New methods in engineering geophysics: distributed acoustic sensing and machine learning
Eileen Martin
Virginia Tech
- Date & Time
- Location
- Online-only seminar via Microsoft Teams
- Summary
Geotechnical engineers have noted that in many places there is significant variability in the near surface over much shorter spatial scales than what is measured by traditional techniques. Over the past decade new seismic sensing technologies such as distributed acoustic sensing have enabled continuous, high-density seismic acquisition over long distances. The ability to install cables quickly or to plug into existing telecommunications infrastructure have enabled engineering geophysics around infrastructure and in urban areas. Further lowering costs, these low-labor acquisitions have been analyzed with passive seismic methods for near-surface imaging and earthquake engineering. While data are now incredibly easy to acquire, processing these data has been a challenge both due to large data volumes (multi-terabyte per day) and due to challenging noise environments in urban areas. New algorithms including machine learning are increasingly required to analyze these data. These new techniques will be discussed in the context of several recent experiments with applications in earthquake engineering and near-surface geohazards.
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