1	(t,x) domain, pattern-based ground roll removal Morgan P. Brown* and Robert G. Clapp Stanford Exploration Project Stanford University
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3	Ground Roll - what is it? To first order: Rayleigh (SV) wave. Dispersive, often high-amplitude In (t,x,y), ground roll = cone. Usually spatially aliased. In practice, “ground roll cone” muted.
4	Talk Outline Motivation for advanced separation techniques. Model-based signal/noise separation. Non-stationary (t,x) PEF. Least squares signal estimation. Real Data results.
5	Advanced Separation techniques…why bother? Imaging/velocity estimation for deep targets. Rock property inversion (AVO, impedance). Single-sensor configurations.
6	Signal/Noise Separation: an Algorithm wish-list Amplitude-preservation. Robustness to signal/noise overlap. Robustness to spatially aliased noise.
7	Talk Outline Motivation for advanced separation techniques. Model-based signal/noise separation. Non-stationary (t,x) PEF. Least squares signal estimation. Real Data results.
8	Coherent Noise Separation - a “model-based” approach
9	Coherent Noise Subtraction The Noise model: kinematics usually OK, amplitudes distorted. Simple subtraction inferior. Adaptive subtraction: mishandles crossing events, requires unknown source wavelet. Wiener optimal signal estimation.
10	Wiener Optimal Estimation Assume: data = signal + noise signal, noise uncorrelated signal, noise spectra known.
11	Spectral Estimation Answer: Smoothly non-stationary (t,x) Prediction Error Filter (PEF).
12	Spectral Estimation Answer: Smoothly non-stationary (t,x) Prediction Error Filter (PEF).
13	Talk Outline Motivation for advanced separation techniques. Model-based signal/noise separation. Non-stationary (t,x) PEF. Least squares signal estimation. Real Data results.
14	Helix Transform and multidimensional filtering
15	Helix Transform and multidimensional filtering
16	Why use the Helix Transform?
17	Convolution with stationary PEF
18	Convolution with smoothly non-stationary PEF
19	Smoothly Non-Stationary (t,x) PEF - Pro and Con Robust for spatially aliased data. Handles missing/corrupt data. No explicit patches (gates). Stability not guaranteed.
20	Estimating the Noise PEF Small phase errors. Amplitude difference OK.
21	Estimating the Noise PEF
22	Estimating the Noise PEF Problem often underdetermined. Apply regularization.
23	Estimating the Noise PEF Problem often underdetermined. Apply regularization.
24	Estimating the Signal PEF Use Spitz approach, only in (t,x)
25	Talk Outline Motivation for advanced separation techniques. Model-based signal/noise separation. Non-stationary (t,x) PEF. Least squares signal estimation. Real Data results.
26	Estimating the Unknown Signal
27	Estimating the Unknown Signal
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32	"e too small = leftover..." e too small = leftover noise. e too large = signal removed. Ideally, should pick e = f(t,x).
33	Talk Outline Motivation for advanced separation techniques. Model-based signal/noise separation. Non-stationary (t,x) PEF. Least squares signal estimation. Real Data results.
34	Data Specs Saudi Arabian 3-D shot gather - cross-spread acquisition. Test on three 2-D receiver lines. Strong, hyperbolic ground roll. Good separation in frequency. Noise model = 15 Hz Lowpass.
35	Data Results - Gather #1
36	Data Results - Gather #1
37	Data Results - Gather #1
38	Data Results - Gather #2
39	Data Results - Gather #2
40	Data Results - Gather #3
41	Data Results - Gather #3
42	Conclusions (t,x) domain, pattern-based coherent noise removal Amplitude-preserving. Robust to signal/noise overlap. Robust to spatial aliasing. Parameter-intensive.
43	Acknowledgements Saudi Aramco SEP Sponsors Antoine Guitton