A CDP gather before and after 5D interpolation.
A seismic trace has four spatial dimensions describing its geographical location. An ideal 3D seismic survey is densely and evenly populated in these four dimensions, but such surveys are almost never acquired due to physical and economic constraints. 5D interpolation takes a prestack seismic data set that is sparsely populated in four spatial dimensions, and increases the trace density by interpolating in all four dimensions simultaneously. This can bring the following benefits:
- Improved prestack migration.
- Less random noise.
- Reduced acquisition footprint.
- More continuous reflectors, particularly up shallow.
- Improved multiple removal afterwords.
- Improved AVA / AVO analysis.
- Improved inversion.
Juniper Bay’s 5D interpolation is based on matrix rank reduction on constant-frequency slices. It has the following features:
- Preserves multiples, curving events, diffractions, and AVA / AVO effects.
- Excellent at interpolating across small gaps.
- Strong random noise attenuation.
- Interpolates better than MWNI in regions of low signal-to-noise.
- Interpolates in the common-offset-azimuth (COA) or common-offset-vector (COV) domain.
- Only a few easy-to-choose parameters.
- Option to only perform random noise attenuation without interpolation.
- Built-in easy-to-use quality controls such signal leakage tests and COA / COV stacks
It has limitations:
- Like most 5D interpolators, all four spatial dimensions are represented by discrete spatial bins. That is to say, it doesn’t handle continuous coordinates.
- Coherent noise such as ground roll is often interpreted as signal and thus interpolated.
Three types of easy-to-use quality controls are built into Juniper Bay’s 5D interpolation:
- Signal leakage test after stack.
- Signal leakage test before stack.
- Automatically generated COA / COV stacks.
The first type is described in Cary and Perz (2012), and can be conducted on any survey. The second was developed by Juniper Bay. It’s highly useful, but only recommended for unstructured data. The third, comparing interpolated gathers to COA / COV stacks of super gathers, is also only for unstructured data.
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References
Downton, J., D. Holy, D. Trad, L. Hunt, S. Reynolds, and S. Hadley, 2010, The effect of interpolation on imaging and azimuthal AVO: A Nordegg case study: 80th Annual International Meeting, SEG, Expanded Abstracts, 383-387.
Hunt, L., J. Downton, S. Reynolds, S. Hadley, D. Trad, and M. Hadley, 2010, The effect of interpolation on imaging and AVO: A Viking case study: Geophysics 75 (6), WB265-WB274.
Milton, A., S. Trickett, and L. Burroughs, 2011, Reducing acquisition costs with random sampling and multidimensional interpolation: 81st Annual International Meeting, SEG, Expanded Abstracts, 52–56.
Oropeza, V. E., and M. D. Sacchi, 2011, Simultaneous seismic data denoising and reconstruction via multichannel singular spectrum analysis (MSSA): Geophysics, 76.
Trad, D., 2009, Five-dimensional interpolation: Recovering from acquisition constraints: Geophysics, 74, no. 6, V123- V132.
Trickett, S., L. Burroughs, A. Milton, L. Walton, and R. Dack, 2010, Rank-reduction-based trace interpolation: 80th Annual International Meeting, SEG, Expanded Abstracts, 3829–3833.
Zheng, Y., L. Ross, J. Guo, W. Liao, B. Nemeth, and C. Escalante, 2015, Choice of algorithm and data domain for 5D trace interpolation: SEG Technical Program Expanded Abstracts 2015, 3895-3899.