Image gather reconstruction using StOMP |

In RTM downward continuation based wave-equation imaging we have a source and receiver wavefield , where is a given time and is a given shot. We form our migrated image as

(1) |

The imaging condition for RTM just replaces the sum over time with a sum over frequency. This basic imaging condition hides our information about velocity and rock properties by stacking over all angles illuminated by our source/receiver geometry.

Several solutions to this problem have been proposed that attempt to extract information as a function of angle from the data. In this paper I focus on the family of methods that construct shift gathers by cross-correlation. The shift can be a function of space (Sava and Fomel, 2003) or time (Sava and Fomel, 2006), or both. In these cases the imaging condition takes the form of

(2) |

where are how much the source and receiver wavefields have been shifted in a given direction. Introducing these shift gathers poses two problems. The first, and smaller of the problems, is that computational expense associated with constructing these gathers, when going beyond a single shift axis, is at least on the same order of magnitude as the propagation kernel. The larger problem is the expansion of the volume size anywhere from 20-1000 fold, means that the shift gathers must be stored a memory level further away from the processor, often on disk, which is often several orders of magnitude more expensive to access. Finding some way to reduce the volume size, and better still, the computational expense associated with shift gather construction, can be highly beneficial. In order for compressive sensing to be an appropriate way to reduce the volume size it is important to consider the compressibility of seismic data.

There is significant literature on compressing seismic data. Relatively low compression ratios are achievable by compressing a trace. Significantly higher compression ratios are achieved by multi-dimensional approaches. Generally, the best results have used either multi-dimensional wavelets (Mallat, 1999) or its successor curvelets (Cands and Donoho, 1999). Villasenor et al. (1996) showed that compression ratios of 100:1 were achievable by compressing a 4-D volume ( ). Further, Villasenor et al. (1996) states that the header information was the limiting factor in achieving even higher ratios.

Subsurface offset gathers potentially represent even higher, up to six, dimensional data. To test compressibility, I used a 4-D volume ( ) of dimensions (32,32,400,64). Figure 1 shows one of these subsurface offset gathers and its neighbors, note the similarlity. Following Villasenor et al. (1996), I chose the 9/7 bi-orthonormal transform (Antonini et al., 1992) used in JPEG compression. Figure 2 shows the resulting transform space and a histogram of the absolute values. I then used several different thresholds throwing away 90%, 95%, 98%, and 99% of the data in the wavelet domain respectively. Figure 3 shows the result of transforming these thresholded volumes back into the space-domain. The resulting images are near-perfect at 95% and potentially acceptable at 98%. This translates into an acceptable compression ratio of approximately 30:1.

raw
Five neighboring subsurface offset gathers. B and C are one midpoint in
X before and after A. E is one midpoint in Y before A. Note the
spatial similarity, which lends itself to compression.
Figure 1. |
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wavelet1
Panel A shows the wavelet domain representation of the
4-D volume used in this experiment. Panel B shows a zoom into a portion of
the wavelet domain. Panel C shows a histogram of the
wavelet domain values. Note how the vast majority of the values are nearly zero.
Figure 2. |
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offset
The result of zeroing the smallest values of the wavelet
domain representation shown in Figure 2A. All four
panels show the same subsurface offset gather shown in Figure 1. A shows the
result of clipping 90% of the values; B, 95%; C, 98%; and D,99%. Note how
the reconstructed gather is nearly identical up to a 98% clip.
Figure 3. |
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Image gather reconstruction using StOMP |

2012-05-10