Abstract. The 24 January 2020 Mw 6.77 Elazığ-Sivrice earthquake (Turkey), responsible for 42 casualties and ~ 1600 injured people, is the largest earthquake affecting the East Anatolian Fault (EAF) since 1971. The earthquake partially ruptured a seismic gap. The mainshock was preceded by two foreshocks with Mw ≥ 4.9 and small seismicity clusters occurring in the previous months close to the nucleation point of the main rupture. The significant aftershock sequence comprises twelve earthquakes with Mw ≥ 4.5 within 60 days. We jointly model quasi co-seismic static surface displacements from Interferometric Synthetic Aperture Radar (InSAR) and high-frequency co-seismic data from seismological networks at local, regional and teleseismic distances to retrieve source parameters of the mainshock. We reconstruct the rupture process using a Bayesian bootstrap based probabilistic joint inversion scheme to obtain source parameters and their uncertainties. Full moment tensor for 18 fore-/after-shocks with Mw ≥ 4.3 are obtained based on the modeling of regional broadband data. The posterior mean model for the 2020 Elazığ-Sivrice mainshock shows that the earthquake, with a magnitude Mw 6.77, ruptured at shallow depth (5 ± 2 km) with a left-lateral strike-slip focal mechanism, with a dip angle of 74° ± 2° and a causative fault plane strike of 242° ± 1°, which is compatible with the orientation of the EAF at the centroid location. The rupture nucleated in the vicinity of small foreshock clusters and slowly propagated towards WSW, with a rupture velocity of ~ 2100 ± 130 m s−1 and ~ 27 s rupture duration. The main rupture area, with a length of ~ 26 ± 5 km, only covered 70 % of the former seismic gap, leaving a smaller, unbroken segment of ~ 30 km length to the SE with positive stress change. The subsequent aftershock sequence extended over a broader region of ~ 70 km in length, spreading to both sides of the mainshock rupture patch into the regions experiencing a stress increase according to our Coulomb stress modeling. Our results support the hypothesis of a shallow locking depth of the Anatolian micro-plate, which has a possible implication to the seismic bursts along the EAF and alternating seismic activity on the North Anatolian and the East Anatolian faults.

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Summary On 12 November 2017, an earthquake with a moment magnitude of 7.3 struck the west of Iran near the Iraq border. This event was followed about 9 and 12 months later by two large aftershocks of magnitude 5.9 and 6.3, which together triggered intensive seismic activity known as the 2017-2019 Kermanshah sequence. In this study, we analyze this sequence regarding the potential to forecast the spatial aftershock distribution based on information about the mainshock and its largest aftershocks. Recent studies showed that classical Coulomb failure stress (CFS) maps are outperformed by alternative scalar stress quantities, as well as a distance-slip probabilistic model (R) and deep neural networks (DNN). In particular, the R-model performed best. However, these test results were based on the receiver operating characteristic (ROC) metric, which is not well suited for imbalanced data sets such as aftershock distributions. Furthermore, the previous analyses also ignored the potential impact of large secondary earthquakes. For the complex Kermanshah sequence, we applied the same forecast models but used the more appropriate MCC-F1 metric for testing. Similar to previous studies, we also observe that the receiver independent stress scalars yield better forecasts than the classical CFS values relying on the specification of receiver mechanisms. However, detailed analysis based on the MCC-F1 metric revealed that the performance depends on the grid size, magnitude cutoff, and test period. Increasing the magnitude cutoff and decreasing the grid size and period reduce the performance of all methods. Finally, we found that the performance of the best methods improves when the source information of large aftershocks is additionally considered, with stress-based models outperforming the R model. Our results highlight the importance of accounting for secondary stress changes in improving earthquake forecasts.