Transient signal detection using GPS position time series

Abstract

Continuously operating Global Positioning System (GPS) networks record station position changes with millimeter-level accuracy and have revealed transient deformations on various spatial and temporal scales. However, the transient deformation may not be easily identified from the position time series because of low signal-to-noise ratios (SNR), correlated noise in space and time and large number of sites in a network. As a systematic detection method, we use state estimation based on Kalman filtering and principal component analysis (PCA). State estimation improves the SNR in the time domain by estimating secular and transient motions and reducing the level of both white and colored noise. PCA improves the SNR in space domain by accounting for the coherence of transient motions between nearby sites. Synthetic tests show that the method is capable of detecting transient signals embedded in noisy data but complex signals (e.g., large-scale signals in space and time, multiple and/or propagating signals) are difficult to detect and interpret. We demonstrate the detection capability with two known signals in the Los Angeles basin, California: far-field coseismic offsets associated with the 1999 Hector Mine earthquake and locally-observed hydrologic deformation due to heavy rainfall in winter 2004-2005 in San Gabriel Valley. We applied the method to the daily GPS data from the Plate Boundary Observatory (PBO) network in Alaska and in the Washington State section of the Cascadia subduction zone. We have detected a transient signal whose maximum displacement is -9 mm in the horizontal and -41 mm in the vertical at Akutan volcano, Alaska, during the first half of 2008 and two previously unrecognized small slow slip events with average surface displacements less than 2 mm, which was thought to be below current GPS resolution. The detection method improves the SNR and therefore provides higher resolution for detecting weak transient signals, and it can be used as a routine monitoring system.