FINAL TECHNICAL REPORT: Award Number G14AP00037 Network Based Estimates of Strain-rate and Uncertainty in the Central U.S.

Abstract Paleoseismic observations in the New Madrid Seismic Zone (NMSZ) show sequences of large earthquakes with mean interevent times of ∼ 500 years. Yet, current geodetic strain-rates are very low. Some studies have shown that average regional strain-rates are insignificantly different from zero. Others have found that differential velocities between selected sites are statistically significant. Clearly, a thorough understanding of errors in the GPS data is crucial in assessing the significance of any putative strain signal. Some estimates of GPS velocity uncertainties are very low, less than 0.1 mm/yr with 10 years of data. Yet, residual velocities relative to rigid plate models in nominally stable plate interiors can be an order of magnitude larger. This discrepancy could be caused by underestimating low frequency time-dependent noise in position time series, such as random walk. We show that traditional estimators, based on individual time series, are insensitive to low-amplitude random walk, yet such noise significantly increases GPS velocity uncertainties. We developed a method for determining representative noise parameters in GPS position time series, by analyzing an entire network simultaneously, that we refer to as the Network Noise Estimator (NNE). We analyze data from the aseismic central-eastern US, assuming that residual motions relative to North-America, corrected for glacial isostatic adjustment (GIA), represent noise. The position time series are decomposed into signal (plate rotation and GIA), and noise components. NNE simultaneously processes multiple stations with a Kalman filter, and solves for average noise components for the network by maximum likelihood estimation. Synthetic tests show that NNE correctly estimates even low level random walk, thus providing better estimates of velocity uncertainties than conventional, single station methods. To test NNE on actual data, we analyzed a heterogeneous 15 station GPS network from the central-eastern US, assuming the noise is a sum of random walk, flicker and white noise. For the horizontal time series NNE