The Relationship between Deformation Localization and Catastrophic Failure in Fluid-Saturated Crystalline Rocks in Upper Crustal Conditions

Abstract

Previous studies show that the total volume of fractures increases non-linearly during macroscopic yielding as rocks approach failure in triaxial compression at the stress and temperature conditions of the upper crust. However, the factors that control the critical volume of fractures or the critical spatial organization of the fracture network that trigger failure remain unclear. To identify the fracture characteristics that determine the timing of macroscopic failure and localization of the fracture networks, we analyze data from six X-ray tomography experiments on Westerly granite with varying confining stress, fluid pressure, and amounts of preexisting damage. We develop machine learning models to predict 1) the timing of failure, 2) the localization of the fracture networks with the Gini coefficient of the fracture volume, and 3) the change in localization from one differential stress step to the next. When the models only have access to individual fracture characteristics, the fracture length produces the best predictions of the distance to failure. When the models have access to the fracture length and sets of other characteristics, the fracture volume, aperture, and distance between fractures produce the best predictions of the distance to failure. The characteristics that describe the time or loading in the experiment, the axial strain and differential stress, produce some of the best predictions of the Gini coefficient. The results are generally consistent among the different experiments, suggesting that the fracture characteristics that determine the timing of macroscopic failure, and the localization of the fracture network are independent of the range of confining stress, fluid pressure, and amount of preexisting damage tested here. Our results are consistent with the idea that monitoring the spatial distribution of deformation and changes in the seismic wave properties indicative of fracture growth may improve forecasting efforts of failure in the crust.

Keywords: Fractures, localization, catastrophic failure, machine learning, X-ray tomography