Michelle Guy
I architect, manage, and build highly specialized state-of-the-art scientific software systems. These systems are one of a kind and serve to bring the unique science of the GHSC into real-time operations and produce products for worldwide consumption by scientific communities, emergency responders, government agencies and the general public.
Funding
- Crowd-Sourced Earthquake Detections Integrated with Instrumental Seismic Processing; PI; April 2016; Community for Data Integration, USGS; http://bit.ly/2gxWsX0
- Characterization of Earthquake Damage and Effects Using Social-Media Data; Co-PI; April 2014; Community for Data Integration, USGS
Publications
- Guy, Michelle, Patton, John, Fee, J.M., Hearne, Mike, Martinez, E.M., Ketchum, D., Worden, C.B., Quitoriano, Vince, Hunter, E.J., Smoczyk, G.M., and Schwarz, Stan, 2015, National Earthquake Information Center systems overview and integration: U.S. Geological Survey Open-File Report 2015–1120, 25 p., http://dx.doi.org/10.3133/ofr20151120.
- Patton, J.M., Ketchum, D.C., and Guy, M.R., 2015, An overview of the National Earthquake Information Center acquisition software system, Edge/Continuous Waveform Buffer: U.S. Geological Survey Open-File Report 2015–1174, 10 p., http://dx.doi.org/10.3133/ofr20151174.
- Madison L. Langseth, Michelle Y. Chang, Jennifer Carlino, Daniella D. Birch, Joshua Bradley, R. Sky Bristol, Craig Conzelmann, Robert H. Diehl, Paul Earle, Laura E. Ellison, Anthony L. Everette, Pam Fuller, Janice M. Gordon, David L. Govoni, Michelle R. Guy, Heather S. Henkel, Vivian B. Hutchison, Tim Kern, Frances L. Lightsom, Joseph W. Long, Ryan Longhenry, Todd M. Preston, Stan Smith, Roland J. Viger, Katherine Wesenberg, Eric Wood. (July 2015). “Community for Data Integration 2014 Annual Report”. IPDS number IP-066330.
- Michelle Guy. Paul Earle, Scott Horvath, Jessica Turner, Douglas Bausch, Greg Smoczyk; Association of Computing Machinery (ACM) Significant Interest Group conference on Knowledge Discovery and Data Mining (SIGKDD) workshop on Learning about Emergencies from Social Media (LESI) August 2014: “Social Media Based Earthquake Detection and Characterization”; https://sites.google.com/site/kddlesi2014/program/papers;
- Earle, P.S., D.C. Bowden, and M. Guy (2011) Twitter earthquake detection: earthquake monitoring in a social world. Annals of Geophysics, 54(6), 708-715.
- Guy, M., P. Earle, C. Ostrum, K. Gruchalla and S. Horvath (2010) Integration and dissemination of citizen reported and seismically derived earthquake information via social network technologies. Advances in Intelligent Data Analysis IX Lecture Notes in Computer Science, 6065/2010, 42-53. &nb
Science and Products
Crowd-Sourced Earthquake Detections Integrated into Seismic Processing
The goal of this project is to improve the USGS National Earthquake Information Center’s (NEIC) earthquake detection capabilities through direct integration of crowd-sourced earthquake detections with traditional, instrument-based seismic processing. During the past 6 years, the NEIC has run a crowd-sourced system, called Tweet Earthquake Dispatch (TED), which rapidly detects earthquakes worldwide
Characterization of Earthquake Damage and Effects Using Social Media Data
People in the locality of earthquakes are publishing anecdotal information about the shaking within seconds of their occurrences via social network technologies, such as Twitter. In contrast, depending on the size and location of the earthquake, scientific alerts can take between two to twenty minutes to publish. The goals of this project are to assess earthquake damage and effects information, as
Science and Products
Crowd-Sourced Earthquake Detections Integrated into Seismic Processing
The goal of this project is to improve the USGS National Earthquake Information Center’s (NEIC) earthquake detection capabilities through direct integration of crowd-sourced earthquake detections with traditional, instrument-based seismic processing. During the past 6 years, the NEIC has run a crowd-sourced system, called Tweet Earthquake Dispatch (TED), which rapidly detects earthquakes worldwide
Characterization of Earthquake Damage and Effects Using Social Media Data
People in the locality of earthquakes are publishing anecdotal information about the shaking within seconds of their occurrences via social network technologies, such as Twitter. In contrast, depending on the size and location of the earthquake, scientific alerts can take between two to twenty minutes to publish. The goals of this project are to assess earthquake damage and effects information, as