Institut für Erd- und Umweltwissenschaften
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Earthquake localization is both a necessity within the field of seismology, and a prerequisite for further analysis such as source studies and hazard assessment. Traditional localization methods often rely on manually picked phases. We present an alternative approach using deep learning that once trained can predict hypocenter locations efficiently. In seismology, neural networks have typically been trained with either single-station records or based on features that have been extracted previously from the waveforms. We use three-component full-waveform records of multiple stations directly. This means no information is lost during preprocessing and preparation of the data does not require expert knowledge. The first convolutional layer of our deep convolutional neural network (CNN) becomes sensitive to features that characterize the waveforms it is trained on. We show that this layer can therefore additionally be used as an event detector. As a test case, we trained our CNN using more than 2000 earthquake swarm events from West Bohemia, recorded by nine local three-component stations. The CNN successfully located 908 validation events with standard deviations of 56.4 m in east-west, 123.8 m in north-south, and 136.3 m in vertical direction compared to a double-difference relocated reference catalog. The detector is sensitive to events with magnitudes down to M-L = -0.8 with 3.5% false positive detections.
In order to understand present day earthquake kinematics at the Indian plate boundary, we analyse seismic broadband data recorded between 2007 and 2015 by the regional network in the Garhwal-Kumaun region, northwest Himalaya. We first estimate a local 1-D velocity model for the computation of reliable Green's functions, based on 2837 P-wave and 2680 S-wave arrivals from 251 well located earthquakes. The resulting 1-D crustal structure yields a 4-layer velocity model down to the depths of 20 km. A fifth homogeneous layer extends down to 46 km, constraining the Moho using travel-time distance curve method. We then employ a multistep moment tensor (MT) inversion algorithm to infer seismic moment tensors of 11 moderate earthquakes with Mw magnitude in the range 4.0–5.0. The method provides a fast MT inversion for future monitoring of local seismicity, since Green's functions database has been prepared. To further support the moment tensor solutions, we additionally model P phase beams at seismic arrays at teleseismic distances. The MT inversion result reveals the presence of dominant thrust fault kinematics persisting along the Himalayan belt. Shallow low and high angle thrust faulting is the dominating mechanism in the Garhwal-Kumaun Himalaya. The centroid depths for these moderate earthquakes are shallow between 1 and 12 km. The beam modeling result confirm hypocentral depth estimates between 1 and 7 km. The updated seismicity, constrained source mechanism and depth results indicate typical setting of duplexes above the mid crustal ramp where slip is confirmed along out-of-sequence thrusting. The involvement of Tons thrust sheet in out-of-sequence thrusting indicate Tons thrust to be the principal active thrust at shallow depth in the Himalayan region. Our results thus support the critical taper wedge theory, where we infer the microseismicity cluster as a result of intense activity within the Lesser Himalayan Duplex (LHD) system.
The 2014 April 1, M-w 8.1 Iquique (Chile) earthquake struck in the Northern Chile seismic gap. With a rupture length of less than 200 km, it left unbroken large segments of the former gap. Early studies were able to model the main rupture features but results are ambiguous with respect to the role of aseismic slip and left open questions on the remaining hazard at the Northern Chile gap. A striking observation of the 2014 earthquake has been its extensive preparation phase, with more than 1300 events with magnitude above M-L 3, occurring during the 15 months preceding the main shock. Increasing seismicity rates and observed peak magnitudes accompanied the last three weeks before the main shock. Thanks to the large data sets of regional recordings, we assess the precursor activity, compare foreshocks and aftershocks and model rupture preparation and rupture effects. To tackle inversion challenges for moderate events with an asymmetric network geometry, we use full waveforms techniques to locate events, map the seismicity rate and derive source parameters, obtaining moment tensors for more than 300 events (magnitudes M-w 4.0-8.1) in the period 2013 January 1-2014 April 30. This unique data set of fore- and aftershocks is investigated to distinguish rupture process models and models of strain and stress rotation during an earthquake. Results indicate that the spatial distributions of foreshocks delineated the shallower part of the rupture areas of the main shock and its largest aftershock, well matching the spatial extension of the aftershocks cloud. Most moment tensors correspond to almost pure double couple thrust mechanisms, consistent with the slab orientation. Whereas no significant differences are observed among thrust mechanisms in different areas, nor among thrust foreshocks and aftershocks, the early aftershock sequence is characterized by the presence of normal fault mechanisms, striking parallel to the trench but dipping westward. These events likely occurred in the shallow wedge structure close to the slab interface and are consequence of the increased extensional stress in this region after the largest events. The overall stress inversion result suggests a minor stress rotation after the main shock, but a significant release of the deviatoric stress. The temporal change in the distribution of focal mechanisms can also be explained in terms of the spatial heterogeneity of the stress field: under such interpretation, the potential of a large megathrust earthquake breaking a larger segment offshore Northern Chile remains high.