Computationally Enabled 4D Visualizations Facilitate the Detection of Rock Fracture Patterns from Acoustic Emissions

Monitoring and predicting crack propagation inrock using acoustic emission (AE) technology is integral to a variety of sub-disciplines in the geosciences. The utility of existing AE data, however, is severely limited by prevailing visualization techniques, which suffer from problems of occlusion. Here, we introduce a novel approach to visualize 3D data through time (4D data) using unfiltered individual (AE) event data collected from a granite boulder for a period over 3 years. We implement a 3D extension of Ripley's K function to evaluate the magnitude of clustering, and use the scale at which clustering is strongest to parameterize three-dimensional kernel density estimation (3DKDE) of AE events. We develop a parallel approach that features a load balancing technique to decrease the computational effort for 3DKDE and hence, reduce execution time. The results from the 3DKDE allow for comprehensible visualization of high AE density areasbestreflecting the actuallocation of subcritical crackingand their changes through time, which is a substantial improvement over most existing methods. Our framework is scalable and portable to a variety of other disciplines such as epidemiology, ecology, and any point data by extension. Assumptions and limitations are identified as well as possible future research directions.