We will concentrate on the characteristics of the existing software We review the most important computer programs for the classification of igneous rocks and point out those that follow the recommendations of the International Union of Geological Sciences (IUGS). A new program "Igneous Rock Classification System" (IgRoCS), written in Visual Basic, is then described in detail. IgRoCS allows the user to follow strictly the IUGS classification and nomenclature scheme for igneous rocks. The special rocks are first classified, then plutonic rocks are named after the IUGS mineralogical classification, next high-Mg and picritic volcanic rocks are identified from the IUGS criteria, and finally other volcanic rocks are classified according to the TAS diagram and CIPW norm. The chemical classification of volcanic rocks can also be achieved directly without going through the classification of other rock types. The IgRoCS program incorporates the revised standard igneous norm. Thus, the use of this new software is encouraged for a strict application of the IUGS recommendations for igneous rock classification and nomenclature.

A new multidimensional classification scheme consistent with the chemical classification of the International Union of Geological Sciences (IUGS) is proposed for the nomenclature of High‐Mg altered rocks. Our procedure is based on an extensive database of major element (SiO2, TiO2, Al2O3, Fe2 O3t, MnO, MgO, CaO, Na2O, K2O, and P2O5) compositions of a total of 33,868 (920 High‐Mg and 32,948 “Common”) relatively fresh igneous rock samples. The database consisting of these multinormally distributed samples in terms of their isometric log‐ratios was used to propose a set of 11 discriminant functions and 6 diagrams to facilitate High‐Mg rock classification. The multinormality required by linear discriminant and canonical analysis was ascertained by a new computer program DOMuDaF. One multidimensional function can distinguish the High‐Mg and Common igneous rocks with high percent success values of about 86.4% and 98.9%, respectively. Similarly, from 10 discriminant functions the High‐Mg rocks can also be classified as one of the four rock types (komatiite, meimechite, picrite, and boninite), with high success values of about 88%–100%. Satisfactory functioning of this new classification scheme was confirmed by seven independent tests. Five further case studies involving application to highly altered rocks illustrate the usefulness of our proposal. A computer program HMgClaMSys was written to efficiently apply the proposed classification scheme, which will be available for online processing of igneous rock compositional data. Monte Carlo simulation modeling and mass‐balance computations confirmed the robustness of our classification with respect to analytical errors and postemplacement compositional changes.

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We applied both the ordinary linear regression (OLR) and the new uncertainty weighted linear regression (UWLR) models for the calibration and comparison of a XRF machine through 59 geochemical reference materials (GRMs) and a procedure blank sample. The mean concentration and uncertainty data for the GRMs used for the calibrations (Supplementary Materials) (available here) filewere achieved from an up-to-date compilation of chemical data and their processing from well-known discordancy and significance tests. The drift-corrected XRF intensity and its uncertainty were determined from mostly duplicate pressed powder pellets. The comparison of the OLR (linear correlation coefficient r∼0.9523–0.9964 and 0.9771–0.9999, respectively, for before and after matrix correction) and UWLR models (r∼0.9772–0.9976 and 0.9970–0.9999, respectively) clearly showed that the latter with generally higher values of r is preferable for routine calibrations of analytical procedures. Both calibrations were successfully applied to rock matrices, and the results were generally consistent with those obtained in other laboratories although the UWLR model showed mostly narrower confidence limits of the mean (slope and intercept) or lower uncertainties than the OLR. Similar sensitivity (∼2.69–46.17 kc·s−1·%−1 for the OLR and ∼2.78–59.69 kc·s−1·%−1 for the UWLR) also indicated that the UWLR could advantageously replace the OLR model. Another novel aspect is that the total uncertainty can be reported for individual chemical data. If the analytical instruments were routinely calibrated from the UWLR model, this action would make the science of geochemistry more quantitative than at present.