This study focuses on unraveling the microphysical origins of the nonlinear elastic effects, which are pervasive in the Earth’s crust. Here, we examine the influence of grain shape on the elastic nonlinearity of granular assemblies. We find that the elastic nonlinearity of angular sand particles is of the same order of magnitude as that previously measured in spherical glass beads. However, while the elastic nonlinearity of glass beads increases by an order of magnitude with RH, that of sand particles is rather RH independent. We attribute this difference to the angularity of sand particles: absorbed water on the spherical grains weakens the junctions making them more nonlinear, while no such effect occurs in sand due to grain interlocking. Additionally, for one of the nonlinear parameters that likely arises from shearing/partial slip of the grain junctions, we observe a sharp amplitude threshold in sand which is not observed in glass beads.

We exploit nonlinear elastodynamic properties of fractured rock to probe the micro-scale mechanics of fractures and understand the relation between fluid transport and fracture aperture and area, stiffness proxy, under dynamic stressing. Experiments are conducted on rough, tensile-fractured Westerly granite specimen subject to triaxial stresses. Fracture permeability is measured from steady-state fluid flow with deionized water. Pore pressure oscillations are applied at amplitudes ranging from 0.2 to 1~MPa at 1~Hz frequency. During dynamic stressing we transmit acoustic signals through the fracture using an array of piezoelectric transducers (PZTs) to monitor the evolution of fracture interface properties. We examine the influence of fracture aperture and contact area by conducting measurements at effective normal stresses of 10, 12.5, 15, 17.5, and 20~MPa. Additionally, the evolution of contact area with stress is characterized using pressure sensitive film. These experiments are conducted separately with the same fracture and they map contact area at stresses from 9 to 21~MPa. The resulting ‘true’ area of contact measurements made for the entire fracture surface and within the calculated PZT sensor footprints, numerical modeling of Fresnel zone. We compare the elastodynamic response of the the fracture using the stress-induced changes ultrasonic wave velocities for a range of transmitter-receiver pairs to image spatial variations in contact properties, which is informed by fracture contact area measurements. These measurements of the nonlinear elasticity are related to the fluid-flow, permeability, in response to dynamic stressing and similar comparisons are made for the slow-dynamics, recovery, of the fracture interface following the stress perturbations.

Machine learning (ML) techniques have become increasingly important in seismology and earthquake science. Lab-based studies have used acoustic emission data to predict time-to-failure and stress state, and in a few cases the same approach has been used for field data. However, the underlying physical mechanisms that allow lab earthquake prediction and seismic forecasting remain poorly resolved. Here, we address this knowledge gap by coupling active-source seismic data, which probe asperity-scale processes, with ML methods. We show that elastic waves passing through the lab fault zone contain information that can predict the full spectrum of labquakes from slow slip instabilities to highly aperiodic events. The ML methods utilize systematic changes in p-wave amplitude and velocity to accurately predict the timing and shear stress during labquakes. The ML predictions improve in accuracy closer to fault failure, demonstrating that the predictive power of the ultrasonic signals improves as the fault approaches failure. Our results demonstrate that the relationship between the ultrasonic parameters and fault slip rate, and in turn, the systematically evolving real area of contact and asperity stiffness allow the gradient boosting algorithm to ‘learn’ about the state of the fault and its proximity to failure. Broadly, our results demonstrate the utility of physics-informed machine learning in forecasting the imminence of fault slip at the laboratory scale, which may have important implications for earthquake mechanics in nature.