SCEC Award Number 11124 View PDF
Proposal Category Individual Proposal (Integration and Theory)
Proposal Title Near Real-Time Estimation of Velocity Gradient Tensor Fields for Continuous Monitoring in Southern California
Investigator(s)
Name Organization
William Holt Stony Brook University
Other Participants
SCEC Priorities A5, C, A11 SCEC Groups Transient Detection, Geodesy, SDOT
Report Due Date 02/29/2012 Date Report Submitted N/A
Project Abstract
 We have further tested and developed a geodetic network processing system designed to detect anomalous strain transients. The modeling procedure determines time-dependent velocity, displacement and velocity gradient tensor fields from continuous GPS time series [Hernandez et al., 2005, 2006, 2007a,b,c]. The plan thus fulfills the SCEC Science Objective A5 to “Develop a geodetic network processing system that will detect anomalous strain transients”. We tested retrospectively two cases from the SCEC IV test and the third case blind. All three tests resolved strain anomalies both spatially and temporally at a high level of accuracy. Work also began in the development of detection automation. More work is needed in this area to determine objective thresholds and methods for automated transient recognition. This plan for automation fulfills the recommendations under Research Strategies in Tectonic Geodesy for A5 to: (a) “Adapt methods for detecting, assessing and interpreting transient deformation signals so that they can be run with minimal user intervention as part of an ongoing detection effort that ingests data at frequent (daily to weekly) time intervals”; and (b) “Refine capabilities of detection algorithms and assess their sensitivity thresholds through continued participation in the Transient Detection Blind Test Exercise”. The data product that we are developing falls under Science Objective C to “Improve and develop community products (data or descriptions) that can be used in system-level models for the forecasting of seismic hazard.”
Intellectual Merit The intellectual merit is that we have developed a modeling procedure that quantifies time-dependent velocity, displacement and velocity gradient tensor fields from continuous GPS time series in southern California. The code is currently under development for automated detection of anomalous strain events within southern California. The plan fulfills the SCEC Science Objective A5 to “Develop a geodetic network processing system that will detect anomalous strain transients”. The work on automation fulfills the recommendations under Research Strategies in Tectonic Geodesy for A5 to: (a) “Adapt methods for detecting, assessing and interpreting transient deformation signals so that they can be run with minimal user intervention as part of an ongoing detection effort that ingests data at frequent (daily to weekly) time intervals”; and (b) “Refine capabilities of detection algorithms and assess their sensitivity thresholds through continued participation in the Transient Detection Blind Test Exercise”. The data product that we are developing falls under Science Objective C to “Improve and develop community products (data or descriptions) that can be used in system-level models for the forecasting of seismic hazard.”
Broader Impacts The Broader Impact of this work involves the training of a graduate student, Gina Shcherbenko, and two undergraduates, Eric Caruso and Patrick Abejar. The development of a data product enabling automated detection of anomalous strain from CGPS data also constitutes a broader impact.
Exemplary Figure Figure 2