TY - JOUR A1 - Hammer, Conny A1 - Fäh, Donat A1 - Ohrnberger, Matthias T1 - Automatic detection of wet-snow avalanche seismic signals JF - Natural hazards : journal of the International Society for the Prevention and Mitigation of Natural Hazards N2 - 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. KW - Snow avalanche recognition KW - Automatic detection KW - Avalanche forecasting KW - Hidden Markov model Y1 - 2016 U6 - https://doi.org/10.1007/s11069-016-2707-0 SN - 0921-030X SN - 1573-0840 VL - 86 SP - 601 EP - 618 PB - Springer CY - New York ER - TY - JOUR A1 - Foti, Sebastiano A1 - Hollender, Fabrice A1 - Garofalo, Flora A1 - Albarello, Dario A1 - Asten, Michael A1 - Bard, Pierre-Yves A1 - Comina, Cesare A1 - Cornou, Cecile A1 - Cox, Brady A1 - Di Giulio, Giuseppe A1 - Forbriger, Thomas A1 - Hayashi, Koichi A1 - Lunedei, Enrico A1 - Martin, Antony A1 - Mercerat, Diego A1 - Ohrnberger, Matthias A1 - Poggi, Valerio A1 - Renalier, Florence A1 - Sicilia, Deborah A1 - Socco, Valentina T1 - Guidelines for the good practice of surface wave analysis BT - a product of the InterPACIFIC project JF - Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering N2 - 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. KW - Rayleigh waves KW - MASW KW - Ambient vibration analysis KW - Site characterization KW - Shear wave velocity KW - V-S,V-30 Y1 - 2017 U6 - https://doi.org/10.1007/s10518-017-0206-7 SN - 1570-761X SN - 1573-1456 VL - 16 IS - 6 SP - 2367 EP - 2420 PB - Springer CY - Dordrecht ER - TY - JOUR A1 - Knapmeyer-Endrun, Brigitte A1 - Golombek, Matthew P. A1 - Ohrnberger, Matthias T1 - Rayleigh Wave Ellipticity Modeling and Inversion for Shallow Structure at the Proposed InSight Landing Site in Elysium Planitia, Mars JF - Space science reviews N2 - 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. KW - Mars KW - Interior KW - Seismology KW - Regoliths Y1 - 2017 U6 - https://doi.org/10.1007/s11214-016-0300-1 SN - 0038-6308 SN - 1572-9672 VL - 211 SP - 339 EP - 382 PB - Springer CY - Dordrecht ER -