Imaging Global Seismic Phase Arrivals by Stacking Array Processed Short-Period Data

Abstract

For decades, seismic data stacking has been used to improve the signal-to-noise ratio (SNR) of coherent arrivals in seismic recordings. Seismic arrays are especially well-suited for this task and have been used to detect and identify typically weak seismic arrivals ( e.g., Birtill and Whiteway 1965; Green et al. 1965). Seismic arrays are deployments of no fewer than three seismometers with uniform instrumentation that allow the recorded time series to be stacked as an ensemble. Typical array apertures range from 2 km to 200 km (Rost and Garnero 2004) and consist of 10 to 100 stations. All seismic array elements receive a centralized time signal, minimizing possible timing errors. Stations are deployed in specialized configurations to ensure a high coherence of transient signals in the wavefield, while the statistical noise component is uncorrelated between stations (Haubrich 1968). Special techniques have been developed to process seismic array data (for a review, see Rost and Thomas 2002), enabling the use of subtle arrivals in the seismic wavefield for seismic analysis and thus holding promise for higher resolution snapshots of the structure of the Earth's interior ( e.g. , Vidale and Benz 1993; Vidale and Earle 2000; Rost et al. 2005b). With increasing numbers of temporary and permanent array installations, data are becoming readily available for further high-resolution studies of the Earth's interior. In this paper we present a method to extract array-specific information ( e.g., vertical incidence angle-slowness) from a large number of array recordings. Slowness, time, and amplitude information from array recordings of individual earthquakes are combined into a single summary trace per earthquake, then stacked with other summary traces from a multitude of earthquakes recorded at the same seismic array. As a test case we process short-period array data from the Canadian Yellowknife Array (YKA) to highlight seismic phases …