Deciphering High-Pressure metamorphism in collisional context using microprobe-mapping methods : application to the Stak eclogitic massif (NW Himalaya)

The Stak massif, northern Pakistan, is a newly recognized occurrence of eclogite formed by the subduction of the northern margin of the Indian continent in the northwest Himalaya. Although this unit was extensively retrogressed during the Himalayan collision, records of the high-pressure (HP) event as well as a continuous pressure-temperature (P-T) path were assessed from a single thin section using a new multiequilibrium method. This method uses microprobe X-ray compositional maps of garnet and omphacitic pyroxene followed by calculations of ∼200,000 P-T estimates using appropriate thermobarometers. The Stak eclogite underwent prograde metamorphism, increasing from 650 °C and 2.4 GPa to the peak conditions of 750 °C and 2.5 GPa, then retrogressed to 700–650 °C and 1.6–0.9 GPa under amphibolite-facies conditions. The estimated peak metamorphic conditions and P-T path are similar to those of the Kaghan and Tso Morari high- to ultrahigh-pressure (HP-UHP) massifs. We propose that these three massifs define a large HP to UHP province in the northwest Himalaya, comparable to the Dabie-Sulu province in China and the Western Gneiss Region in Norway.

Functional error modeling for uncertainty quantification in hydrogeology

Approximate models (proxies) can be employed to reduce the computational costs of estimating uncertainty. The price to pay is that the approximations introduced by the proxy model can lead to a biased estimation. To avoid this problem and ensure a reliable uncertainty quantification, we propose to combine functional data analysis and machine learning to build error models that allow us to obtain an accurate prediction of the exact response without solving the exact model for all realizations. We build the relationship between proxy and exact model on a learning set of geostatistical realizations for which both exact and approximate solvers are run. Functional principal components analysis (FPCA) is used to investigate the variability in the two sets of curves and reduce the dimensionality of the problem while maximizing the retained information. Once obtained, the error model can be used to predict the exact response of any realization on the basis of the sole proxy response. This methodology is purpose-oriented as the error model is constructed directly for the quantity of interest, rather than for the state of the system. Also, the dimensionality reduction performed by FPCA allows a diagnostic of the quality of the error model to assess the informativeness of the learning set and the fidelity of the proxy to the exact model. The possibility of obtaining a prediction of the exact response for any newly generated realization suggests that the methodology can be effectively used beyond the context of uncertainty quantification, in particular for Bayesian inference and optimization.

Dehydration of the zeolite merlinoite from the Khibiny massif, Russia: an in situ temperature-dependent single-crystal X-ray study

Dehydration behaviour of the zeolite merlinoite, NaK11[Al12Si20O64]·15H2O, from the Khibiny massif (Russia) was studied by means of single-crystal X-ray diffraction conjoined with step-wise heating to 225 C. At room temperature merlinoite has the space group Immm with a = 14.0312(5), b = 14.2675(6), c = 10.0874(4) Å, and V = 2019.40(14) Å3. At 75 °C the merlinoite structure undergoes pronounced dehydration accompanied by a phase transition to a structure that has the space group P42/nmc and remains consistent at elevated temperature. A fully dehydrated phase occurs at 200 °C (at 225 °C: a = 13.341(4), b = 13.341(4), c = 9.707(4) Å, V = 1727.7(12) Å3). Dehydration-induced framework distortion and symmetry were found to be different from those observed for synthetic potassium merlinoite with the K11.5[Al11.5Si20.5O64]·15H2O composition.