Nima Nooshiri
Dear colleagues,
We would like to draw your attention to the following session at the EGU 2020.
__Abstract deadline has now been EXTENDED until 16 January, 9:00 CET.__
SM1.2 - New seismic data analysis methods for automatic characterization of seismicity
Session link:
https://meetingorganizer.copernicus.org/EGU2020/session/35390
With a solicited presentation by Dr. Sebastian Heimann (GFZ Helmholtz Center, Potsdam, Germany) on probabilistic source optimization and joint inversion of seismic and geodetic data sets.
We cordially invite you to submit your abstracts for oral or poster presentations at this session.
Please inform your colleagues and research groups to consider our session to
present their work. We are looking forward to receiving your contributions.
With kind regards,
The session conveners,
Nima Nooshiri, Natalia Poiata, Francesco Grigoli, Simone Cesca, Federica Lanza
Session description:
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 led to a dramatic increase in the volume of available seismic data and pointed out the limits of the current standard routine seismic analysis, often performed manually by seismologists. Exploiting this 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. Moreover, machine learning techniques, which are dedicated methods 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 data sets characterized by large numbers of weak events with low signal-to-noise ratio, such as those collected in induced seismicity, seismic swarms and volcanic monitoring operations. This session aims to bring to light new methods that can be applied to large data sets, either retro-actively or in (near) real-time, to characterize seismicity (i.e., perform detection, location, magnitude and source mechanisms estimation) at different scales and in different environments. We thus encourage contributions that demonstrate how the proposed methods help improve our understanding of earthquake and/or volcanic processes.
We would like to draw your attention to the following session at the EGU 2020.
__Abstract deadline has now been EXTENDED until 16 January, 9:00 CET.__
SM1.2 - New seismic data analysis methods for automatic characterization of seismicity
Session link:
https://meetingorganizer.copernicus.org/EGU2020/session/35390
With a solicited presentation by Dr. Sebastian Heimann (GFZ Helmholtz Center, Potsdam, Germany) on probabilistic source optimization and joint inversion of seismic and geodetic data sets.
We cordially invite you to submit your abstracts for oral or poster presentations at this session.
Please inform your colleagues and research groups to consider our session to
present their work. We are looking forward to receiving your contributions.
With kind regards,
The session conveners,
Nima Nooshiri, Natalia Poiata, Francesco Grigoli, Simone Cesca, Federica Lanza
Session description:
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 led to a dramatic increase in the volume of available seismic data and pointed out the limits of the current standard routine seismic analysis, often performed manually by seismologists. Exploiting this 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. Moreover, machine learning techniques, which are dedicated methods 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 data sets characterized by large numbers of weak events with low signal-to-noise ratio, such as those collected in induced seismicity, seismic swarms and volcanic monitoring operations. This session aims to bring to light new methods that can be applied to large data sets, either retro-actively or in (near) real-time, to characterize seismicity (i.e., perform detection, location, magnitude and source mechanisms estimation) at different scales and in different environments. We thus encourage contributions that demonstrate how the proposed methods help improve our understanding of earthquake and/or volcanic processes.
