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Photonic and Non-Inertial Seismology

Date: 4/24/2019

Time: 8:30 AM to 9:45 AM

Room: Grand Crescent

Emerging photonic and non-inertial seismic measurements of ground motion that use laser interferometery or a networked array of stationary receivers (e.g., GNSS) instead of a classical “mass-on-a-spring” are expanding our capacity to observe the structure and dynamics of Earth systems. These photonic and non-inertial tools include fiber-optic and distributed fiber-optic strain sensors (e.g., interferometers, Bragg grating methods, DAS) and ring-laser rotational sensors. There are key theoretical and practical differences (advantages and disadvantages) between inertial and non-inertial sensors, which are the present focus of many seismological and computational science research groups worldwide. These include measurement of strain and rotation, perhaps in addition to particle velocity, and the ability to record terabytes of Large-N seismic data with meter-scale sensor spacing. Also, seismogeodetic techniques such as GNSS precise point positioning increases the dynamic range and accuracy of (particularly large) ground displacements and strain. Because non-inertial data often contain information on displacement gradients of a seismic wavefield (i.e., strain), there is a need to develop a fundamental theoretical framework to cope with this new data type. Moreover, the diverse advantages of non-inertial seismology make way for new data analysis methods, or the adaptation of existing methods to this new data type, with the potential to make novel observations of the planet. This session aims to crosscut the emerging space of photonic and other non-inertial seismological methods with contributions on sensor design, technical instrumentation aspects and current roadblocks, inertial/non-inertial comparisons, case studies involving theoretical and real datasets and applications ranging from basic science to engineering/monitoring.

Conveners

Nathaniel J. Lindsey, University of California, Berkeley (natelindsey@berkeley.edu)
Patrick Paitz, ETH Zurich (patrick.paitz@erdw.ethz.ch)
Paul Bodin, University of Washington (bodin@uw.edu)
Jamie Steidl, University of California, Santa Barbara (steidl@eri.ucsb.edu)
Eileen Martin, Virginia Tech (eileenrmartin@vt.edu)
Zefeng Li, Caltech (zefengli@gps.caltech.edu)

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