Acoustic-Friction Networks and the Evolution of Precursor Rupture Fronts in Laboratory Earthquakes

Abstract

The evolution of shear rupture fronts in laboratory earthquakes is analysed with the corresponding functional networks, constructed over acoustic emission friction-patterns. We show that the mesoscopic characteristics of functional networks carry the characteristic time for each phase of the rupture evolution. The classified rupture fronts in network states–obtained from a saw-cut fault and natural faulted Westerly granite - show a clear separation into three main groups, indicating different states of rupture fronts. With respect to the scaling of local ruptures' durations with the networks' parameters, we show that the gap in the classified fronts could be related to the possibility of a separation between slow and regular fronts.

Faulting of Rocks in a Three-Dimensional Stress Field by Micro-Anticracks

“…To build networks, we use the algorithm introduced in17. We built a network on each time step in which the nodes correspond with acoustic sensors where elastic “excitement” induces voltage-fluctuations in them.…”

“…Inset shows events from the CTT at the same plane with three main classified classes corresponding to three classes of rupture fronts energies17. Class I, II and III corresponds with slow, regular and very fast weakening rates, respectively.…”

Microscopic Evolution of Laboratory Volcanic Hybrid Earthquakes

“…The first intriguing result of the analysis is that we find that ultrasound excitations possess patterns of temporal evolution of network parameters that are universal among recorded events22232425. The appearance of universal patterns in any measure of excited signals shows the robustness of the collective process in the source(s) against the much faster scattering processes.…”

“…From this perspective, the system relaxes in multiple stages and time scales rather than single time scale. In general, the aforementioned phases are typical for dry events and simple friction tests conducted under dry conditions2223242526 (Fig. S2).…”

“…Further analysis of acoustic networks shows an intriguing “ pairing ” mechanism, in which kink-pair like structures (i.e., defects in the network structure) form in the network parameters, governing the oscillations of Q(t) or other relevant parameters23. Furthermore, comparing the trend of Q(t) with the power spectral density (Figs 1e and S4–S6) shows that the onset of the W-phase coincides with a dramatic shift in the dominant frequency.…”