TY - JOUR A1 - Steinberg, Andreas A1 - Vasyura-Bathke, Hannes A1 - Gaebler, Peter Jost A1 - Ohrnberger, Matthias A1 - Ceranna, Lars T1 - Estimation of seismic moment tensors using variational inference machine learning JF - Journal of geophysical research : Solid earth N2 - We present an approach for rapidly estimating full moment tensors of earthquakes and their parameter uncertainties based on short time windows of recorded seismic waveform data by considering deep learning of Bayesian Neural Networks (BNNs). The individual neural networks are trained on synthetic seismic waveform data and corresponding known earthquake moment-tensor parameters. A monitoring volume has been predefined to form a three-dimensional grid of locations and to train a BNN for each grid point. Variational inference on several of these networks allows us to consider several sources of error and how they affect the estimated full moment-tensor parameters and their uncertainties. In particular, we demonstrate how estimated parameter distributions are affected by uncertainties in the earthquake centroid location in space and time as well as in the assumed Earth structure model. We apply our approach as a proof of concept on seismic waveform recordings of aftershocks of the Ridgecrest 2019 earthquake with moment magnitudes ranging from Mw 2.7 to Mw 5.5. Overall, good agreement has been achieved between inferred parameter ensembles and independently estimated parameters using classical methods. Our developed approach is fast and robust, and therefore, suitable for down-stream analyses that need rapid estimates of the source mechanism for a large number of earthquakes. KW - seismology KW - machine learning KW - earthquake source KW - moment tensor KW - full KW - waveform Y1 - 2021 U6 - https://doi.org/10.1029/2021JB022685 SN - 2169-9313 SN - 2169-9356 VL - 126 IS - 10 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Stich, Daniel A1 - Martin, Rosa A1 - Morales, Jose A1 - Lopez-Comino, Jose Angel A1 - Mancilla, Flor de Lis T1 - Slip partitioning in the 2016 Alboran Sea earthquake sequence (western Mediterranean) JF - Frontiers in Earth Science N2 - AM(W)= 5.1 earthquake on January 21st, 2016 marked the beginning of a significant seismic sequence in the southern Alboran Sea, culminating in aM(W)= 6.3 earthquake on January 25th, and continuing with further moderate magnitude earthquakes until March. We use data from 35 seismic broadband stations in Spain, Morocco and Portugal to relocate the seismicity, estimate seismic moment tensors, and isolate regional apparent source time functions for the main earthquake. Relocation and regional moment tensor inversion consistently yield very shallow depths for the majority of events. We obtain 50 moment tensors for the sequence, showing a mixture of strike-slip faulting for the foreshock and the main event and reverse faulting for the major aftershocks. The leading role of reverse focal mechanisms among the aftershocks may be explained by the geometry of the fault network. The mainshock nucleates at a bend along the left-lateral Al-Idrisi fault, introducing local transpression within the transtensional Alboran Basin. The shallow depths of the 2016 Alboran Sea earthquakes may favor slip-partitioning on the involved faults. Apparent source durations for the main event suggest a similar to 21 km long, asymmetric rupture that propagates primarily toward NE into the restraining fault segment, with fast rupture speed of similar to 3.0 km/s. Consistently, the inversion for laterally variable fault displacement situates the main slip in the restraining segment. The partitioning into strike-slip rupture and dip-slip aftershocks confirms a non-optimal orientation of this segment, and suggests that the 2016 event settled a slip deficit from previous ruptures that could not propagate into the stronger restraining segment. KW - slip partitioning KW - fault bend KW - moment tensor KW - source time function KW - shallow earthquakes Y1 - 2020 U6 - https://doi.org/10.3389/feart.2020.587356 SN - 2296-6463 VL - 8 PB - Frontiers Media CY - Lausanne ER -