@article{HammerFaehOhrnberger2017,
  author    = {Hammer, Conny and F{\"a}h, Donat and Ohrnberger, Matthias},
  title     = {Automatic detection of wet-snow avalanche seismic signals},
  series = {Natural hazards : journal of the International Society for the Prevention and Mitigation of Natural Hazards},
  volume    = {86},
  journal   = {Natural hazards : journal of the International Society for the Prevention and Mitigation of Natural Hazards},
  publisher = {Springer},
  address   = {New York},
  issn      = {0921-030X},
  doi       = {10.1007/s11069-016-2707-0},
  pages     = {601 -- 618},
  year      = {2017},
  abstract  = {Avalanche activity is an important factor when estimating the regional avalanche danger. Moreover, a complete and detailed picture of avalanche activity is needed to understand the processes that lead to natural avalanche release. Currently, information on avalanche activity is mainly obtained through visual observations. However, this involves large uncertainties in the number and release times, influencing the subsequent analysis. Therefore, alternative methods for the remote detection of snow avalanches in particular in non-observed areas are highly desirable. In this study, we use the excited ground vibration to identify avalanches automatically. The specific seismic signature of avalanches facilitates the objective detection by a recently developed classification procedure. A probabilistic description of the signals, called hidden Markov models, allows the robust identification of corresponding signals in the continuous data stream. The procedure is based upon learning a general background model from continuous seismic data. Then, a single reference waveform is used to update an event-specific classifier. Thus, a minimum amount of training data is required by constructing such a classifier on the fly. In this study, we processed five days of continuous data recorded in the Swiss Alps during the avalanche winter 1999. With the restriction of testing large wet-snow avalanches only, the presented approach achieved very convincing results. We successfully detect avalanches over a large volume and distance range. Ninety-two percentage of all detections (43 out of 47) could be confirmed as avalanche events; only four false alarms are reported. We see a clear dependence of recognition capability on run-out distance and source-receiver distance of the observed events: Avalanches are detectable up to a source-receiver distance of eight times the avalanche length. Implications for analyzing a more comprehensive data set (smaller events and different flow regimes) are discussed in detail.},
  language  = {en}
}
@article{FotiHollenderGarofaloetal.2017,
  author    = {Foti, Sebastiano and Hollender, Fabrice and Garofalo, Flora and Albarello, Dario and Asten, Michael and Bard, Pierre-Yves and Comina, Cesare and Cornou, Cecile and Cox, Brady and Di Giulio, Giuseppe and Forbriger, Thomas and Hayashi, Koichi and Lunedei, Enrico and Martin, Antony and Mercerat, Diego and Ohrnberger, Matthias and Poggi, Valerio and Renalier, Florence and Sicilia, Deborah and Socco, Valentina},
  title     = {Guidelines for the good practice of surface wave analysis},
  series = {Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering},
  volume    = {16},
  journal   = {Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering},
  number    = {6},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {1570-761X},
  doi       = {10.1007/s10518-017-0206-7},
  pages     = {2367 -- 2420},
  year      = {2017},
  abstract  = {Surface wave methods gained in the past decades a primary role in many seismic projects. Specifically, they are often used to retrieve a 1D shear wave velocity model or to estimate the V-s,V-30 at a site. The complexity of the interpretation process and the variety of possible approaches to surface wave analysis make it very hard to set a fixed standard to assure quality and reliability of the results. The present guidelines provide practical information on the acquisition and analysis of surface wave data by giving some basic principles and specific suggestions related to the most common situations. They are primarily targeted to non-expert users approaching surface wave testing, but can be useful to specialists in the field as a general reference. The guidelines are based on the experience gained within the InterPACIFIC project and on the expertise of the participants in acquisition and analysis of surface wave data.},
  language  = {en}
}
@misc{KnapmeyerEndrunGolombekOhrnberger2017,
  author    = {Knapmeyer-Endrun, Brigitte and Golombek, Matthew P. and Ohrnberger, Matthias},
  title     = {Rayleigh Wave Ellipticity Modeling and Inversion for Shallow Structure at the Proposed InSight Landing Site in Elysium Planitia, Mars},
  series = {Space science reviews},
  volume    = {211},
  journal   = {Space science reviews},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {0038-6308},
  doi       = {10.1007/s11214-016-0300-1},
  pages     = {339 -- 382},
  year      = {2017},
  abstract  = {The SEIS (Seismic Experiment for Interior Structure) instrument onboard the InSight mission will be the first seismometer directly deployed on the surface of Mars. From studies on the Earth and the Moon, it is well known that site amplification in low-velocity sediments on top of more competent rocks has a strong influence on seismic signals, but can also be used to constrain the subsurface structure. Here we simulate ambient vibration wavefields in a model of the shallow sub-surface at the InSight landing site in Elysium Planitia and demonstrate how the high-frequency Rayleigh wave ellipticity can be extracted from these data and inverted for shallow structure. We find that, depending on model parameters, higher mode ellipticity information can be extracted from single-station data, which significantly reduces uncertainties in inversion. Though the data are most sensitive to properties of the upper-most layer and show a strong trade-off between layer depth and velocity, it is possible to estimate the velocity and thickness of the sub-regolith layer by using reasonable constraints on regolith properties. Model parameters are best constrained if either higher mode data can be used or additional constraints on regolith properties from seismic analysis of the hammer strokes of InSight's heat flow probe HP3 are available. In addition, the Rayleigh wave ellipticity can distinguish between models with a constant regolith velocity and models with a velocity increase in the regolith, information which is difficult to obtain otherwise.},
  language  = {en}
}