwaan et al. (2020).