固体地球科学(S)
セッション小記号地震学(SS)
セッションIDS-SS04
タイトル和文New methods for seismicity characterization
英文New methods for seismicity characterization
タイトル短縮名和文New methods for seismicity characterization
英文New seismic analysis methods
代表コンビーナ 氏名和文Francesco Grigoli
英文Francesco Grigoli
所属和文ETH-Zurich, Swiss Seismological Service
英文ETH-Zurich, Swiss Seismological Service
共同コンビーナ 1氏名和文加藤 愛太郎
英文Aitaro Kato
所属和文東京大学地震研究所
英文Earthquake Research Institute, the University of Tokyo
共同コンビーナ 2氏名和文青木 陽介
英文Yosuke Aoki
所属和文東京大学地震研究所
英文Earthquake Research Institute, University of Tokyo
共同コンビーナ 3氏名和文Claudio Satriano
英文Claudio Satriano
所属和文Institut de Physique du Globe de Paris
英文Institut de Physique du Globe de Paris
発表言語E
スコープ和文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.
英文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.
発表方法口頭および（または）ポスターセッション
招待講演Hilary Chang (Memorial University of Newfoundland)
Marius Kriegerowski (German Research Centre for Geosciences Potsdam, Germany)
Natalia Poiata (Institut de Physique du Globe de Paris)
時間講演番号タイトル発表者予稿原稿
口頭発表 5月26日 AM2
10:45 - 11:00SSS04-01MyShake: A global smartphone seismic network to characterize urban earthquakesRichard M Allen予稿
11:00 - 11:15SSS04-02Full waveform-based automatic monitoring of microseismic activity using high sampling rate records: application to Garpenberg mine (Sweden)Natalia Poiata予稿
11:15 - 11:30SSS04-03A deep convolutional neural network for localizing and detecting earthquake swarm activity based on full waveforms: Chances, challenges and questionsMarius Kriegerowski予稿
11:30 - 11:45SSS04-04Fast Location Parameters Determination of Seismic Events from Few Seconds of P Wave Recorded at a Single Seismological Station Using Support Vector Machine Regression Luis Hernan Ochoa Gutierrez予稿
11:45 - 12:00SSS04-05しきい値なし波形相関による前震検出平野 史朗予稿
12:00 - 12:15SSS04-06Automated seismic event detection and localization: An application to long-period seismicity at Aso Volcano influenced by large earthquakeAndri Hendriyana予稿
講演番号タイトル発表者予稿原稿
ポスター発表 5月26日 PM2
SSS04-P01Constraint of focal mechanisms of induced seismicity by using misfit angles based on known in-situ stress椋平 祐輔予稿
SSS04-P02Kinematic slip imaging of the Mw 3.3 earthquake in the St. Gallen 2013 geothermal reservoir, Switzerland, using an isochrone back projection approachClaudio Satriano予稿
SSS04-P03Monitoring induced seismicity with a single seismic station by combining coda wave interferometry with distance geometry solversFrancesco Grigoli予稿
SSS04-P04Automatic Earthquake Locating by Stacking Characteristic Functions in a Source Scanning MethodHilary Chang予稿
SSS04-P05Earthquake nowcasting: further development and application to Japan楠城 一嘉予稿