Interferometric body-wave retrieval from ambient noise after polarization filtering: Application to shallow reflectivity imaging

Abstract

Interferometric retrieval of body waves from ambient noise recorded at surface stations is usually challenged by the dominance of surface-wave energy, in particular in settings dominated by anthropogenic activities (e.g., natural resource exploitation, traffic, and infrastructure construction). As a consequence, ambient noise imaging of shallow structures such as sedimentary layers remains a difficult task for sparse and irregularly distributed receiver networks. We have determined how polarization filtering can be used to automatically extract steeply inclined compressional waves (P-waves) from continuous 3C recordings, and, in turn, it improves passive body-wave imaging. Being a single-station approach, the technique does not rely on a dense receiver array and is therefore well suited for data collected during surveillance monitoring for tasks such as reservoir hydraulic stimulation, CO2 sequestration, and wastewater disposal injection. We apply the method on a continuous data set acquired in the Wellington oilfield (Kansas, US), where local and regional seismicity and other forms of ambient noise provide an abundant source of surface- and body-wave energy recorded at 15 short-period receivers. We use autocorrelation (AC) to derive the shallow (<1 km) reflectivity structure below the receiver array and validate our workflow and results with well logs and active seismic data. Ray-tracing analysis and waveform modeling indicate that converted shear waves need to be taken into account for realistic ambient noise body-wave source distributions because they can be projected on the vertical component and might lead to misinterpretation of the P-wave reflectivity structure. Overall, our study suggests that polarization filtering significantly improves passive body-wave imaging on AC and interstation crosscorrelation. It reduces the impact of time-varying noise source distributions and is therefore also potentially useful for time-lapse ambient noise interferometry.