Session 4 | Processing, modelling and artificial intelligence for fibre optic sensing users

Session 4

Processing, modelling and artificial intelligence for fibre optic sensing users
Convener: Martina Allegra | Co-coveners: Corentin Caudron, Chris Bean

Fibre optic sensing supported by the recent improvements in optical and atom interferometry, has enabled accurate and high-coverage sensing of the full ground motion wave-field and environmental parameters. Even in inaccessible domains or poorly instrumented environments, such as urban and submarine areas, a variety of signals have been successfully detected, ranging from microseism to teleseismic earthquakes, including volcanic events. The high sensitivity of the instrument is reflected in an increased number of possible applications. In this regard, the seismic source and wave-field characterization in harsh environments, the ocean bottom, the correction of tilt effects, as well as seismic ambient noise interferometry are just a few examples (some non-exhaustive examples).

However, the peculiarities of the acquired data demand the customization of signal processing techniques. If on the one hand traditional algorithms in geosciences are tailored to handle either the high spatial or the temporal resolution, on the other hand, the combination of high spatio-temporal acquisition throughput has fostered the widespread adoption of recent breakthroughs in Big Data analysis and advanced data analytics engines.

The session aims to highlight the innovation in classical methods/procedures and the recent technological advances on fibre optic sensing data analysis in any field of geosciences: seismology, volcanology, glaciology, geodesy, geophysics, natural hazards, oceanography, urban environment, geothermal applications, laboratory studies, large-scale field tests, planetary exploration, gravitational wave detection, fundamental physics.

Contributions dealing with processing, analysis and modelling for fibre optic sensing users are equally solicited. The overarching objective is to gather ingenious approaches in the application of the state-of-the-art algorithms in the geophysical field as well as recent cutting-edge techniques, such as High Performance Computing and Artificial Intelligence processes, with particular emphasis on Machine Learning models.

Invited speaker: Martijn Van den Ende (Université Côte d'Azur, France)

Fibre optic sensing supported by the recent improvements in optical and atom interferometry, has enabled accurate and high-coverage sensing of the full ground motion wave-field and environmental parameters. Even in inaccessible domains or poorly instrumented environments, such as urban and submarine areas, a variety of signals have been successfully detected, ranging from microseism to teleseismic earthquakes, including volcanic events. The high sensitivity of the instrument is reflected in an increased number of possible applications. In this regard, the seismic source and wave-field characterization in harsh environments, the ocean bottom, the correction of tilt effects, as well as seismic ambient noise interferometry are just a few examples (some non-exhaustive examples).

However, the peculiarities of the acquired data demand the customization of signal processing techniques. If on the one hand traditional algorithms in geosciences are tailored to handle either the high spatial or the temporal resolution, on the other hand, the combination of high spatio-temporal acquisition throughput has fostered the widespread adoption of recent breakthroughs in Big Data analysis and advanced data analytics engines.

The session aims to highlight the innovation in classical methods/procedures and the recent technological advances on fibre optic sensing data analysis in any field of geosciences: seismology, volcanology, glaciology, geodesy, geophysics, natural hazards, oceanography, urban environment, geothermal applications, laboratory studies, large-scale field tests, planetary exploration, gravitational wave detection, fundamental physics.

Contributions dealing with processing, analysis and modelling for fibre optic sensing users are equally solicited. The overarching objective is to gather ingenious approaches in the application of the state-of-the-art algorithms in the geophysical field as well as recent cutting-edge techniques, such as High Performance Computing and Artificial Intelligence processes, with particular emphasis on Machine Learning models.

Invited speaker: Martijn Van den Ende (Université Côte d'Azur, France)