Enhanced refractor imaging by supervirtual interferometry


Refraction surveys are a well-established method of imaging subsurface velocities, both in terms of the deep crustal structure at global scales and in the shallow near surface. These surveys generally involve deploying an array of receivers on the surface (or water bottom) and recording arrivals from a seismic source initiated at or near the surface. In an ideal case where an interface defines a boundary with a sharp increase in velocity, the head-wave refraction arrivals are described by raypaths which follow a diving-wave path down to the interface and refract along it, then follow a diving-wave path back to the surface (or water bottom) where the receivers are located. These arrivals, if they have a suffi ciently high signal-to-noise ratio (SNR), are picked in the shot gathers and inverted to give the traveltime tomograms in either exploration-scale or global-scale tomography. However there are two common limitations of conventional refraction tomography:

  • Poor signal-noise ratio of first-arrival refractions at long offsets. Due to spherical divergence, attenuation and ambient noise, the SNR of head-wave refractions is insufficient for accurate picking of first-break traveltimes beyond a certain source-receiver offset.
  • Only first-arrival refractions are typically picked in the raw data, and later refraction arrivals are generally unpickable because of interference from body waves.

As result the maximum depth of investigation of refraction surveys is limited by the inability to identify later head-wave arrivals in the record. Refraction interferometry offers the possibility of overcoming these limitations as it aligns and stacks together refraction arrivals that propagate along the same portion of the refractor (Dong et al., 2006). Similar to the NMO correction that flattens reflections in a CMP gather, interferometric correlation of traces recorded at two fixed geophones aligns the refraction arrivals from the same refractor; this alignment is valid for a large number of different source positions. Th e result is that head-wave arrivals generated from diff erent sources can be stacked together to form virtual head-wave traces with an enhanced SNR (Bharadwaj and Schuster, 2010). This potentially offers a significant improvement over conventional processing of head-wave arrivals

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