Epidote-amphibolites form along the plate interface during subduction infancy, and are stable in warm, mature subduction zones that generate slow earthquakes. Epidote-amphibolite rheology therefore likely influences plate-scale processes facilitating plate boundary formation, and grain-scale processes generating slip transients. We present optical and electron microscopy of naturally-deformed epidote-amphibolites from beneath the Oman ophiolite (~7–10 kbar, 400–550 °C) to characterize their deformation behavior. Epidote-amphibolites are fine-grained, strongly foliated and lineated, and exhibit polyphase fabrics in which amphiboles (~10–50 μm) and epidotes (~5–20 μm) are strain-accommodating phases. Two-point correlation connectivity analysis demonstrates that amphiboles are always well-connected, regardless of phase proportions/distributions. Electron Backscatter Diffraction reveals strong amphibole Crystallographic and Shape Preferred Orientations (CPOs and SPOs), subgrain geometries indicating (hk0)[001] slip, and high average Mean Orientation Spreads (MOS; ~6°), interpreted as coupled rigid rotation and dislocation glide. Epidotes, in contrast, record weak CPOs, low intragranular misorientations, moderate SPOs, and low MOS (~0–2°), interpreted as deformation by dissolution-precipitation creep. Depending on phase distributions, epidote-amphibolite rheology can be approximated as interconnected weak layers of amphibole dislocation glide, or a composite rheology of plasticity and fluid-assisted/diffusion-accommodated creep. We estimate strain rates from geologic and geochronological data (6 · 10-11 to 10-12 s-1), stress from quartz piezometry (11 – 45 MPa), and equivalent viscosities of 1016 – 1018 Pa-s. On tectonic timescales, such low viscosities are consistent with epidote-amphibolites serving as strain localizing agents during subduction infancy. On seismic timescales, coupled glide- and diffusion exemplify a strain-hardening deformation state that could culminate in creep transients.

A document by Hamed Amiri. Click on the document to view its contents.

Researchers analyzing data collected from borehole drilling projects can face dozens of terabytes of seismic, hydrologic, geologic, and rock mechanics data, including complex imagery, physical measurements, and expert-written reports. These diverse data sets play a pivotal role in understanding solid Earth processes. Ingesting and analyzing such data presents a colossal challenge that typically demands a team of experts and large amounts of time. The utilization of Artificial Intelligence (AI) and machine learning emerges as a compelling approach to help tackle the volume and complexity of drilling data. This paper presents an AI-based pipeline for ingesting data from the Oman Drilling Project’s Multi-borehole Observatory. The study focuses on the alteration of peridotite core segments taken from Borehole BA1B, utilizing a catboost classification model trained on an integrated data set of machine learning segmented core images, physical measurements, geological, lithographic data, and AI-summarized expert texts and feature selection. This paper’s central objective is to establish a repeatable, efficient pattern for processing such multifaceted borehole data through connecting fracture networks detected in the borehole BA1B imagery to the host rock alteration.

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 n-point correlation functions - known as statistical microstructural descriptors (SMDs) - from images. SMDs can then be used to generate statistically equivalent structures having larger sizes and additional dimensions – this process is known as $reconstruction$. We show that a deep-convolutional generative adversarial network trained with Wasserstein-loss and gradient penalty (WGAN-GP) results in a stable training and high-quality reconstructions of two-dimensional electron microscopy images of complex rock samples. To evaluate reconstruction performance, n-point polytope functions are calculated in both reconstructed and original microstructures and mean square error between them is used as a quality metric. These n-point polytope functions provide statistical information about symmetric, user-oriented higher-order geometrical patterns in microstructures. Our results show that GANs can naturally capture these higher-order statistics at short and long ranges. Furthermore, we compare our model with a benchmark stochastic reconstruction method based solely on two-point correlation. Our findings indicate that although yielding the same two-point statistics, two microstructures can be morphologically and structurally different, emphasising the need for coupling higher-order correlation functions with reconstruction methods. This is a critical step for future schemes that aim to reconstruct complex heterogeneous systems and couple microstructures to macroscopic phenomena.