*Yusuke Mukuhira1, Makoto Naoi2, Michael C Fehler3, Hirokazu Moriya5, Takatoshi Ito1, Hiroshi Asanuma4, Markus O Häring6 (1.Institute of Fluid Science, Tohoku University, 2.Disaster Prevention Research Institute, Kyoto University, 3.Earth Resources Laboratory, MIT, 4.FREA, AIST, 5.School of Engineering, Tohoku University, 6.Häring GeoProject)
S (Solid Earth Sciences ) » S-SS Seismology
[S-SS04] New methods for seismicity characterization
Sun. May 26, 2019 3:30 PM - 5:00 PM Poster Hall (International Exhibition Hall8, Makuhari Messe)
convener:Francesco Grigoli(ETH-Zurich, Swiss Seismological Service), Aitaro Kato(Earthquake Research Institute, the University of Tokyo), Yosuke Aoki(Earthquake Research Institute, University of Tokyo), Claudio Satriano(Institut de Physique du Globe de Paris)
In the last two decades the number of high quality seismic instruments being installed around the world has grown exponentially and probably will continue to grow in the coming decades. This data explosion has shown the limits of the current standard routine seismic analysis, often performed manually by seismologists. Exploiting the massive amount of data is a challenge that can be overcome by using new generation, fully automated and noise robust seismic processing techniques. In the last years waveform-based detection and location methods have grown in popularity and their application have dramatically improved seismic monitoring capability. More recently, Machine Learning techniques, which are a perfect playground for data-intensive applications, are showing promising results in seismicity characterization applications opening new horizons for the development of innovative, fully automated and noise robust seismic analysis methods. Such techniques are particularly useful when working with datasets characterised by a massive number of weak events with low signal-to-noise ratio, such as those collected in induced seismicity and volcanic monitoring operations. This session aims to bring to light new methods that can be applied to large datasets, either retro-actively or in near-real time, to characterize seismicity (i.e. detection, location, magnitude and source mechanisms estimation) at different scales and in different environments. We thus encourage contributions that demonstrate how the proposed methods helps improve our understanding of earthquake and/or volcanic processes.
[SSS04-P04] Automatic Earthquake Locating by Stacking Characteristic Functions in a Source Scanning Method
*Hilary Chang1, Alison Malcolm1, Frédérick Massin2, Francesco Grigoli2 (1.Memorial University of Newfoundland, Earth Sciences, Canada, 2. ETH Zurich, Department of Earth Sciences, Zürich, Switzerland )