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

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

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

ISSN:0956-540XISSN:1365-246

SERA WP7/NA5 - Deliverable 7.4: Towards improvement of site condition indicators

This report summarizes the research undertaken by ETH in the framework of WP7/NA5 – Task 7.4 of SERA project (“Towards improvement of site characterization indicators”), in collaboration with partners AUTH, INGV, CNRS. We have addressed the broad topic of site condition indicators, or proxies, with a comprehensive work: i) firstly, we have reviewed the state of the art and tracked the present research trends in the use of proxies; ii) secondly, to test their applicability at a wide scale, we have compiled an extensive database of site condition parameters, covering more than 1000 instrumented sites in Switzerland and Japan, and paired it with a companion dataset of empirically-derived local amplification functions. In this phase of data collection, particular attention was dedicated to the harmonization of information derived from disparate sources and referring to different geological and geographical contexts; iii) in a third step, we have systematically assessed the sensitivity of site condition indicators towards local seismic amplification, ranking and collating their behaviour, also in different environments; iv) lastly, we have attempted to assess their potential for the prediction of local site response, resorting to a neural-network structure. The results we have obtained from the two latter stages offer an interesting insight on the varied correlations between local amplification and various typologies of site condition indicators. Furthermore, the findings gained by the neural-network analysis allow us to determine a ‘best’ set of site condition parameters appropriate to predict local site amplification

On the Relation between Empirical Amplification and Proxies Measured at Swiss and Japanese Stations: Systematic Regression Analysis and Neural Network Prediction of Amplification

We address the relation between local amplification and site‐condition indicators derived from in situ geophysical surveys for the estimation of the VS profile, and single‐station recordings processed with horizontal‐to‐vertical spectral ratio technique. Site‐condition indicators, or proxies (e.g., VS30), aim at “summarizing” the description of the local geophysical structure, with a focus on its relation to site amplification. The premise for our work was the compilation of two companion databases: one of soil condition proxies and the other of empirically derived Fourier amplification functions, for Swiss and Japanese stations. We investigated the connection between these two datasets, at first, with a systematic set of regressions correlating each proxy to amplification factors within the frequency band 0.5–20 Hz, second, with a neural network (NN) structure predicting site amplification from proxies. The regression analyses showed that, generally, site‐condition parameters (SCPs) bear a better correlation with amplification within 1.7–6.7 Hz; the “best” indicators are the frequency‐dependent quarter‐wavelength (QWL) velocity and, among scalar parameters, VS30, the bedrock depth, and f0. Collating Swiss and Japanese datasets, the trend of variation of amplification with respect to most proxies is similar. Finally, we evaluated the prediction performance of various combinations of SCPs, for local amplification, using a NN. To attain a database large enough to constrain the estimation of the network parameters, we merged Swiss and Japanese stations into a single training and validation dataset, motivated by the similarities observed in the regression analyses. The outcome we obtained from the NN is encouraging and consistent with the results of the regressions; SCPs with higher correlation to amplification provide a better forecast of the latter (particularly within 1.7–6.7 Hz). More complete input information, such as QWL parameters (velocity, impedance contrast), or extended ensembles of scalar proxies (particularly, including f0), offer a better estimation of local amplification.ISSN:0037-1106ISSN:1943-357

Automatic detection of avalanches combining array classification and localization

We used continuous data from a seismic monitoring system to automatically determine the avalanche activity at a remote field site above Davos, Switzerland. The approach is based on combining a machine learning algorithm with array processing techniques to provide an operational method capable of near real-time classification. First, we used a recently developed method based on hidden Markov models (HMMs) to automatically identify events in continuous seismic data using only a single training event. For the 2016–2017 winter period, this resulted in 117 events. Second, to eliminate falsely classified events such as airplanes and local earthquakes, we implemented an additional HMM-based classifier at a second array 14 km away. By cross-checking the results of both arrays, we reduced the number of classifications by about 50 %. In a third and final step we used multiple signal classification (MUSIC), an array processing technique, to determine the direction of the source. As snow avalanches recorded at our arrays typically generate signals with small changes in source direction, events with large changes were dismissed. From the 117 initially detected events during the 4-month period, our classification workflow removed 96 events. The majority of the remaining 21 events were on 9 and 10 March 2017, in line with visual avalanche observations in the Davos region. Our results suggest that the classification workflow presented could be used to identify major avalanche periods and highlight the importance of array processing techniques for the automatic classification of avalanches in seismic data