the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The secret life of garnets: A comprehensive, standardized dataset of garnet geochemical analyses integrating localities and petrogenesis
Morgan Gabor
Isabella Lupini
Randolph Rutledge
Julia Ann Nord
Shuang Zhang
Asmaa Boujibar
Emma S. Bullock
Michael J. Walter
Kerstin Lehnert
Frank Spear
Shaunna M. Morrison
Robert M. Hazen
Abstract. Integrating mineralogy with data science is critical to modernizing Earth materials research and its applications to geosciences. Data were compiled on 95,588 garnet sample analyses from a variety of sources, ranging from large repositories (EarthChem, RRUFF, MetPetDB) to individual peer-reviewed literature. An important feature is the inclusion of mineralogical “dark data” from papers published prior to 1990. Garnets are commonly used as indicators of formation environments, which directly correlate with their geochemical properties; thus, they are an ideal subject for the creation of an extensive data resource that incorporates composition, locality information, paragenetic mode, age, temperature, pressure, and geochemistry. For the data extracted from existing databases, we increased the resolution of several key aspects, including petrogenetic and paragenetic attributes, which we extended from generic material type (e.g., igneous, metamorphic) to more specific rock type names (e.g., diorite, eclogite, skarn) and locality information, increasing specificity by examining the continent, country, area, geological context, longitude, and latitude. Likewise, we implemented a broad silica confidence interval to exclude samples of questionable composition from further analysis. This comprehensive dataset of garnet information is an open-access resource available in the Evolutionary System of Mineralogy Database (ESMD) for future mineralogical studies, paving the way for characterizing correlations between chemical composition and paragenesis through natural kinds clustering. We encourage scientists to contribute their own unpublished and unarchived analyses to the growing data repositories of mineralogical information that are increasingly valuable for advancing scientific discovery.
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Kristen Chiama et al.
Status: open (until 06 Jul 2023)
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RC1: 'Comment on essd-2023-45', Anonymous Referee #1, 02 May 2023
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General Comments
The manuscript reports on a database of chemical analyses, ages, localities, paragenesis, P-T conditions etc. of garnets and some preliminary interpretations.
1) The dataset could be indeed be very interesting if obviously wrong entries would be eliminated, and a more convincing quality control would be used, e.g. by calculation of garnet species or endmembers from the analyses.
Only a very small part of the data presented in the database is new and original. The quality of the new data is very good. However, the authors have also included analyses of inclusion minerals as “garnet” analyses in the database. I cannot see any use of including such obvious wrong analyses in the data set.
A similar problem is that the authors include data from older peer reviewed publications (the “dark data”) that are clearly in the original publication not identified as garnet, but as whole rock analyses of garnet-bearing rocks, such as quartzites. These include:
Project ID 43-52 are not spessartine mineral analyses but so-called “coticules”, i.e. garnet-bearing quartzites and one of the authors, KC, has even included bulk analyses of slates and volcanic rocks as “garnet” analyses (see Herbosch et al. 2016, Table 3). Project ID 146-171 are quartzite whole rock analyses, not garnets (Reinecke et al. 1986 not Reincke as stated in the database).
Thus, whole rock analyses were included in the data set as mineral analyses. These were avoidable errors of the authors. These inconsistent data must be eliminated.
2) The authors use a “Silica Confidence Interval” (SCI) method to exclude samples of questionable composition from further analysis. This method seems to identify the above mentioned whole rock analyses of quartzites as unlikely of garnets and analyses of minerals that are not garnets but pyroxenes or spinel group minerals from the EarthChem database. However, it also seems to eliminate analyses, e.g. of henritermierites (Project ID 60-61), titanian andradites, schorlomites, kimzeyites, katoite-rich (hydro)grossulars of high quality! Thus, the SCI method is not very useful if the mineral species is not considered. Partial analysis of inclusions and garnet, a concern of the authors, will often not be identified correctly by this method.
A much better method to evaluate the quality of garnet analyses would be to calculate the endmember species from the chemical analysis using the approach of Locock (2008) and Grew et al. (2013) and calculate a “Quality Index” as suggested by Locock (2008). See Hawthorne (2021, Can. Mineral. 59, 169ff).
