Search

Fluid-rock interactions play a critical role in Earth’s lithosphere and environmental subsurface systems. In the absence of chemical mass transport, mineral-hydration reactions would be accompanied by a solid-volume increase that may induce differential stresses and associated reaction-induced deformation processes, such as dilatant fracturing to increase fluid permeability. However, the magnitudes of stresses that manifest in natural systems remain poorly constrained. We used optical and electron microscopy to show that one of the simplest hydration reactions in nature [MgO + H2O = Mg(OH)2] can induce stresses of several hundred megapascals, with local stresses of as much as ∼1.5 GPa. We demonstrate that these stresses not only cause fracturing but also induce plastic deformation with dislocation densities (1015 m−2) exceeding those typical of tectonically deformed rocks. If these reaction-induced stresses can be transmitted across larger length scales, they may influence the bulk stress state of reacting regions. Moreover, the structural damage induced may be the first step toward catastrophic rock failure, triggering crustal seismicity.

Key to most subsurface processes is to determine how structural and topological features at small length scales, i.e., the microstructure, control the effective and macroscopic properties of earth materials. Recent progress in imaging technology has enabled us to visualise and characterise microstructures at different length scales and dimensions. An approach to characterisation is the sampling of multi-point spatial correlation functions - known as statistical microstructural descriptors (SMDs) - from images. SMDs can then be used to generate statistically equivalent structures having different scales and additional dimensions – this process is known as $reconstruction$. We use higher-order SMDs ($n$-point polytope functions) to characterise two hydrothermally altered rocks as examples of quantifying varying degrees of geometric complexity in the Earth's lithosphere. Using a generative adversarial network (GAN), trained with Wasserstein-loss and gradient penalty, we subsequently reconstruct two-dimensional electron microscopy images of these rocks. In addition to improving the training stability of GANs, we show that our model is capable of reconstructing higher-order, spatially-correlated patterns of complex earth materials, capturing underlying structural and morphological properties. Our approach is critical to address a wide range of geoscientific challenges aiming at reconstructing morphology-dependent physical rock properties to, e.g.,: i) increase the number of digital samples from limited real samples to, e.g., assess the variability of rock transport properties; ii) perform upscaling, i.e., generate representative domains from high-resolution images of small complex samples and couple microstructures to macroscopic phenomena; iii) reconstruct 3D microstructures when only 2D images are available.

Fluid-rock interactions play a critical role in Earth’s lithosphere and in engineered subsurface systems. In the absence of chemical mass transport, mineral-hydration reactions will be accompanied by a solid-volume increase that may induce differential stresses and associated reaction-induced deformation processes, such as dilatant fracturing to increase fluid permeability. However, the magnitudes of stresses that manifest in natural systems remain poorly constrained. Here we show that the simplest hydration reaction in nature MgO + H2O⇔ Mg(OH)2 can induce stresses of several hundred megapascals, with local stresses up to ∼1.5 GPa. We demonstrate that these stresses are dissipated not only by fracturing but also induce plastic deformationwith dislocation densities (10^15m−2) exceeding those typical of tectonically deformedrocks. If these reaction-induced stresses can be transmitted across larger length scales they may influence the bulk stress state of reacting regions. Moreover, the structural damage induced may be the first step towards catastrophic rock failure, triggering crustal seismicity.