Decades of seismological observations have highlighted the variability of foreshock occurrence prior to natural earthquakes, making thus difficult to track how earthquakes start. Here, we report on three stick-slip experiments performed on cylindrical samples of Indian metagabbro under upper crustal stress conditions (30-60 MPa). Acoustic emissions (AEs) were continuously recorded by 8 calibrated acoustic sensors during the experiments. Seismological parameters of the detected AEs (-8.8 <= Mw <= -7 ) follow the scaling law between moment magnitude and corner frequency that characterizes natural earthquakes. AE activity always increases towards failure and is found to be driven by along fault slip velocity. The stacked AE foreshock sequences follow an inverse power-law of the time to failure (inverse Omori), with a characteristic Omori time c inversely proportional to normal stress and nucleation length. AEs moment magnitudes increase towards failure, as manifested by a decrease in b-value from ~ 1 to ~ 0.5 at the end of the nucleation process. During nucleation, the averaged distance of foreshocks to mainshock continuously decreases, highlighting the fast migration of foreshocks towards the mainshock epicenter location, and stabilizing at a distance from the latter compatible with the predicted Rate-and-State nucleation size. Finally, the seismic component of the nucleation phase is orders of magnitude smaller than that of its aseismic component, which suggests that foreshocks are the byproducts of a process almost fully aseismic. Seismic/aseismic energy release ratio continuously increases during nucleation, which starts as a fully aseismic process and evolves towards a cascading process.

Recent experiments systematically explore rock friction under crustal earthquake conditions revealing that faults undergo abrupt dynamic weakening. Processes related to heating and weakening of fault surface have been invoked to explain pronounced velocity weakening. Both contact asperity temperature $T_a$ and background temperature $T$ of the slip zone evolve significantly during high velocity slip due to heat sources (frictional work), heat sinks (e.g. latent heat of decomposition processes) and diffusion. Using carefully calibrated High Velocity Rotary Friction experiments, we test the compatibility of thermal weakening models: (1) a model of friction based only on $T$ in an extremely simplified, Arrhenius-like thermal dependence; (2) a flash heating model which accounts for evolution of both $V$ and $T$; (3) same but including heat sinks in the thermal balance; (4) same but including the thermal dependence of diffusivity and heat capacity. All models reflect the experimental results but model (1) results in unrealistically low temperatures and models (2) reproduces the restrengthening phase only by modifying the parameters for each experimental condition. The presence of dissipative heat sinks in (3) significantly affects $T$ and reflects on the friction, allowing a better joint fit of the initial weakening and final strength recovery across a range of experiments. Temperature is significantly altered by thermal dependence of (4). However, similar results can be obtained by (3) and (4) by adjusting the energy sinks. To compute temperature in this type of problem we compare the efficiency of three different numerical solutions (Finite differences, wavenumber summation, and discrete integral).

Convolutional Neural Networks (CNNs) can detect patterns that are otherwise difficult to identify and have been shown to excel in predicting fault characteristics in laboratory shear experiments and slow slip \emph{in situ}. Here we show that a suitably designed CNN can be trained to identify some precursory change in the seismic signal preceding some large natural earthquakes by up to a few hours, with a variable success rate. We use 65 $\textrm{M}_w\geq 6$ events in the NE pacific in and around Japan from 2012 to 2022. By repeating the training/testing cycle with variable random initial weights, we obtained up to 98\% in training accuracy and 96\% in testing accuracy in discriminating noise and precursor windows. In the $\sim 3$ hours preceding the earthquakes, the network identifies precursors progressively more frequently as earthquake time approaches. A final subset of more recent seismic events was used for a further verification, with mixed results. While the network appears to differentiate noise and precursor with a statistically positive incidence, the results are highly variable depending on the events that are analysed, with poor potential for generalisation. This may indicate that not all earthquakes in the catalog contain precursor signals, or at least no signal similar to the training subset. Discriminative features between precursor and noise windows appear most dominant over a frequency range of $\approx$ 0.1-0.9 Hz (in particular $\approx$0.16 and $\approx$0.21 Hz) broadly coinciding with observations made elsewhere of microseismic noise and broadband slow earthquake signal \cite{masuda_bridging_2020}.