3) The discussion and interpretation of the data set is focused mainly on frequency plots of major (and some minor) elements and on the binary correlations of elements for various “material types” (igneous, metamorphic, detrital and unknown), in my view, the least reliable categorization of garnets (see below).
The dataset is heavily biased by garnets from the mantle (mostly brought to the surface by volcanic rocks) and by a study of garnets in a single amphibolite from the crust. The authors do not eliminate these overrepresented data in their data evaluation or weight them accordingly. Thus, any meaningful evaluation of the data must consider or correct for the bias.
The interpretation of the data, the main part of the manuscript, is therefore not very insightful. The interpretation of observed correlations between two elements as binary series of two garnet species on the other hand, is trivial if only binary correlations are studied. Why not use multivariate statistical methods or explore ternary compositions? The lengthy discussion of the binary element correlations and the frequency plots of strongly biased data stands in contrast to the few new insights gained from the analysis of the database. Some of the conclusions are probably wrong (see below).
4) The discrimination between an igneous and a metamorphic origin for mantle garnets is a question of semantics and ambiguous. I would rather suggest that the authors discuss garnets from distinct “paragenesis” instead of their “material types”. See for example the approach of Krippner et al. (2014 Sed. Geol. 306, 36ff) - a relevant publication not cited by the authors.
Igneous versus metamorphic origin of Earth’s mantle materials: The authors (and the sources they use) classify all ultramafic/peridotitic materials as “igneous” and all eclogites as “metamorphic”. This is an arbitrary decision. If a fertile mantle lherzolite is partially molten, a basaltic liquid extracted and then crystallizes within the upper mantle, it will have an eclogite-like mineral paragenesis consisting of pyrope- and grossular-rich almandine garnet and an omphacitic clinopyroxene. The authors will classify the rock and garnet as “metamorphic”, although it is obviously an igneous rock and mineral. An ultramafic rock of bridgmanite-ferropericlase composition from the lower mantle that is brought by diapirism into the upper mantle will be transformed in solid state thus by a metamorphic process into a rock of garnet-bearing peridotite paragenesis. Thus, the discrimination between (and discussion of) compositions of igneous and metamorphic rocks makes little sense in the realm of mantle rocks, the overwhelming lithology in the database. This problem can also be seen in the material type classification of majorite analyses from inclusions in diamonds (see below). Thus, similar to the authors’ use of the class “detrital”, I would suggest that the authors use the term “mantle” in the category “Material”. But it is recommended that the authors discuss the garnet compositions of distinct paragenetic assemblages not the ambiguous “Materials”.
Specific comments:
line 112: Goldmannite is defined as the Ca3V3+2Si3O12 endmember, not as Ca3[V,Al,Fe,Ti]2Si3O12. It might (and it always does) contain additionally tri- or tetravalent cations in the octahedral site such as Al, Fe3+ and Ti4+ or very rarely Ti3+, but these elements are not essential (e.g. Grew et al. 2013) and should not be reported in the formula of a mineral species.
line 119: First formation of almandine around 4.0 to 3.5 Ga: Some of the Hadean zircons (> 4.0 Ga) are probably derived from felsic continental crust (e.g. Zhong et al. 2023 Comm. Earth and Env. https://doi.org/10.1038/s43247-023-00731-7 and references therein) that could also contain almandine. I can see no indication for this late suggested appearance of almandine (and spessartine).
line 124: Uvarovite does not occur or form in “igneous environments”. It is rather a typical metasomatic or better hydrothermal mineral (see e.g. Melcher et al. J. Petrol. 38, 1419ff and Farré-de-Pablo et al. 2021 Mineralium Deposita 57, 955ff and references therein).
line 435: The authors use the category ‘almandine-pyrope’ for garnets near 50-50 compositions. An approach not supported by the IMA convention, but in my (and some other’s) view quite useful in practice. But what is the meaning of - and the reason to include - the category ‘pyrope-almandine’ then (see e.g. Fig.1)? I would suggest to merge these two categories and those of the other intermediate species with “flipped” composite names.
