Segmentation in episodic tremor and slip all along Cascadia
نویسندگان
چکیده
A.1. Slow Slip Episodes In this section, we discuss the relevant information for identification of slow slip episodes, with additional details presented elsewhere (Holtkamp et al., 2006, ms. in prep.). The GPS data analyzed for slow slip episodes is from the PANGA network provided by the Central Washington University clearinghouse (2005). PANGA time series are network solutions with phase ambiguities resolved and typically consist of one sample per day. Provided time series have been postprocessed with GIPSY, and have linear trends, steps due to earthquakes or hardware upgrades, and annual and semi-annual sinusoids signals simultaneously estimated and removed (Szeliga et al., 2004). In this study, the signal to noise ratio of the GPS data is further enhanced by taking a 10 sample running average of the time series that reduces the scatter among neighboring data points. As shown in previous studies examining transient episodes (Brudzinski et al., 2007; Larson et al., 2004; Lowry et al., 2001), anomalous displacements during slow slip events can be estimated by fitting the GPS coordinate time series with combination of a linear and hyperbolic tangent function. By using a grid search over hyperbolic tangent function parameters (timing and duration of transient), the linear parameters of steady-state velocity and transient displacement are estimated via least squares minimization, weighted by the formal inverse variance of GPS coordinate estimates. This technique has been particularly useful for characterizing the precise timing and overall displacement when a slow slip episode has been identified (Brudzinski et al., 2007; Larson et al., 2004; Lowry et al., 2001). Slow slip episodes are automatically identified by 1) applying the hyperbolic tangent fit over a scrolling window of the time series (6 months before event time and 6 months after) incremented at 0.01 years, 2) using an f-test to confirm when the combined linear and hyperbolic tangent fit is significantly better than a linear fit at 99% confidence within the window, and 3)
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