The overwhelming amount of seismic, geodesic and in-situ observations accumulated over the last 30 years clearly indicate that, from a mechanical point of view, faults should be considered as both damageable elastic solids in which highly localized features emerge as a result of very short-term brittle processes and materials experiencing ductile strains distributed in large volumes and over long time scales. The interplay of both deformation mechanisms, brittle and ductile, give rise to transient phenomena associating slow slip and tremors, known as slow earthquakes, which dissipate a significant amount of stress in the fault system. The physically-based numerical models developed to improve our comprehension of the mechanical and dynamical behaviour of faults must therefore have the capacity to treat simultaneously both deformation mechanisms and to cover a wide range of time scales in a numerically efficient manner. This capability is essential, both for simulating accurately their deformation cycles and for improving our interpretation of the available observations. In this paper, we present a numerically efficient visco-elasto-brittle numerical framework that can simulate transient deformations akin to that observed in the context of subduction zones, over the wide range of time scales relevant for slow earthquakes. We implement the model in idealized simple shear simulations and explore the sensitivity of its behavior to the value of its main mechanical parameters.

Continuous seismograms contain a wealth of information with a large variety of signals with different origins. Identifying these signals is a crucial step in understanding physical geological objects. We propose a strategy to identify classes of seismic signals in continuous single-station seismograms in an unsupervised fashion. Our strategy relies on extracting meaningful waveform features based on a deep scattering network combined with an in- dependent component analysis. Based on the extracted features, agglomerative clustering then groups these waveforms in a hierarchical fashion and reveals the process of clustering in a dendrogram. We use the dendrogram to explore the seismic data and identify different classes of signals. To test our strategy, we investigate a two-day-long seismogram collected in the vicinity of the North Anatolian Fault, Turkey. We analyze the automatically inferred clusters' occurrence rate, spectral characteristics, cluster size, and waveform and envelope characteristics. At a low level in the cluster hierarchy, we obtain three clusters related to anthropogenic and ambient seismic noise and one cluster related to earthquake activity. At a high level in the cluster hierarchy, we identify a seismic crisis that includes more than 200 repeating events and high-frequent signals with correlated envelopes and an anthropogenic origin. The application shows that the cluster hierarchy helps to identify particular families of signals and to extract subclusters for further analysis. This is valuable when certain types of signals, such as earthquakes, are under-represented in the data. The proposed method may also successfully discover new types of signals since it is entirely data-driven.

The 17 August 1999 $M_{w}$7.4 Izmit earthquake ruptured the western section of the North Anatolian Fault Zone (NAFZ) and strongly altered the fault zone properties and stress field. Consequences of the co- and post-seismic stress changes were seen in the spatio-temporal evolution of the seismicity and in the surface slip rates. Thirteen years after the Izmit earthquake, in 2012, the dense seismic array DANA was deployed for 1.5 years. We built a new catalog of microseismicity (M < 2) by applying our automated detection and location method to the DANA data set. Our method combines a systematic backprojection of the seismic wavefield and template matching. We analyzed the statistical properties of the catalog by computing the Gutenberg-Richter b-value and by quantifying the amount of temporal clustering in groups of nearby earthquakes. We found that the microseismicity mainly occurs off the main fault and that the most active regions are the Lake Sapanca step-over and near the Akyazi fault. Based on previous studies, we interpreted the b-values and temporal clustering \textit{i}) as indicating that the Akyazi seismicity is occurring in high background stresses and is driven by the Izmit earthquake residual stresses, and \textit{ii}) as suggesting evidence that intricate seismic and aseismic slip was taking place on heterogeneous faults at the eastern Lake Sapanca, near the brittle-ductile transition. Geodesy shows enhanced north-south extension around Lake Sapanca following the Izmit earthquake, therefore, the seismicity supports the possibility of slow slip at depth in the step-over.

Monitoring changes of seismic properties at depth can provide a first order insight into Earth's dynamic evolution. Coda wave interferometry is the primary tool for this purpose. This technique exploits small changes of waveforms in the seismic coda and relates them to temporal variations of attenuation or velocity at depth. While most existing studies assume statistically homogeneous scattering strength in the lithosphere, geological observations suggest that this hypothesis may not be fulfilled in active tectonic or volcanic areas. In a numerical study we explore the impact of a non-uniform distribution of scattering strength on the spatio-temporal sensitivity of coda waves. Based on Monte Carlo simulation of the radiative transfer process, we calculate sensitivity kernels for three different observables, namely travel-time, decorrelation and intensity. Our results demonstrate that laterally varying scattering properties can have a profound impact on the sensitivities of coda waves. Furthermore, we demonstrate that the knowledge of the mean intensity, specific intensity and energy flux, governed by spatial variation of scattering strength, is key to understanding the decorrelation, travel-time and intensity kernels, respectively. A number of previous works on coda wave sensitivity kernels neglect the directivity of energy fluxes by employing formulas extrapolated from the diffusion approximation. In this work, we demonstrate and visually illustrate the importance of the use of specific intensity for the travel-time and scattering kernels, in the context of volcanic and fault zone setting models. Our results let us foresee new applications of coda wave monitoring in environments of high scattering contrast.