Here we present the results of a kinematic slip model of the 2020 M_w 6.7 Doğanyol-Sivrice, Turkey Earthquake, the most important event in the last 50 years on the East Anatolian Fault zone. Our slip model is constrained by two Sentinel-1 interferograms and by 5 three-component high-rate GNSS recordings close to the earthquake source. We find that most of the slip occurs predominantly in three regions, two of them at between 2 and 10 km depth and a deeper slip region extending down to 20 km depth. We also relocate the first two weeks of aftershocks and find a distribution of events that agrees with these slip features. The HR-GNSS recordings suggest a predominantly unilateral rupture with the effects of a directivity pulse clearly seen in the waveforms and in the measure peak ground velocities. The slip model supports rupture propagation from northeast to southwest at a relatively slow speed of 2.2 km/s and a total source duration of ∼20 s. In the absence of near-source seismic stations, space geodetic data provide the best constraint on the spatial distribution of slip and on its time evolution.

We identify the source of the M_w = 6.4 earthquake that rocked north-central Albania on November 26, 2019 02:54 UTC. We use synthetic aperture radar interferograms tied to the time series of coordinates of two permanent Global Navigation Satellite System (GNSS) stations (DUR2 and TIR2). We model the source by inverting the displacement data. Assuming in our model a half-space elastic medium and uniform slip along a rectangular fault surface, we invert the 104 picked measurements on a couple of ascending and descending interferograms to calculate the parameters of the fault. All inversions made with different input parameters converge towards a stable and robust solution with root mean square (r.m.s.) residual of 5.4 mm, thus ~1/5 of a fringe. They reveal that the earthquake occurred deep in the crust on a low-angle fault (23°) dipping towards east with a centroid at 16.5 km depth. The best-fitting length and width of the fault are 22 and 13 km, and the reverse slip, 0.55 m. The seismic moment deduced from our model agrees with those of the published seismic moment tensors. This geometry is compatible with a blind thrust fault that may root on the main basal thrust, i.e., along the thrust front that separates Adria–Apulia from Eurasia. It is notable that there is a 123 ns yr⁻¹ active shortening of the crust between the GNSS stations DUR2-TIR2 (equivalent to a shortening rate of 3.6 mm yr⁻¹), and roughly in the east–west direction. Given this amount of strain the recurrence time of M6+ earthquakes along this fault should be of the order of 150 years.

We studied the kinematic behaviour of active landslides at several localities in the area of Panachaikon Mountain, Achaia (Peloponnese, Greece) using Sentinel (C-band) InSAR time series analysis. We processed LiCSAR interferograms using the SBAS tool, and we obtained average displacement maps for the period 2016–2021. We found that the maximum displacement rate of each landslide is located at about the center of it. The average E-W velocity of the Krini landslide is ~3 cm/year (toward the east) and 0.6 cm/year downward. The line-of-sight (LOS) velocity of the landslide (descending orbit) compares well to a co-located GNSS station within (±) 3 mm/yr. Our results also suggest a correlation between rainfall and landslide motion. For the Krini landslide, a cross-correlation analysis of our data suggests that the mean time lag was 13.5 days between the maximum seasonal rainfall and the change in the LOS displacement rate. We also found that the amount of total seasonal rainfall controls the increase in the displacement rate, as 40–550% changes in the displacement rate of the Krini landslide were detected, following to a seasonal maximum of rainfall values at the nearby meteorological station of Kato Vlassia. According to our results, it seems that large part of this mountainous region of Achaia suffers from slope instability that is manifested in various degrees of ground displacement greatly affecting its morphological features and inhabited areas.

Earthquake hazard analyses rely on seismogenic source models. These are designed in various fashions, such as point sources or area sources, but the most effective is the three-dimensional representation of geological faults. We here refer to such models as fault sources. This study presents the European Fault-Source Model 2020 (EFSM20), which was one of the primary input datasets of the recently released European Seismic Hazard Model 2020. The EFSM20 compilation was entirely based on reusable data from existing active fault regional compilations that were first blended and harmonized and then augmented by a set of derived parameters. These additional parameters were devised to enable users to formulate earthquake rate forecasts based on a seismic-moment balancing approach. EFSM20 considers two main categories of seismogenic faults: crustal faults and subduction systems, which include the subduction interface and intraslab faults. The compiled dataset covers an area from the Mid-Atlantic Ridge to the Caucasus and from northern Africa to Iceland. It includes 1248 crustal faults spanning a total length of ∼95 100 km and four subduction systems, namely the Gibraltar, Calabrian, Hellenic, and Cyprus arcs, for a total length of ∼2120 km. The model focuses on an area encompassing a buffer of 300 km around all European countries (except for Overseas Countries and Territories) and a maximum of 300 km depth for the subducting slabs. All the parameters required to develop a seismic source model for earthquake hazard analysis were determined for crustal faults and subduction systems. A statistical distribution of relevant seismotectonic parameters, such as faulting mechanisms, slip rates, moment rates, and prospective maximum magnitudes, is presented and discussed to address unsettled points in view of future updates and improvements. The dataset, identified by the DOI https://doi.org/10.13127/efsm20 (Basili et al., 2022), is distributed as machine-readable files using open standards (Open Geospatial Consortium).