Volcanic glass and its mixture with smectite are commonly observed in shallow parts of subduction zones. As volcanic glass layers often act as a glide plane to induce mass transportation such as submarine landslides, and because its alteration product, smectite, is one of the frictionally weakest geological materials, the frictional characteristics of volcanic glass-smectite mixtures are important for fault slip behavior in shallow parts of subduction zones. We performed a series of friction experiments on volcanic glass-smectite mixtures with different smectite contents at various velocity conditions from 10 μm/s to 1 m/s under an effective normal stress of 5 MPa and pore pressure of 10 MPa. In general, friction coefficients negatively depend on the smectite content at any velocity tested. We found that samples with smectite contents of 15-30 % showed a drastic slip-weakening behavior at intermediate velocities of 1-3 mm/s with a characteristic slip displacement of ~0.1 m. Finite element method modeling shows that thermal pressurization does not contribute to the observed weakening behavior. We propose that gouge fluidization or compaction-induced pore pressure increase may be the cause of the weakening. The slip-weakening behavior at intermediate velocities enlarges a critical nucleation length for frictional instability to 1-30 km, or prevent acceleration to seismic slip velocities. Therefore, gouges with minor amount of clay, such as subducting volcanic ash layers, may contribute to the occurrence of the at shallow depths in subduction zones.
Basin-scale quasi-geostrophic gyres are common features of large lakes subject to Coriolis force. Cyclonic gyres are often characterized by dome-shaped thermoclines that form due to pelagic upwelling which takes place in their center. At present, dynamics of pelagic upwelling in the Surface Mixed Layer (SML) of oceans and lakes are poorly documented. A unique combination of high-resolution 3D numerical modeling, satellite imagery and field observations allowed confirming for the first time in a lake, the existence of intense pelagic upwelling in the center of cyclonic gyres under strong shallow (summer) and weak deep (winter) stratified conditions/thermocline. Field observations in Lake Geneva revealed that surprisingly intense upwelling from the thermocline to the SML and even to the lake surface occurred as chimney-like structures of cold water within the SML, as confirmed by Advanced Very High-Resolution Radiometer data. Results of a calibrated 3D numerical model suggest that the classical Ekman pumping mechanism cannot explain such pelagic upwelling. Analysis of the contribution of various terms in the vertically-averaged momentum equation showed that the nonlinear (advective) term dominates, resulting in heterogeneous divergent flows within cyclonic gyres. The combination of nonlinear heterogeneous divergent flow and 3D ageostrophic strain caused by gyre distortion is responsible for the chimney-like upwelling in the SML. The potential impact of such pelagic upwelling on long-term observations at a measurement station in the center of Lake Geneva suggests that caution should be exercised when relying on limited (in space and/or time) profile measurements for monitoring and quantifying processes in large lakes.
Seismological data can provide timely information for slope failure hazard assessments, among which rockfall waveform identification is challenging for its high waveform variations across different events and stations. A rockfall waveform does not have typical body waves as earthquakes do, so researchers have made enormous efforts to explore characteristic function parameters for automatic rockfall waveform detection. With recent advances in deep learning, algorithms can learn to automatically map the input data to target functions. We develop RockNet via multitask and transfer learning; the network consists of a single-station detection model and an association model. The former discriminates rockfall and earthquake waveforms. The latter determines the local occurrences of rockfall and earthquake events by assembling the single-station detection model representations with multiple station recordings. RockNet achieves macro F1 scores of 0.990 and 0.981 in terms of discriminating earthquakes and rockfalls from other events with the single-station detection and association models, respectively.
Microseismicity is expected in potash mining due to the associated rock-mass response. This phenomenon is known, but not fully understood. To assess the safety and effciency of mining operations, producers must quantitatively discern between normal and abnormal seismic activity. In this work, statistical aspects and clustering of microseismicity from a Saskatchewan, Canada, potash mine are analyzed and quantified. Specifically, the frequency-magnitude statistics display a rich behavior that deviates from the standard Gutenberg-Richter scaling for small magnitudes. To model the magnitude distribution, we consider two additional models, i.e., the tapered Pareto distribution and a mixture of the tapered Pareto and Pareto distributions to fit the bi-modal catalog data. To study the clustering aspects of the observed microseismicity, the nearest-neighbor distance (NND) method is applied. This allowed the identification of potential cluster characteristics in time, space, and magnitude domains. The implemented modeling approaches and obtained results will be used to further advance strategies and protocols for the safe and effcient operation of potash mines.