Our experimental time series of fracture networks and strain fields provide unique access to coalescing fractures and localizing strain at spatiotemporal resolutions previously unavailable. This access enables quantifying the dynamics of the transition from distributed to localized deformation. The geophysical community lacks a quantitative understanding of the criteria that govern the transition from distributed to localized deformation. To address this gap, we will apply spatial clustering statistics, machine learning, and numerical modeling. The spatial clustering results will quantify the localization process with clear, concise, and quantifiable metrics, and thereby provide a unifying framework to describe the localization process that leads to macroscopic failure. This analysis aims to determine if the clustering statistics of fracture and/or strain networks predicts the time to macroscopic failure. The machine learning analyses will predict the volume by which a fracture grows, magnitude of strain localization, and time to macroscopic failure. These analyses aim to isolate the criteria that govern fracture propagation and coalescence, and strain localization. Determining the factors that exert the greatest impact on fault network evolution may help focus seismic hazard assessments of natural fault systems. The numerical modelling analyses will help determine if the conclusions gleaned from the cm-scale experimental clustering and machine learning applications apply to km-scales and seismogenic depths, and will enable visualizing the evolving stress field. Comparison of the accuracy of the machine learning predictions that do and do not use information about the stress field will quantify the importance of characterizing this parameter. This quantification could help justify the cost of field measurements of the stress field in seismically active areas, or indicate that the stress field is not critical to characterize when predicting fault interaction.
Project leader: Jessica McBeck