@article{EiblMuellerWalteretal.2021,
  author    = {Eibl, Eva P. S. and M{\"u}ller, Daniel and Walter, Thomas R. and Allahbakhshi, Masoud and Jousset, Philippe and Hersir, Gylfi P{\´a}ll and Dahm, Torsten},
  title     = {Eruptive cycle and bubble trap of Strokkur Geyser, Iceland},
  series = {Journal of geophysical research : JGR. B: Solid earth},
  volume    = {126},
  journal   = {Journal of geophysical research : JGR. B: Solid earth},
  number    = {4},
  publisher = {Wiley},
  address   = {Hoboken, NJ},
  issn      = {2169-9313},
  doi       = {10.1029/2020JB020769},
  pages     = {20},
  year      = {2021},
  abstract  = {The eruption frequency of geysers can be studied easily on the surface. However, details of the internal structure including possible water and gas filled chambers feeding eruptions and the driving mechanisms often remain elusive. We used a multidisciplinary network of seismometers, video cameras, water pressure sensors and one tiltmeter to study the eruptive cycle, internal structure, and mechanisms driving the eruptive cycle of Strokkur geyser in June 2018. An eruptive cycle at Strokkur always consists of four phases: (1) Eruption, (2) post-eruptive conduit refilling, (3) gas filling of the bubble trap, and (4) regular bubble collapse at shallow depth in the conduit. For a typical single eruption 19 +/- 4 bubble collapses occur in Phase 3 and 8 +/- 2 collapses in Phase 4 at a mean spacing of 1.52 +/- 0.29 and 24.5 +/- 5.9 s, respectively. These collapses release latent heat to the fluid in the bubble trap (Phase 3) and later to the fluid in the conduit (Phase 4). The latter eventually reaches thermodynamic conditions for an eruption. Single to sextuple eruptions have similar spacings between bubble collapses and are likely fed from the same bubble trap at 23.7 +/- 4.4 m depth, 13-23 m west of the conduit. However, the duration of the eruption and recharging phase linearly increases likely due to a larger water, gas and heat loss from the system. Our tremor data provides documented evidence for a bubble trap beneath a pool geyser.},
  language  = {en}
}
@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{IzgiEiblDonneretal.2021,
  author    = {Izgi, Gizem and Eibl, Eva P. S. and Donner, Stefanie and Bernauer, Felix},
  title     = {Performance test of the rotational sensor blueSeis-3A in a huddle test in F{\"u}rstenfeldbruck},
  series = {Sensors},
  volume    = {21},
  journal   = {Sensors},
  number    = {9},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {1424-8220},
  doi       = {10.3390/s21093170},
  pages     = {20},
  year      = {2021},
  abstract  = {Rotational motions play a key role in measuring seismic wavefield properties. Using newly developed portable rotational instruments, it is now possible to directly measure rotational motions in a broad frequency range. Here, we investigated the instrumental self-noise and data quality in a huddle test in F{\"u}rstenfeldbruck, Germany, in August 2019. We compare the data from six rotational and three translational sensors. We studied the recorded signals using correlation, coherence analysis, and probabilistic power spectral densities. We sorted the coherent noise into five groups with respect to the similarities in frequency content and shape of the signals. These coherent noises were most likely caused by electrical devices, the dehumidifier system in the building, humans, and natural sources such as wind. We calculated self-noise levels through probabilistic power spectral densities and by applying the Sleeman method, a three-sensor method. Our results from both methods indicate that self-noise levels are stable between 0.5 and 40 Hz. Furthermore, we recorded the 29 August 2019 ML 3.4 Dettingen earthquake. The calculated source directions are found to be realistic for all sensors in comparison to the real back azimuth. We conclude that the five tested blueSeis-3A rotational sensors, when compared with respect to coherent noise, self-noise, and source direction, provide reliable and consistent results. Hence, field experiments with single rotational sensors can be undertaken.},
  language  = {en}
}