Nima Nooshiri
2022-01-05 09:31:19
Dear colleagues,
We would like to draw your attention to the following EGU22 session:
SM3.2 - New Seismic Data Analysis Methods and Tools for Automatic Characterization of Seismicity
https://meetingorganizer.copernicus.org/EGU22/session/42868
Abstract: 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, location, and source-parameter estimation methods have grown in popularity and their application have dramatically improved seismic monitoring capability. Moreover, machine learning and deep 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 on bringing to light new methods and tools and also optimizations of existing approaches that make use of High Performance Computing resources (CPU, GPU) and 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-mechanism 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.
If the COVID-19 situation allows, the presentations in this session will take place in a hybrid virtual PICO (vPICO) format:
- This session format allows for both in-person and remote participation.
- A vPICO consists of a 2-minute presentation by the author, which will be hosted on an online platform, followed by further discussion in-person and online.
The deadline for abstract submission is 12 January 2022 (13:00 CET).
We cordially invite you to submit your abstracts at this session and looking forward to receiving your contributions.
With kind regards,
The session conveners,
Nima, Natalia, Federica, Francesco, Simone
We would like to draw your attention to the following EGU22 session:
SM3.2 - New Seismic Data Analysis Methods and Tools for Automatic Characterization of Seismicity
https://meetingorganizer.copernicus.org/EGU22/session/42868
Abstract: 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, location, and source-parameter estimation methods have grown in popularity and their application have dramatically improved seismic monitoring capability. Moreover, machine learning and deep 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 on bringing to light new methods and tools and also optimizations of existing approaches that make use of High Performance Computing resources (CPU, GPU) and 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-mechanism 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.
If the COVID-19 situation allows, the presentations in this session will take place in a hybrid virtual PICO (vPICO) format:
- This session format allows for both in-person and remote participation.
- A vPICO consists of a 2-minute presentation by the author, which will be hosted on an online platform, followed by further discussion in-person and online.
The deadline for abstract submission is 12 January 2022 (13:00 CET).
We cordially invite you to submit your abstracts at this session and looking forward to receiving your contributions.
With kind regards,
The session conveners,
Nima, Natalia, Federica, Francesco, Simone
