A Machine Learning Approach to Single Garnet Geothermometry and Application to Tracing the Fingerprint of Superdeep Diamonds

Abstract

Abstract Estimating the equilibration temperatures of mantle‐derived garnets is crucial for assessing the diamond potential of kimberlites. Traditional garnet geothermometers require co‐existing mineral data or costly trace element analysis, limiting their practical use. As an alternative approach, based on the major and minor element composition of garnet alone, we first re‐calibrated an Mn‐in‐garnet thermometer using a newly compiled data set of garnets from well‐equilibrated peridotitic xenoliths with well‐constrained pressure‐temperature (P‐T) conditions. The re‐calibrated Mn‐in‐garnet thermometer, however, is only of intermediate accuracy, with a relatively large discrepancy relative to the most reliable multi‐phase thermometry, indicated by a high root mean square error value (RMSE = 79°C) across a temperature range from 900 to 1,400°C. In a second improve approach, we developed a new machine learning (ML)‐based garnet thermometer that demonstrated superior performance, achieving significantly better accuracy and reduced discrepancies (average RMSE = 61°C). The ML‐based garnet thermometer outperforms the Mn‐in‐garnet thermometer because it considers not only MnO but also other major and minor elements, particularly TiO 2 , revealed by the ML model to be critical for accurate prediction of garnet temperatures. Applying the ML‐based thermometer to garnet xenocrysts from kimberlites on the Slave and Kaapvaal cratons reveals that high numbers of sublithospheric (superdeep) diamonds are associated with significantly higher proportions of high‐T (>1,200°C) high‐Ti garnets, compared to kimberlites in which superdeep diamonds are either few or absent. This finding indicates that a number of kimberlites, not currently identified as containing superdeep diamond populations, are promising hosts of such diamonds.

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