@article{SteinbergVasyuraBathkeGaebleretal.2021,
  author    = {Steinberg, Andreas and Vasyura-Bathke, Hannes and Gaebler, Peter Jost and Ohrnberger, Matthias and Ceranna, Lars},
  title     = {Estimation of seismic moment tensors using variational inference machine learning},
  series = {Journal of geophysical research : Solid earth},
  volume    = {126},
  journal   = {Journal of geophysical research : Solid earth},
  number    = {10},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {2169-9313},
  doi       = {10.1029/2021JB022685},
  pages     = {16},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}
@article{SocquetValdesJaraetal.2017,
  author    = {Socquet, Anne and Valdes, Jesus Pina and Jara, Jorge and Cotton, Fabrice Pierre and Walpersdorf, Andrea and Cotte, Nathalie and von Specht, Sebastian and Ortega-Culaciati, Francisco and Carrizo, Daniel and Norabuena, Edmundo},
  title     = {An 8month slow slip event triggers progressive nucleation of the 2014 Chile megathrust},
  series = {Geophysical research letters},
  volume    = {44},
  journal   = {Geophysical research letters},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {0094-8276},
  doi       = {10.1002/2017GL073023},
  pages     = {4046 -- 4053},
  year      = {2017},
  abstract  = {The mechanisms leading to large earthquakes are poorly understood and documented. Here we characterize the long-term precursory phase of the 1 April 2014 M(w)8.1 North Chile megathrust. We show that a group of coastal GPS stations accelerated westward 8months before the main shock, corresponding to a M(w)6.5 slow slip event on the subduction interface, 80\% of which was aseismic. Concurrent interface foreshocks underwent a diminution of their radiation at high frequency, as shown by the temporal evolution of Fourier spectra and residuals with respect to ground motions predicted by recent subduction models. Such ground motions change suggests that in response to the slow sliding of the subduction interface, seismic ruptures are progressively becoming smoother and/or slower. The gradual propagation of seismic ruptures beyond seismic asperities into surrounding metastable areas could explain these observations and might be the precursory mechanism eventually leading to the main shock.},
  language  = {en}
}