On February 6, 2023, an Mw7.8 earthquake hit the East Anatolian Fault (EAF) and Narlı Fault (NF), followed by an Mw7.5 event on the Sürgü Fault. We combine multiple seismic datasets, global navigation satellite system recordings, and radar satellite images with finite fault inversion and slowness enhanced back-projection to study the rupture kinematics. Our analysis reveals that the rupture originated on the NF, propagating 120 km northeast at 3.05 km/s and 200 km southwest at 3.11 km/s after reaching the EAF junction, exhibiting overall subshear speeds. Further Mach wave analysis confirms the subshear rupture, matching the prediction using close-Rayleigh speeds. The unexpectedly-large slip on some EAF segments suggests a supercycle lasting ≥900 years. The EAF geometry is similar to the San Andreas-San Jacinto Fault system, while the latter has higher slip rates but without large earthquakes on its southern segments since 1857, carrying the potential of an M8 earthquake.

To reveal the connections between the 2024 moment magnitude ( M _w ) 7.5 Noto earthquake in Japan and the seismicity swarms that preceded it, we investigated its rupture process through near-source waveform analysis and source imaging techniques, combining seismic and geodetic datasets. We found notable complexity in the initial rupture stages. A strong fault asperity, which remained unbroken in preceding seismic swarms, slowed down the rupture. Then, a second rupture initiated at the opposite edge of the asperity, and the asperity succumbed to double-pincer rupture fronts. The failure of this high-stress drop asperity drove the earthquake into a large-scale event. Our observations help unravel the crucial role of fault asperities in controlling swarm migration and rupture propagation and underscore the need for detailed seismological and interdisciplinary studies to assess seismic risk in swarm-prone regions.

Time series analysis of synthetic aperture radar (SAR) and interferometric SAR generally starts with coregistration for the precise alignment of the stack of images. Here, we introduce a model-adjusted geometrical image coregistration (MAGIC) algorithm for stack coregistration. This algorithm corrects for atmospheric propagation delays and known surface motions using existing models and ensures simplicity and computational efficiency in the data processing systems. We validate this approach by evaluating the impact of different geolocation errors on stacks of the C-band Sentinel-1 and L-band ALOS-2 data, with a focus on the ionosphere. Our results show that the impact of the ionosphere dominates Sentinel-1 ascending (dusk-side) orbit and ALOS-2 data. After correcting for ionosphere using the JPL high-resolution global ionospheric maps, with topside total electron content (TEC) estimated from GPS receivers onboard the Sentinel-1 platforms, solid Earth tides, and troposphere, the mis-registration RMSE reduces by over a factor of four from 0.20 to 0.05 m for Sentinel-1 and from 2.66 to 0.56 m for ALOS-2. The results demonstrate that for Sentinel-1, the MAGIC approach is accurate enough in the range direction for most applications, including interferometry; while for the L-band SAR, it can be potentially accurate enough if topside TEC is available. Based on our current understanding of different error sources, we evaluate the expected range geolocation error budget for the upcoming NISAR mission with an upper bound of the relative geolocation error of 1.3 and 0.2 m for its L- and S-band SAR, respectively.