Predicting fracture network development in crystalline rocks

The geometric properties of fractures influence whether they propagate, arrest and coalesce with other fractures. Thus, quantifying the relationship between fracture network characteristics may help predict fracture network development, and hence precursors to catastrophic failure. To constrain the relationship and predictability of fracture characteristics, we deform eight rock cores under triaxial compression while acquiring in situ X-ray tomograms. The tomograms reveal precise measurements of the fracture network characteristics above 6.5 microns. We develop machine learning models to predict the value of each characteristic using the other characteristics, and excluding the macroscopic stress or strain imposed on the rock. The models predict fracture development more accurately in the experiments performed on granite and monzonite, than the experiments on marble. Fracture network development may be more predictable in these igneous rocks because their microstructure is more mechanically homogeneous than the marble, producing more systematic fracture development that is not strongly impeded by grain contacts and cleavage planes. The varying performance of the models suggest that fracture volume, length, and aperture are the most predictable of the characteristics, while fracture orientation is the least predictable. Orientation does not correlate with length, as suggested by the idea that the orientation evolves with increasing differential stress and thus fracture length. This difference between the observed and expected predictability of orientation highlights the significant influence of local stress perturbations on fracture growth within brittle material in laboratory-scale systems with many propagating and interacting fractures.