SM3.2

EDI
Convener: Nima NooshiriECSECS | Co-conveners: Natalia Poiata, Federica LanzaECSECS, Francesco GrigoliECSECS, Simone Cesca

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 on bringing to light new methods 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.