Body-wave extraction and tomography at Long Beach, CA, with ambient-noise interferometry Nori Nakata * , Jason P. Chang, and Jesse F. Lawrence, Stanford University SUMMARY We retrieve diving P waves by applying seismic interferometry to ambient noise records observed at Long Beach, California, and invert travel times of these waves to estimate 3D P-wave velocity structure. The ambient noise is recorded by a dense and large network, which has about 2500 receivers with 100-m spacing. In contrast from the surface-wave extraction, body- wave extraction is much harder because body-wave energy is less than surface waves in this scale. For travel-time tomog- raphy, we need to extract body waves at each pair of receivers separately. Therefore, we employ two post-correlation filters to reject noisy signals (which are unusable for body-wave to- mography). The first filter rejects traces base on low P-wave correlation with the stack of all traces at that distance. The sec- ond filter measures coherent energy between all retained traces and suppresses incoherent noise in each trace. With these fil- ters, we can reconstruct clear body waves from each virtual source. Then we estimate 3D P-wave velocities from these waves with travel-time tomography. The velocities show high resolution structure. INTRODUCTION Seismic interferometry, a type of cross-correlation analysis, is a powerful tool to extract earth response from passive data in the local and global scale (e.g., Curtis et al., 2006; Ruigrok et al., 2010; Nishida, 2013). Theoretically, we can extract Green’s functions by using seismic interferometry (Lobkis and Weaver, 2001; Snieder, 2004; Wapenaar and Fokkema, 2006), and this technique works well to reveal velocity and atten- uation structures (e.g., Lin et al., 2009; Lawrence and Pri- eto, 2011). Although surface waves are easier to retrieve be- cause of stronger energy from ambient noise (e.g., Campillo and Paul, 2003; Shapiro et al., 2005), some studies find body -118˚06 33˚45' 33˚48' 2 km -118˚06 33˚45' 33˚48' -118˚06 33˚45' 33˚48' Figure 1: Map of receivers. The red dots show the location of receivers, and the blue star indicates the reference receiver used in Figure 2a. Figure 2: (a) Example of virtual shot gathers constructed from 10-day ambient-noise data. The virtual source is the blue star shown in Figure 1. Trace number is aligned with the distance from the virtual source. The frequency range is from 0.5 to 15.0 Hz. The blue lines indicate constant velocity travel times with an assumption of straight paths. (b) Stacked crosscorre- lation gather over all virtual shot gathers. A bin for this spatial stacking is 50 m. The frequency range is the same as panel (a). The blue lines indicate constant velocity travel times with an assumption of straight paths. waves (e.g., Roux et al., 2005; Poli et al., 2012). Draganov et al. (2009) and Nakata et al. (2011) used the extracted body waves for imaging with wave migration. Dense arrays are suitable for ambient-noise tomography, and Mordret et al. (2013) and de Ridder and Biondi (2013) dis- covered velocity structures in a regional (reservoir) scale using Scholte waves (interface waves between fluid and solid). In re- gional scales, body waves extracted by seismic interferometry are not clear enough for tomography. Therefore, in this study, we propose a technique to retrieve body waves from ambient noise data recorded at Long Beach, California, USA. Here, we first introduce the data set and initial virtual shot gathers. Then we present a technique to extract body waves. Finally, we show inverted velocity structures using travel-time tomog- raphy.