A single Rayleigh mode may exist with multiple values of phase-velocity at one frequency

Other than commonly assumed in seismology, the phase velocity of Rayleigh waves is not necessarily a single-valued function of frequency. In fact, a single Rayleigh mode can exist with three different values of phase velocity at one frequency. We demonstrate this for the first higher mode on a realistic shallow seismic structure of a homogeneous layer of unconsolidated sediments on top of a half-space of solid rock (LOH). In the case of LOH a significant contrast to the half-space is required to produce the phenomenon. In a simpler structure of a homogeneous layer with fixed (rigid) bottom (LFB) the phenomenon exists for values of Poisson’s ratio between 0.19 and 0.5 and is most pronounced for P-wave velocity being three times S-wave velocity (Poisson’s ratio of 0.4375). A pavement-like structure (PAV) of two layers on top of a half-space produces the multivaluedness for the fundamental mode. Programs for the computation of synthetic dispersion curves are prone to trouble in such cases. Many of them use mode-follower algorithms which loose track of the dispersion curve and miss the multivalued section. We show results for well established programs. Their inability to properly handle these cases might be one reason why the phenomenon of multivaluedness went unnoticed in seismological Rayleigh wave research for so long. For the very same reason methods of dispersion analysis must fail if they imply wave number $k_l(\omega)$ for the $l$-th Rayleigh mode to be a single-valued function of frequency $\omega$. This applies in particular to deconvolution methods like phase-matched filters. We demonstrate that a slant-stack analysis fails in the multivalued section, while a Fourier–Bessel transformation captures the complete Rayleigh-wave signal. Waves of finite bandwidth in the multivalued section propagate with positive group-velocity and negative phase-velocity. Their eigenfunctions appear conventional and contain no conspicuous feature

Pattern of cryospheric seismic events observed at Ekström ice shelf, Antarctica

Mobility of glaciers such as rapid retreat or disintegration of large ice volumes produces a large variety of different seismic signals. Thus, evaluating cryospheric seismic events (e.g. changes of their occurrence in space and time)allows to monitor glacier dynamics. We analyze a one year data span recorded at the Neumayer seismic network in Antarctica. Events are automatically recognized using hidden Markov models. In this study we focused on a specifc event type occurring close to the grounding line of the Ekström ice shelf. Observed waveform characteristics are consistent with an initial fracturing followed by the resonance of a water filled cavity resulting in a so-called hybrid event. The number of events detected strongly correlates with dominant tide periods. We assume the cracking to be driven by existing glacier stresses through bending. Voids are then filled by sea water, exciting the observed resonance. In agreement with this model, events occur almost exclusively during rising tides where cavities are opened at the bottom of the glacier, i.e. at the sea/ice interface

Seismic Monitoring of Permafrost in Svalbard, Arctic Norway

We analyze data from passive and active seismic experiments conducted in the Adventdalen valley of Svalbard in the Norwegian Arctic. Our objective is to characterize the ambient wavefield of the region and to investigate permafrost dynamics through estimates of seismic velocity variations. We are motivated by a need for early geophysical detection of potentially hazardous changes to permafrost stability. We draw upon several data sources to constrain various aspects of seismic wave propagation in Adventdalen. We use f-k analysis of five years of continuous data from the Spitsbergen seismic array (SPITS) to demonstrate that ambient seismic noise on Svalbard consists of continuously present body waves and intermittent surface waves appearing at regular intervals. A change in wavefield direction accompanies the sudden onset of surface waves when the average temperature rises above the freezing point, suggesting a cryogenic origin. This hypothesis is supported further by our analysis of records from a temporary broadband network, which indicates that the background wavefield is dominated by icequakes. Synthetic Green's functions calculated from a 3D velocity model match well with empirical Green's functions constructed from the recorded ambient seismic noise. We use a shallow shear-wave velocity model, obtained from active seismic measurements, to estimate the maximum depth of Rayleigh wave sensitivity to changes in shear velocity to be in the 50-100 m range. We extract seasonal variations in seismic velocities from ambient noise cross-correlation functions computed over three years of SPITS data. We attribute relative velocity variations to changes in the ice content of the shallow (2-4 m depth) permafrost, which is sensitive to seasonal temperature changes. A linear decreasing trend in seismic velocity is observed over the years, most likely due to permafrost warming.Peer reviewe

Guidelines for the good practice of surface wave analysis: a product of the InterPACIFIC project

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 VS,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.Published2367-24205T. Sismologia, geofisica e geologia per l'ingegneria sismicaJCR Journa

Automatic detection of wet-snow avalanche seismic signals

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.ISSN:0921-030XISSN:1573-084

Direct Inversion of Spatial Autocorrelation Curves with the Neighborhood Algorithm

International audienceAmbient vibration techniques are promising methods for assessing the subsurface structure, in particular the shear-wave velocity profile (Vs). They are based on the dispersion property of surface waves in layered media. Therefore, the penetration depth is intrinsically linked to the energy content of the sources. For ambient vibrations, the spectral content extends in general to lower frequency when compared to classical artificial sources. Among available methods for processing recorded signals, we focus here on the spatial autocorrelation method. For stationary wavefields, the spatial autocorrelation is mathematically related to the frequency-dependent wave velocity c({omega}). This allows the determination of the dispersion curve of traveling surface waves, which, in turn, is linked to the Vs profile. Here, we propose a direct inversion scheme for the observed autocorrelation curves to retrieve, in a single step, the Vs profile. The powerful neighborhood algorithm is used to efficiently search for all solutions in an n-dimensional parameter space. This approach has the advantage of taking into account the existing uncertainty over the measured curves, thus generating all Vs profiles that fit the data within their experimental errors. A preprocessing tool is also developed to estimate the validity of the autocorrelation curves and to reject parts of them if necessary before starting the inversion itself. We present two synthetic cases to test the potential of the method: one with ideal autocorrelation curves and another with autocorrelation curves computed from simulated ambient vibrations. The latter case is more realistic and makes it possible to figure out the problems that may be encountered in real experiments. The Vs profiles are correctly retrieved up to the depth of the first major velocity contrast unless low-velocity zones are accepted. We demonstrate that accepting low-velocity zones in the parameterization has a dramatic influence on the result of the inversion, with a considerable increase in the nonuniqueness of the problem. Finally, a real data set is processed with the same method

Classifying seismic waveforms from scratch: A case study in the alpine environment

ISSN:0956-540XISSN:1365-246