Thread: SSA 2020 Session: Innovative Seismo-Acoustic Applications to Forensics and Novel Monitoring Problems

Started: 2020-01-06 21:37:38
Last activity: 2020-01-06 21:37:38
Topics: SSA Meetings
Dear Colleagues,

We would like to draw your attention to the SSA 2020 session “Innovative Seismo-Acoustic Applications to Forensics and Novel Monitoring Problems“. The abstract deadline is 14 January 2020 at 5 PM Pacific. You can submit the abstract with this link (

Session description:

Seismic and acoustic sensors are capable of recording ground motion and acoustic waves originated from many phenomena and activities. Besides traditional monitoring of natural environmental phenomena and military activities, seismo-acoustic measurements can also be used to detect, identify, locate, characterize, and monitor animal, domestic, and industrial processes that generate recordable acoustic, infrasonic, and/or seismic waves. Both established and more innovative data analyses can extract useful information from these wavefields. As our homes, factories, and communities get smarter, more data are needed, if not required, for safe operation. The information extracted from seismo-acoustic measurements of both persistent and transient activity will improve our state-of-health assessments of these environments. For example, seismo-acoustic signals related to machinery operations can be used to monitor status and specifics of the machinery independently.

We welcome submissions on collection and analysis of seismo-acoustic data and techniques including but not limit to (1) seismo-acoustic monitoring of animal, domestic, and industry activities; (2) acoustic and seismic analyses of chemical, ammunition or vapor explosions; (3) multi-signature fusion of seismo-acoustic data with other geophysical signatures; (4) methods to quantify uncertainties of parameter estimates that are derived from observing surficial, transient sources in noisy and cluttered signal environments; (5) special geophysical considerations of human-made environments that can bias source parameter estimates; (6) leveraging unconventional data streams for association and source location that include social media posts; and (7) machine learning applications to acoustic and seismic signals.

Hope to see you in Albuquerque, NM

Chengping Chai, Oak Ridge National Laboratory (chaic<at><chaic<at>>)
Joshua D. Carmichael, Los Alamos National Laboratory (joshuac<at><joshuac<at>>)
Monica Maceira, Oak Ridge National Laboratory (maceiram<at><maceiram<at>>)
Omar Marcillo, Los Alamos National Laboratory (omarcillo<at><omarcillo<at>>)

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