Network strain filter: A new tool for monitoring and detecting transient deformation signals in GPS arrays

We have developed a tool to detect transient deformation signals from large-scale (principally GPS) geodetic arrays, referred to as a Network Strain Filter (NSF). The strategy is to extract spatially and temporally coherent signals by analyzing data from entire geodetic networks simultaneously. The NSF models GPS displacement time series as a sum of contributions from secular motion, transient displacements, site-specific local benchmark motion, reference frame errors, and white noise. Transient displacements are represented by a spatial wavelet basis with temporally varying coefficients that are estimated with a Kalman filter. A temporal smoothing parameter is also estimated online by the filter. The problem is regularized in the spatial domain by minimizing a smoothing (Laplacian) norm of the transient strain rate field. To test the performance of the NSF, we carried out numerical tests using the Southern California Integrated GPS Network station distribution and a 3 year long synthetic transient in a 6 year time series. We demonstrate that the NSF can identify the transient signal, even when the colored noise amplitude is comparable to that of transient signal. Application of the method to actual GPS data from the Japanese GPS network (GEONET) on the Boso Peninsula also shows that the NSF can detect transient motions resulting from aseismic fault slip.