Sources and characteristics of acoustic emissions from mechanically stressed geologic granular media — A review

Abstract

The formation of cracks and emergence of shearing planes and other modes of rapid macroscopic failure in geologic granular media involve numerous grain scale mechanical interactions often generating high frequency (kHz) elastic waves, referred to as acoustic emissions (AE). These acoustic signals have been used primarily for monitoring and characterizing fatigue and progressive failure in engineered systems, with only a few applications concerning geologic granular media reported in the literature. Similar to the monitoring of seismic events preceding an earthquake, AE may offer a means for non-invasive, in-situ, assessment of mechanical precursors associated with imminent landslides or other types of rapid mass movements (debris flows, rock falls, snow avalanches, glacier stick-slip events). Despite diverse applications and potential usefulness, a systematic description of the AE method and its relevance to mechanical processes in Earth sciences is lacking. This review is aimed at providing a sound foundation for linking observed AE with various micro-mechanical failure events in geologic granular materials, not only for monitoring of triggering events preceding mass mobilization, but also as a non-invasive tool in its own right for probing the rich spectrum of mechanical processes at scales ranging from a single grain to a hillslope. We review first studies reporting use of AE for monitoring of failure in various geologic materials, and describe AE generating source mechanisms in mechanically stressed geologic media (e.g., frictional sliding, micro-crackling, particle collisions, rupture of water bridges, etc.) including AE statistical features, such as frequency content and occurrence probabilities. We summarize available AE sensors and measurement principles. The high sampling rates of advanced AE systems enable detection of numerous discrete failure events within a volume and thus provide access to statistical descriptions of progressive collapse of systems with many interacting mechanical elements such as the fiber bundle model (FBM). We highlight intrinsic links between AE characteristics and established statistical models often used in structural engineering and material sciences, and outline potential applications for failure prediction and early-warning using the AE method in combination with the FBM. The biggest challenge to application of the AE method for field applications is strong signal attenuation. We provide an outlook for overcoming such limitations considering emergence of a class of fiber-optic based distributed AE sensors and deployment of acoustic waveguides as part of monitoring networks.