Abstract

Online Material: Synthetic datasets, the parameters used to generate them, and MATLAB scripts for parsing data provided through the testing center described in the text. Over the past decade the number and size of continuously operating Global Positioning System (GPS) networks has grown substantially worldwide. A steadily increasing volume of freely available GPS measurements, combined with the application of new approaches for mining these data for signals of interest, has led to the identification of a large and diverse collection of time‐varying Earth processes. One phenomenon that has been observed is transient fault slip (also termed slow slip events or silent earthquakes) occurring over time spans of days to years (e.g., Linde et al. , 1996; Hirose et al. , 1999; Dragert et al. , 2001; Miller et al. , 2002; Kostoglodov et al. , 2003; Douglas et al. , 2005; Shelly et al. , 2006; Ide et al. , 2007; Lohman and McGuire, 2007; Schwartz and Rokosky, 2007; Szeliga et al. , 2008). Such events have been widely observed in subduction zones but are also found in other tectonic settings (Linde et al. , 1996; Cervelli et al. , 2002; Murray and Segall, 2005; Lohman and McGuire, 2007; Montgomery‐Brown et al. , 2009; Shelly, 2010; and references therein). Although retrospective study of slow‐slip events using geodetic observations is driving the formulation of new models for fault‐zone behavior and constitutive laws (e.g., Lapusta et al. , 2000; Liu and Rice, 2007; Lapusta and Liu, 2009; Segall and Bradley, 2012a), much of the research on near‐real‐time detection and characterization of anomalous behaviors along fault zones has focused solely on the use of seismic tremor (e.g., Rogers and Dragert, 2003; Shelly et al. , 2006; Ito et al. , 2007). The characteristics of transient slip …

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