New Technologies and Data Analysis Methods for Seismic Monitoring
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".
Deadline for manuscript submissions: closed (30 July 2021) | Viewed by 8094
Special Issue Editors
Interests: waveform-based seismological techniques; induced seismicity; microseismic monitoring; earthquake source characterization; real-time seismology
Interests: waveform-based seismological techniques; induced seismicity; microseismic monitoring; earthquake source characterization; volcano seismology
Special Issue Information
Dear Colleagues,
Since the last decade, seismology has been characterized by an exponential growth of data, mainly due to the increasing number of high-quality seismic networks being installed around the world; these numbers will probably continue to increase in the coming decades. As a consequence, seismological data are growing in size and variety at an exceptionally fast rate, and this data explosion is opening a new era for this field of research. Modern microseismic networks based on new sensor technologies (such as hyper-dense surface arrays composed by thousands of low-cost seismometers, ultra-sensitive MEMS-based and interferometric accelerometers that can be deployed in boreholes, and distributed acoustic sensing (DAS) based on fiber optics technology) allow detecting a massive number of tiny earthquakes, generating an extremely large dataset for analysis. The analysis of such a huge amount of data, hundreds of times larger than those available today, highlights the limits of standard routines for seismic analysis, which are generally performed in a manual or semi-automated fashion. Exploiting these new massive datasets is a challenge that can be overcome only by using new-generation, automated, and noise-robust data analysis methods. In this context, the advent of fast graphics processing units (GPUs) has significantly increased our computational capability and now made it possible to apply innovative processing workflows, enabling to move away from standard manual or semi-automated seismic processing toward much more consistent and fully automated data analysis schemes.
In recent years, waveform-based detection and location methods have grown in popularity, and their application has dramatically improved seismic monitoring capability. Moreover, machine learning approaches to data-intensive seismic analysis are showing promising results, opening new horizons for the development of innovative, fully automated, and noise-robust 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 Special Issue aims to highlight advances in the development of new monitoring technologies and data analysis methods that can be applied to large seismic datasets and used to characterize seismicity (i.e., perform detection, location, magnitude, and source mechanism estimation) at different scales and in different environments. We encourage methodological contributions as well as key applications, which demonstrate how these new technologies and/or methods help to improve our understanding of the physical processes governing earthquake and/or volcanic phenomena.
Dr. Francesco Grigoli
Dr. Simone Cesca
Dr. Claudio Satriano
Guest Editors
Manuscript Submission Information
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Keywords
- Earthquake detection and location
- Seismicity characterization
- Seismic sensors
- Microseismic monitoring
- Satellite sensors
- Induced seismicity
- Volcano seismicity
- Machine learning
- Synthetic aperture radar (SAR)
