@article{ZaliOhrnbergerScherbaumetal.2021,
  author    = {Zali, Zahra and Ohrnberger, Matthias and Scherbaum, Frank and Cotton, Fabrice and Eibl, Eva P. S.},
  title     = {Volcanic tremor extraction and earthquake detection using music information retrieval algorithms},
  series = {Seismological research letters},
  volume    = {92},
  journal   = {Seismological research letters},
  number    = {6},
  publisher = {Seismological Society of America},
  address   = {Boulder, Colo.},
  issn      = {0895-0695},
  doi       = {10.1785/0220210016},
  pages     = {3668 -- 3681},
  year      = {2021},
  abstract  = {Volcanic tremor signals are usually observed before or during volcanic eruptions and must be monitored to evaluate the volcanic activity. A challenge in studying seismic signals of volcanic origin is the coexistence of transient signal swarms and long-lasting volcanic tremor signals. Separating transient events from volcanic tremors can, therefore, contrib-ute to improving upon our understanding of the underlying physical processes. Exploiting the idea of harmonic-percussive separation in musical signal processing, we develop a method to extract the harmonic volcanic tremor signals and to detect tran-sient events from seismic recordings. Based on the similarity properties of spectrogram frames in the time-frequency domain, we decompose the signal into two separate spec-trograms representing repeating (harmonic) and nonrepeating (transient) patterns, which correspond to volcanic tremor signals and earthquake signals, respectively. We reconstruct the harmonic tremor signal in the time domain from the complex spectrogram of the repeating pattern by only considering the phase components for the frequency range in which the tremor amplitude spectrum is significantly contribut-ing to the energy of the signal. The reconstructed signal is, therefore, clean tremor signal without transient events. Furthermore, we derive a characteristic function suitable for the detection of tran-sient events (e.g., earthquakes) by integrating amplitudes of the nonrepeating spectro-gram over frequency at each time frame. Considering transient events like earthquakes, 78\% of the events are detected for signal-to-noise ratio = 0.1 in our semisynthetic tests. In addition, we compared the number of detected earthquakes using our method for one month of continuous data recorded during the Holuhraun 2014-2015 eruption in Iceland with the bulletin presented in Agustsdottir et al. (2019). Our single station event detection algorithm identified 84\% of the bulletin events. Moreover, we detected a total of 12,619 events, which is more than twice the number of the bulletin events.},
  language  = {en}
}
@article{EsfahaniVogelCottonetal.2021,
  author    = {Esfahani, Reza Dokht Dolatabadi and Vogel, Kristin and Cotton, Fabrice and Ohrnberger, Matthias and Scherbaum, Frank and Kriegerowski, Marius},
  title     = {Exploring the dimensionality of ground-motion data by applying autoencoder techniques},
  series = {Bulletin of the Seismological Society of America : BSSA},
  volume    = {111},
  journal   = {Bulletin of the Seismological Society of America : BSSA},
  number    = {3},
  publisher = {Seismological Society of America},
  address   = {El Cerito, Calif.},
  issn      = {0037-1106},
  doi       = {10.1785/0120200285},
  pages     = {1563 -- 1576},
  year      = {2021},
  abstract  = {In this article, we address the question of how observed ground-motion data can most effectively be modeled for engineering seismological purposes. Toward this goal, we use a data-driven method, based on a deep-learning autoencoder with a variable number of nodes in the bottleneck layer, to determine how many parameters are needed to reconstruct synthetic and observed ground-motion data in terms of their median values and scatter. The reconstruction error as a function of the number of nodes in the bottleneck is used as an indicator of the underlying dimensionality of ground-motion data, that is, the minimum number of predictor variables needed in a ground-motion model. Two synthetic and one observed datasets are studied to prove the performance of the proposed method. We find that mapping ground-motion data to a 2D manifold primarily captures magnitude and distance information and is suited for an approximate data reconstruction. The data reconstruction improves with an increasing number of bottleneck nodes of up to three and four, but it saturates if more nodes are added to the bottleneck.},
  language  = {en}
}