Previous: Extension to 3-D
- The predictive signal/noise separation method operates successfully, even when the
noise is severely spatially aliased.
The data need not be sampled finely in space to remove ground roll, a fact which is
profound, considering the high cost of land-based seismic acquisition.
Crawley 1998 shows another example of the feasibility
of using PEF's to estimate aliased, coherent seismic signals.
- Ground roll comes in many different flavors.
A method which exploits the particular moveout patterns of one observed case
of ground roll is sure to fail on others.
The predictive method is blind to this; assuming a first order separation gives
a viable noise model, the specific moveout patterns of signal and noise are
- Ground roll is almost always dispersive.
The f-x-based algorithm of Spitz 1999 is computationally
more efficient than our t-x domain algorithm, but it requires a time-invariant
Ground roll is usually a potpourri of different wave trains, all with different
dispersion rates, strongly violating this temporal stationarity assumption.
The nonstationary t-x domain technique has no such limitation.
Nonstationary filtering is possible in the frequency domain, but the
distinct computational advantage over time domain methods is lost.
- An effective, general method for ground removal in 3-D may have profound
effects on the future of seismic acquisition. If severe ground roll necessitates
the recording of extremely long offsets or the use of large receiver arrays,
the ability to remove the ground roll robustly from single-sensor data will cut
acquisition costs considerably.
Such a ground roll removal technique could greatly assist smaller-scale
survey efforts (environmental or university research) which do not boast the same
resources as a large, multinational oil company.
- Parenthetically, it should be emphasized that we have presented a method to do
signal/noise separation, not simply noise removal. The output estimated noise
may contain useful information, such as shear wave velocities. Multicomponent seismology
is a promising specialty which would certainly benefit from any additional
constraints provided by a robust ``noise'' extraction.
- Considerable effort is currently expended in pursuit of the perfect noise model,
particularly in multiple suppression.
However the results presented here show that predictive signal/noise separation produces
good results with an imperfect noise model.
An open question remains: might more primitive methods of obtaining a multiple model
suffice if passed to a predictive signal/noise separation algorithm?
- As discussed in Appendix A, we are concerned that the current approach may not
fully account for correlation between signal and noise.
- While the nonstationary t-x PEF estimation is more robust than stationary
f-x methods in terms of accurately predicting all the coherent events,
the cost is considerably higher. As mentioned above, the method must be
optimized before it becomes an industry standard.
- Our approach is parameter-intensive. Fortunately,
in practical cases, the parameter choices are similar for most data gathers in
the same survey.
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Stanford Exploration Project