Solid Earth Sciences (S) | ||
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Session Sub-category | Seismology (SS) | |
Session ID | S-SS06 | |
Title | New trends in data acquisition, analysis and interpretation of seismicity | |
Short Title | New trends in seismology | |
Main Convener | Name | Bogdan Enescu |
Affiliation | Department of Geophysics, Kyoto University | |
Co-Convener 1 | Name | Francesco Grigoli |
Affiliation | University of Pisa | |
Co-Convener 2 | Name | Yosuke Aoki |
Affiliation | Earthquake Research Institute, University of Tokyo | |
Co-Convener 3 | Name | Takahiko Uchide |
Affiliation | Research Institute of Earthquake and Volcano Geology, Geological Survey of Japan, National Institute of Advanced Industrial Science and Technology (AIST) | |
Session Language |
E |
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Scope |
In the last two decades, the number of high-quality seismic instruments installed worldwide has grown exponentially and likely will continue to grow in the coming decades, producing larger and larger datasets. This dramatic increase in the volume of available seismic data is partially due to the rising popularity of new technologies for seismic data acquisition based on fiber optics, characterized by an extremely high spatial and temporal sampling. Such systems are making seismological datasets grow in size and variety at an exceptionally fast rate, pushing the limit of current data analysis techniques. This data explosion, combined with new data analysis paradigms, including AI-based methods, is opening new research horizons in Seismology and related fields. Exploiting the massive amount of data is a challenge that can be overcome by adopting new approaches for seismic data analysis that can lead to enhanced seismic catalogs that can be used in conjunction with advanced statistical or physics-based methods to forecast seismicity or to correlate the seismic activity with other geophysical processes, including stress changes and migration of fluids in the crust or aseismic processes. This session aims to bring to light new methods for the analysis (either offline or in real-time) and quantitative interpretation of seismicity datasets collected across different scales and environments or with new seismic data acquisition technologies, such as fiber-optics-based sensors. Relevant topics to be presented include but are not limited to methods for seismicity characterization, statistical analysis of seismicity patterns in the space-time-magnitude domain, modeling and forecasting of seismicity, earthquake triggering and case studies. We thus encourage contributions that demonstrate how the proposed methods or the analysis of large datasets help to improve our understanding of earthquake and/or volcanic processes. |
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Presentation Format | Oral and Poster presentation | |
Invited Authors |
Reiju Norisugi (Kyoto University) Takahiro Hatano (Department of Earth and Space Science, Osaka University) |
Time | Presentation No | Title | Presenter |
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Oral Presentation May 30 PM1 | |||
13:45 - 14:00 | SSS06-01 | Machine Learning and Deep Learning Predicts Meter-Scale Laboratory Earthquakes | Reiju Norisugi |
14:00 - 14:15 | SSS06-02 | Noise Attenuation in Distributed Fiber-Optic Sensing Data Using a Spectral Subtraction-based Approach | Giulio Pascucci |
14:15 - 14:30 | SSS06-03 | Event location by waveform stacking using integrated fiber optic-seismometer networks | Emanuele Bozzi |
14:30 - 14:45 | SSS06-04 | Joint earthquake source inversion method using P-wave spectra and focal mechanism solutions | Yifang Cheng |
14:45 - 15:00 | SSS06-05 | Information Content of Earthquake Catalogs | John B Rundle |
15:00 - 15:15 | SSS06-06 | Information-theory-based quantification of earthquake predictability and the predictive capacity of the ETAS model | Jiancang Zhuang |
Oral Presentation May 30 PM2 | |||
15:31 - 15:46 | SSS06-07 | Inter-event time statistics for volcanic earthquakes | Takahiro Hatano |
15:46 - 16:01 | SSS06-08 | Shallow rupture processes accompanying uplift at Campi Flegrei, Italy, revealed by moment tensor inversion and waveform clustering | Giacomo Rapagnani |
16:01 - 16:16 | SSS06-09 | Unsupervised exploration of seismic activity at Mount Fuji, Japan | Adele Doucet |
16:16 - 16:31 | SSS06-10 | Multi-array experiment of seismic monitoring at Merapi volcano using low-cost seismometers at sub-optimal distances | Theodorus Permana |
16:31 - 16:46 | SSS06-11 | Exploring the eruption sequence of the Klyuchevskoy volcano group and Shiveluch volcano (Kamchatka) in 2022-2023 with the seismic background level (SBL) technique | Nataliya Galina |
16:46 - 17:01 | SSS06-12 | Detection and location of seismovolcanic signals at Mutnovsky and Gorely volcanoes using seismic network-based methods | Yaroslav Berezhnev |
Presentation No | Title | Presenter |
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Poster Presentation May 30 PM3 | ||
SSS06-P01 | Mutsu Bay Seismic Cluster along the Volcanic Front in November 2024 | Yasunori Sawaki |
SSS06-P02 | Deep fault geometries in the Hidaka collision zone in southern Hokkaido revealed by hierarchical clustering of hypocenter distributions. | Admore Mpuang |
SSS06-P03 | The determination of the seismic sequence characteristics and post-earthquake trend of the Ms6.4 earthquake in Yangbi, Yunnan,China on May 21, 2021 | Bateer Wu |
SSS06-P04 | Determination of focal mechanism of earthquakes in and around the Korean Peninsula using P-wave first-motion polarity analysis based on deep learning | Mikyung Choi |
SSS06-P05 | Integrating Advanced Phase Pickers and Graph Neural Networks for Seismic Phase Association in Türkiye | Nurcan Meral Ozel |
SSS06-P06 | Machine Learning-based Surface Wave Analysis for High-Resolution Seismic Imaging in North China | Lu Dan |