HR: 0800h
AN: G21A-0127    [Abstracts]
TI: Use of a Principal Component Analysis to Identify Precise Tectonic Rates From GPS Time-Series in the Ventura Basin
AU: * Austin, K E
EM: austink@usc.edu
AF: Department of Earth Sciences, University of Southern California, 3651 Trousdale Pkwy, Los Angeles, CA 90089-0740 United States
AU: Owen, S E
EM: owen@usc.edu
AF: Department of Earth Sciences, University of Southern California, 3651 Trousdale Pkwy, Los Angeles, CA 90089-0740 United States
AB: The region in the Ventura basin is one of the fastest converging areas in Southern California. Convergence rates in the basin and the Santa Barbara channel from geodetic measurements are ~6-8mm/yr, with uplift rates estimated at 1-2mm/yr in places measured by leveling. These low uplift rates can easily be masked over the short time periods of GPS measurements by strong seasonal signals that may differ from station to station and result from differing processes. Similar to the Los Angeles basin, the seasonal signals in this area are almost certainly caused by fluid injection/removal, and are not related to fault motion. By identifying and removing these signals we can better estimate the interseismic strain rates and aid in estimating fault locking depths. A principal component analysis (PCA) is used to isolate the tectonic signals in the GPS time-series from Southern California Integrated GPS Network (SCIGN) stations within the Ventura basin, with measurements that span a two year period. Using data products available from the regional processing centers, the PCA is applied to the regionally filtered time-series to break down the signal and identify the different components. Our method of post processing requires that all of the data be simultaneous, and have equal time steps. Since zero or null values are not allowed in the time-series data, the data gaps are filed via interpolation. This approach stacks the data within the network and breaks it down into the dominant patterns common to all of the time-series data within the region. As a control we test the approach against a set of synthetic time-series that contain variations in linear rates, annual signals and random noise. Our analysis of the Ventura Basin, as well as the synthetic data shows that this process is most effective at identifying and removing strong isolated signals present at 1 or 2 stations within the network, as opposed to signals spatially and temporally correlated across the network, and is useful in removing scatter in the data, resulting in a better determined rate.
DE: 8107 Continental neotectonics
DE: 8110 Continental tectonics--general (0905)
DE: 1200 GEODESY AND GRAVITY
DE: 1208 Crustal movements--intraplate (8110)
SC: Geodesy [G]
MN: 2004 AGU Fall Meeting