Integrating laboratory acoustic measurements, deep neural networks, and micro-CT imaging for characterizing rock brittle deformation

Abstract

Brittle deformation is prevalent in both geological processes and engineered structures, so probing its actions is an important task as much for Earth materials and engineered ones. To characterize brittle deformation, acoustic waves are especially useful in revealing deformation processes. To promote the use of acoustic techniques, we present an integrated characterization approach that includes both acoustic data collection and analysis. By customizing a rock sample and acoustic sensor assembly, we incorporate acoustic data acquisition into a core holder system that accommodates relatively small samples (2.54 cm diameter) under triaxial loading. Along with fast and high-resolution acoustic waveform recording, the compact design facilitates convenient collection of high-quality acoustic data. To meet the challenge of efficiently and accurately picking P-wave arrivals for hundreds of thousands of acoustic waveforms, we modified and implemented a deep neural network model from the seismology literature called PhaseNet. After training with an augmented dataset of manually-picked arrivals (a total of around 50,000 waveforms), the modified PhaseNet model achieved more than 88% (96%) picking accuracy within ±1 μs (±2 μs) time residual relative to manual picks. This demonstrates the potential of integrating deep learning techniques into the workflow of acoustic data analysis for rapid and accurate extraction of valuable information from a large acoustic dataset. Finally, we conducted high-resolution micro-computed tomography (micro-CT) to inform and complement acoustic characterization at micron- and centimeter-scales. Microscopic observations validate the spatial development of two macroscopic fractures, and suggest that deformation-induced changes in velocity need to be incorporated for accurately locating microcracking events. Thus, integrating acoustic monitoring, a deep neural network, and micro-CT imaging offers an effective means to understand brittle deformation from micro to centimeter scales.