Fig.1: The authors should eliminate the following “Mineral” categories, as they are meaningless:
“Andradite-Grandit” (single entry): “grandite” is not a garnet species but an acronym derived from grossular-andradite for garnets of the grossular-andradite join. Thus, andradite-grossular-andradite makes no sense.
“Piemontite-Spessartin” (15 entries): “piemontite is a Mn-rich species of the epidote family not a garnet. The reported analyses have 0.4 to 2.2 % K2O and more than 70% SiO2 and only very minor MnO and Mn2O3 concentrations (<3 wt%). This is a very clear misidentification by Chiama et al. The original publication (Reinecke et al. 1985 not Reincke et al. 1985) unambiguously says “piemontite-spessartine and spessartine quartzite”. Thus these analyses are bulk XRF analyses of various quartzites and not garnet mineral analyses! The garnet analyses are presented for this locality in Reinecke (1986) in the same journal, but they were not included in the database. Why and how the authors have selected this source?
line 679-683 and Fig 6a: The discussion of age distributions in terms of mineral evolution is in my view misleading. The overwhelming majority of garnet “ages” relate to mantle xenoliths transported by explosive volcanism to the surface and the reported ages are overwhelmingly the ages of the kimberlite eruptions. As the timing of kimberlite volcanism is in almost all cases unrelated to garnet growth in the mantle rocks, the discussion of age distribution is meaningless for age distribution of garnet growths. Thus, only age values of directly dated garnets should be evaluated here.
line 765: For metasomatic garnets, dominated by skarn assemblages, a significant correlation between Fe3+ and Al3+ is found that is later interpreted as representing the binary substitution between andradite and grossular. The other significant correlation between Fe3+ and Si is not discussed. Why? This correlation is simply a consequence of interpreting mass-percentages instead of molar units or endmembers. Andradite with a full occupancy of Si on the tetrahedral site has only 35 mass-% SiO2, while grossular lacking any katoite (or hydrous) component has 40 mass-% SiO2. Thus, the negative correlation of Fe3+ and Si is simply a consequence of the interpretation of mass percentages and is not related to any substitution of Si by Fe3+.
line 927: the moderate to weak correlation for Mg-Si in the “metamorphic matrix” “may be caused by majorite analyses”. The correlation coefficient is only 0.126. The 156 majorite analyses in the database are classified as extraterrestrial (4 entries), igneous (28 entries), metamorphic (39 entries) and unknown (85 entries). All igneous and unknown majorites are inclusions in diamonds. 22 of the “metamorphic” majorites are also inclusions in diamonds and the remaining 17 analysis formed in an amphibolite with an impact setting. Here you can see the significant problem of categorizing of materials from the mantle. In the category “metamorphic” 24,601 garnet analyses are plotted and the 39 majorite analyses from a metamorphic setting should be responsible for the moderate to weak correlation between Mg and Si? I doubt that. The correlation is again related to effects of discussing mass-percentages instead of molar proportions. Thus, I strongly recommend to discuss molar proportions or endmembers.
line 935-6. “Unknown” matrix: “The Mg - Si relationship represents majorite garnets”. Here the correlation coefficient Mg-Si is much higher (0.491). Again, only 85 majorite analysis should influence the correlation of 9476 garnets? It is more probable that garnets with high pyrope content, the most common Mg-rich endmember garnet, also have higher Si values, as pyrope is the garnet with the highest Si content on a mass or weight basis.
References: Where are the full references of all the publications cited in the database? I have checked some and found many typos, especially in the new additions from the authors.
Citation: https://doi.org/10.5194/essd-2023-45-RC1
Kristen Chiama et al.
Data sets
ESMD - Garnet Dataset Kristen Chiama, Morgan Gabor, Isabella Lupini, Randolph Rutledge, Julia Ann Nord, Shuang Zhang, Asmaa Boujibar, Emma S. Bullock, Michael J. Walter, Kerstin Lehnert, Frank Spear, Shaunna Morrison, Robert M. Hazen https://doi.org/10.48484/camh-xy98
Kristen Chiama et al.
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