Towards adapting reverse vertical seismic profiling for ambient-noise imaging with transient sources: Automatic estimation of stationary-phase receivers for improved retrieval of the interferometric Green's function

摘要

Most of the ambient-noise studies are performed with sensor arrays located at the surface. Passive recordings containing seismic arrivals from subsurface sources could be seen as having a geometry resembling reverse vertical seismic profiling (RVSP). In such scenarios, the intersource seismic-interferometry technique can be used to redatum the surface receivers to the subsurface source positions resulting in virtual shot gathers at depth. The success of the interferometric processing primarily requires that a correlation panel created in the interferometric process contains stationary-phase regions that, when summed, retrieve events with correct timing, whereas nonstationary contributions are canceled. We have addressed the combination of the RVSP configuration and ambient-noise measurements. We develop a prototype of a data-driven technique allowing us to adapt the summation process such that changes in the stationary-phase requirements imposed by changes in the noise-sources distribution can be adaptively satisfied without the need for array redeployment. We propose to identify the receivers located in the stationary-phase regions by scanning for stationary contributions in the correlation panel prior to stacking. Our method uses the correlation coefficient and time windowing to distinguish between stationary and nonstationary arrivals. The improved Green's function estimate is obtained by limiting the summation to only those receivers that, when summed, enhance the stationary and attenuate the nonstationary contributions. We test this using a simple 2D numerical example to find a practical way to alleviate insufficient receiver coverage.We determine the theoretical possibility to improve the intersource Green's function estimates without explicit knowledge of the target and the source distribution. However, our data-driven approach has the disadvantage of being limited to scenarios in which the correlation panel from all receivers contains identifiable stationary