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, 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 demonstrate how polarization filtering can be used to automatically extract steeply inclined P-waves from continuous three-component recordings and in turn 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, CO_2 sequestration, and wastewater disposal injection. We apply the method on a continuous dataset acquired in the Wellington oilfield (Kansas, US), where local and regional seismicity, and other forms of ambient noise provide an abundant source of both surface- and body-wave energy recorded at 15 short-period receivers. We use autocorrelation to derive the shallow (lt; 1 km) reflectivity structure below the receiver array and validate our workflow and results with well logs and active seismic data. Raytracing analysis and waveform modeling indicates that converted shear waves need to be taken into account for realistic ambient noise body-wave source distributions, as 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 both autocorrelation 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.
High-resolution passive seismic imaging of shallow subsurface structures is often challenged by the scarcity of coherent bodywave energy in ambient noise recorded at surface stations. We show that autocorreleation (AC) of teleseismic P-wave coda extracted from just 1-month of continuous recording at 5 Hz geophones can overcome this limitation. We apply this method to investigate the longitudinal subsurface structure of Unaweep Canyon, a paleovalley in western Colorado (US) with complex evolution. Both fluvial and glacial processes have been proposed to explain the canyon's genesis and morphology. The teleseismic P-wave coda AC retrieves zero-offset reflections from the shallow (200 -500 m depth) basement interface at 120 stations along a 5 km long profile. Additionally, we invert interferometrically retrieved surface wave dispersion for the shear-wave structure of the sedimentary fill. Combined interpretation of these results and other geophysical and well data suggests an overdeepened basement geometry due to glacial processes.
High-resolution passive seismic imaging of shallow subsurface structures is often challenged by the scarcity of coherent body-wave energy in ambient noise recorded at surface stations. We show that the autocorrelation (AC) of teleseismic P-wave coda extracted from just one month of continuous recording at 5 Hz geophones can overcome this limitation. We apply this method to investigate the longitudinal subsurface bedrock structure of Unaweep Canyon—a paleovalley in western Colorado (United States) with complex evolution. Both fluvial and glacial processes have been proposed to explain the canyon’s genesis and morphology. The teleseismic P-wave coda AC retrieves zero-offset reflections from the shallow (200–500 m depth) basement interface at 120 stations along a 5 km long profile. In addition, we invert interferometrically retrieved surface-wave dispersion for the shear-wave structure of the sedimentary fill. Combined interpretation of these results and other geophysical and well data suggests an overdeepened basement geometry most consistent with glacial processes.
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