Theory of supervirtual refraction interferometry

Abstract

S U M M A R YInverting for the subsurface velocity distribution by refraction traveltime tomography is a wellaccepted imaging method by both the exploration and earthquake seismology communities. A significant drawback, however, is that the recorded traces become noisier with increasing offset from the source position, and so accurate picking of traveltimes in far-offset traces is often prevented. To enhance the signal-to-noise ratio (SNR) of the far-offset traces, we present the theory of supervirtual refraction interferometry where the SNR of far-offset head-wave arrivals can be theoretically increased by a factor proportional to √ N ; here, N is the number of receiver or source positions associated with the recording and generation of the head-wave arrival. There are two steps to this methodology: correlation and summation of the data to generate traces with virtual head-wave arrivals, followed by the convolution of the data with the virtual traces to create traces with supervirtual head-wave arrivals. This method is valid for any medium that generates head-wave arrivals recorded by the geophones. Results with both synthetic traces and field data demonstrate the feasibility of this method.There are at least four significant benefits of supervirtual interferometry: (1) an enhanced SNR of far-offset traces so the first-arrival traveltimes of the noisy far-offset traces can be more reliably picked to extend the useful aperture of the data, (2) the SNR of head waves in a trace that arrive later than the first arrival can be enhanced for accurate traveltime picking and subsequent inversion by later-arrival traveltime tomography, (3) common receiver-pair gathers can be analysed to detect the presence of diving waves in the first arrivals, which can be used to assess the nature of the refracting boundary, and (4) the source statics term is eliminated in the correlation operations so that the timing of the virtual traces is independent of the source excitation time. This suggests the possibility of applying this method to earthquake data recorded by receivers that are inline with the refraction paths and source locations.

Three-dimensional supervirtual seismic refraction interferometry

“…The first stage involves crosscorrelation of traces recorded from postcritical sources to generate virtual traces, and the second stage is the convolution of the virtual traces with the raw traces (i.e traces before crosscorrelation) to create the supervirtual traces (Bharadwaj et al, 2012). However, the process of convolving the enhanced traces with the raw traces re-introduces certain proportion of noise back into the data by generating side lobes (Alshuhail et al, 2012) around the maximum amplitude of the enhanced traces.…”

Monitoring CO2 storage using ghost reflections retrieved from seismic interferometry

“…An example of non-physical arrivals are the so-called virtual refractions (e.g. Dong et al, 2006;Tatanova et al, 2008;Mikesell et al, 2009;Bharadwaj et al, 2011). These non-physical events can be identified, as they pass through the virtual-source position at 0 s and have a linear moveout.…”

On a plane-wave based crosscorrelation-type seismic interferometry

“…Supervirtual interferometry is a novel technique to increase the signal-to-noise ratio (S/N) of far-offset refracted waves (Bharadwaj et al, 2012). With this technique, the receiver spread of the refraction survey can be fully used if we are only interested in the traveltime information.…”

“…This mapping is generally applicable to reflection seismic data, such as surface seismic profile (SSP) and vertical seismic profile (VSP), both of which are typically considered in seismic interferometry (Schuster and Zhou, 2006). This mapping is also applicable to refraction geometry used in the recent work of supervirtual interferometry (Mallison et al, 2011;Bharadwaj et al, 2012). Numerous methods using this transform have been investigated for seismic wave filtering (Tatham, 1989), multiple attenuation (Liu et al, 2000), seismic forward modeling (Vigh and Starr, 2008;Tao and Sen, 2013a), migration (Stoffa et al, 2006), and inversion (Sen et al, 2003).…”