@article{KriegerowskiPetersenVasyuraBathkeetal.2018,
  author    = {Kriegerowski, Marius and Petersen, Gesa Maria and Vasyura-Bathke, Hannes and Ohrnberger, Matthias},
  title     = {A Deep Convolutional Neural Network for Localization of Clustered Earthquakes Based on Multistation Full Waveforms},
  series = {Seismological research letters},
  volume    = {90},
  journal   = {Seismological research letters},
  number    = {2},
  publisher = {Seismological Society of America},
  address   = {Albany},
  issn      = {0895-0695},
  doi       = {10.1785/0220180320},
  pages     = {510 -- 516},
  year      = {2018},
  abstract  = {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.},
  language  = {en}
}
@article{HeimannVasyuraBathkeSudhausetal.2019,
  author    = {Heimann, Sebastian and Vasyura-Bathke, Hannes and Sudhaus, Henriette and Isken, Marius Paul and Kriegerowski, Marius and Steinberg, Andreas and Dahm, Torsten},
  title     = {A Python framework for efficient use of pre-computed Green's functions in seismological and other physical forward and inverse source problems},
  series = {Solid earth},
  volume    = {10},
  journal   = {Solid earth},
  number    = {6},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {1869-9510},
  doi       = {10.5194/se-10-1921-2019},
  pages     = {1921 -- 1935},
  year      = {2019},
  abstract  = {The computation of such synthetic GFs is computationally and operationally demanding. As a consequence, the onthe-fly recalculation of synthetic GFs in each iteration of an optimisation is time-consuming and impractical. Therefore, the pre-calculation and efficient storage of synthetic GFs on a dense grid of source to receiver combinations enables the efficient lookup and utilisation of GFs in time-critical scenarios. We present a Python-based framework and toolkit - Pyrocko-GF - that enables the pre-calculation of synthetic GF stores, which are independent of their numerical calculation method and GF transfer function. The framework aids in the creation of such GF stores by interfacing a suite of established numerical forward modelling codes in seismology (computational back ends). So far, interfaces to back ends for layered Earth model cases have been provided; however, the architecture of Pyrocko-GF is designed to cover back ends for other geometries (e.g. full 3-D heterogeneous media) and other physical quantities (e.g. gravity, pressure, tilt). Therefore, Pyrocko-GF defines an extensible GF storage format suitable for a wide range of GF types, especially handling elasticity and wave propagation problems. The framework assists with visualisations, quality control, and the exchange of GF stores, which is supported through an online platform that provides many pre-calculated GF stores for local, regional, and global studies. The Pyrocko-GF toolkit comes with a well-documented application programming interface (API) for the Python programming language to efficiently facilitate forward modelling of geophysical processes, e.g. synthetic waveforms or static displacements for a wide range of source models.},
  language  = {en}
}