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Detailing the relationship between stress and reactions in metamorphic rocks has been controversial, and much of the debate has centered on theory. Here, we add to this discussion and make a major advance by showing in time-resolved synchrotron microtomography experiments that a reacting and deforming sample experiencing an elastic differential stress produces a fabric orthogonal to the largest principal stress. This fabric forms very early in the reaction and can be shown to be unrelated to strain. The consequences of this are significant because a non-hydrostatic stress state is a very common geological occurrence. Our data provide the basis for new interpretations of the classical, and enigmatic, serpentine fabrics of Val Malenco, Italy, and Cerro del Almirez, Spain, where we relate the reported fabrics to transient, and cyclical, differential stresses from magma intrusion and the earthquake cycle. [ABSTRACT FROM AUTHOR]

Third- and fourth-generation synchrotron light sources with high fluxes and beam energies enable the use of innovative X-ray translucent experimental apparatus. These experimental devices access geologically relevant conditions whilst enabling in situ characterization using the spatial and temporal resolutions accessible at imaging beamlines. Here, Heitt Mjölnir is introduced, a heated miniature triaxial rig based on the design of Mjölnir, but covering a wider temperature range and larger sample volume at similar pressure capacities. This device is designed to investigate coupled thermal, chemical, hydraulic and mechanical processes from grain to centimetre scales using cylindrical samples of 10 mm × 20 mm (diameter × length). Heitt Mjölnir can simultaneously reach confining (hydraulic) pressures of 30 MPa and 500 MPa of axial stress with independently controlled sample pore fluid pressure < 30 MPa. This internally heated apparatus operates to temperatures up to 573 K with a minimal vertical thermal gradient in the sample of <0.3 K mm-1. This new apparatus has been deployed in operando studies at the TOMCAT (Swiss Light Source), I12 JEEP (Diamond Light Source) and PSICHE' (Synchrotron SOLEIL) beamlines for 4D X-ray microtomography with scan intervals of a few minutes. Heitt Mjölnir is portable and modular, allowing a wide range of 4D characterizations of low-grade metamorphism and deformational processes. It enables spatially and temporally resolved fluid-rock interaction studies at conditions of crustal reservoirs and is suitable for characterization of material properties in geothermal, carbonation or subsurface gas storage applications. Technical drawings and an operation guide are included in this publication. [ABSTRACT FROM AUTHOR]

X-ray computed tomography has established itself as a crucial tool in the analysis of rock materials, providing the ability to visualise intricate 3D microstructures and capture quantitative information about internal phenomena such as structural damage, mineral reactions, and fluid-rock interactions. The efficacy of this tool, however, depends significantly on the precision of image segmentation, a process that has seen varied results across different methodologies, ranging from simple histogram thresholding to more complex machine learning and deep learning strategies. The irregularity in these segmentation outcomes raises concerns about the reproducibility of the results, a challenge that we aim to address in this work. In our study, we employ the mass balance of a metamorphic reaction as an internal standard to verify segmentation accuracy and shed light on the advantages of deep learning approaches, particularly their capacity to efficiently process expansive datasets. Our methodology utilises deep learning to achieve accurate segmentation of time-resolved volumetric images of the gypsum dehydration reaction, a process that traditional segmentation techniques have struggled with due to poor contrast between reactants and products. We utilise a 2D U-net architecture for segmentation and introduce machine learning-obtained labelled data (specifically, from random forest classification) as an innovative solution to the limitations of training data obtained from imaging. The deep learning algorithm we developed has demonstrated remarkable resilience, consistently segmenting volume phases across all experiments. Furthermore, our trained neural network exhibits impressively short run times on a standard workstation equipped with a Graphic Processing Unit (GPU). To evaluate the precision of our workflow, we compared the theoretical and measured molar evolution of gypsum to bassanite during dehydration. The errors between the predicted and segmented volumes in all time-series experiments fell within the 2 % confidence intervals of the theoretical curves, affirming the accuracy of our methodology. We also compared the results obtained by the proposed method with standard segmentation methods and found a significant improvement in precision and accuracy of segmented volumes. This makes the segmented CT images suited for extracting quantitative data, such as variations in mineral growth rate and pore size during the reaction. In this work, we introduce a distinctive approach by using an internal standard to validate the accuracy of a segmentation model, demonstrating its potential as a robust and reliable method for image segmentation in this field. This ability to measure the volumetric evolution during a reaction with precision paves the way for advanced modelling and verification of the physical properties of rock materials, particularly those involved in tectono-metamorphic processes. Our work underscores the promise of deep learning approaches in elevating the quality and reproducibility of research in the geosciences. [ABSTRACT FROM AUTHOR]

In experiments designed to understand deep shear zones, we show that periodic porous sheets emerge spontaneously during viscous creep, forming a hydro-mechanical anisotropy that influences mass transfer. These findings challenge the current paradigm of viscosity in solid rocks. In particular, they showcase how shear zones may actively focus mass transport and highlight the possibility that viscous rocks could locally transition from flow to fracture. Our work demonstrates that viscosity in solids is not directly comparable to viscosity in fluids and this is consequential for a range of important solid Earth topics, like slow earthquakes, the flow of glacial ice and the tectonics of exoplanets. [ABSTRACT FROM AUTHOR]

Here we will address two possible applications of wavelet analysis for deformation microstructures: firstly, the isolation of features of interest and secondly the quantification of the frequency of those features.In microstructural analysis data is often complex with many features that have various fractal or periodic characteristics. Consequentially, when data is acquired it often contains an excess of data for the questions at hand. In image analysis of microstructures, data is often excluded qualitatively by eye. This is a time consuming exercise and might succumb to selection and confirmation biases. Here we show how wavelets can be used to find statistically significant features and isolate them. Furthermore, by virtue of the wavelet technique this analysis also yields the frequency of those features. Even more critically, the technique preserves the spatial arrangement of the features of interest. In this way, wavelet analysis overcomes some of the abstraction and loss of spatial information that classical Fourier frequency analysis is encumbered with. Wavelet analysis is routinely used in Geophysics and other wave based fields of study and is emerging in more of Earth sciences. Our analysis highlights two possibilities where the technique enhances the knowledge that can be obtained from quantitative microstructural analysis. [ABSTRACT FROM AUTHOR]