1. Filters  Temporal Fourier (t f) transformation  Spatial Fourier (x k x ) transformation applications  f-k x transformation  Radon (-p x ) transformation Linear Radon transform Parabolic Radon transform

2. t domainf domain Time domainFrequency domain Fourier transformation t-x domainf-k x domain Time-Place Frequency-Wavenumber  -p x domain (Intercept - slowness) 1-D-Transformation 2-D-Transformation Time-Place t-x domain 2-D-Fourier  -p-Transformation Transformation domains

3. Temporal Fourier transformation Fourier Transformation: Inverse Fourier Transformation: f N =1/(2  t) Sampling will preserve all frequencies up to the Nyquist frequency:

4. frequency amplitude phase f1f1 f2f2 Sum: f1f1 f2f2

5. f1f1 f2f2 f1f1 f2f2

6. Spatial Fourier transformation Fourier Transformation: Inverse Fourier Transformation: Spatial Fourier transformation is discussed for one horizontal (x) direction, but can be carried out in the two horizontal directions.

7. Temporal versus Spatial Fourier transformation Sampling interval  t sampling rate (sampling frequency) 1/  t Sampling will preserve all frequencies up to the Nyquist frequency: f N =1/(2  t) Spatial sampling interval  x Spatial sampling rate (sampling frequency) 1/  x Sampling will preserve all frequencies up to the Nyquist frequency: k N =1/(2  x) Temporal Fourier transformation Spatial Fourier transformation

8. Apparent velocity:  Apparent wavelength: The phase velocity which a wavefront appears to have along a line of geophones

9.  Number of waves per unit distance perpendicular to a wavefront  v app = v / sin  Horizontal Wave Incoming Wave v v Wavefront  v app = v Apparent wavenumber k app

10. f k a (apparent wavenumber) f = v a k Slope v a f-k-Spectrum a

11. Example of a shot Reynolds, 1997

12. kf-spectrum High apparent Velocity noise

13. Spatial sampling criterion From a practical point of view, subsequent measurements must be carried out in such a way that events on separate traces can be correlated as coming from the same horizon or reflection point in the subsurface (Yilmaz, 1987) For a given frequency component, the time delay between subsequent measurements can be at most half the period (T/2) of that frequency component to enable a correlation of two measured reflections as coming from the same horizon Max time delay: Two spatial samples for one apparent wavelength

14. Spatial sampling criterion xx t1t1 t2t2 xx t3t3 T/2       < < xx > >

15. Aliasing

16. Yilmaz, 1987 Influence of frequency on aliasing

17. Yilmaz, 1987 Influence of dip on aliasing

18. Yilmaz, 1987 Summation of dipping events

19. Yilmaz, 1987 Influence of spacing on aliasing

20. Yilmaz, 1987 Zero-offset section And its k-f amplitude spectrum

21. Composite walk-away noise test Yilmaz, 1987 Rejection ground roll energy

22. Yilmaz, 1987 CMP gathers from a shallow marine survey before and after F-k dip filtering to remove coherent noise with corresponding f-k spectra

23. CMP gathers from a shallow marine survey Before and after f-k dip filtering to remove coherent linear noise Yilmaz, 1987

24. Synthetic CMP gathers containing multiples primaries Water-bottom multiples +=VM velocity multiples VP velocity primaries Yilmaz, 1987

25. NMO correction using primary velocity function Yilmaz, 1987

26. Zeroing in the f-k domain After NMO correction Zero-ing in f-k quadrant Inverse NMO Yilmaz, 1987

27. Zero-ing in f-k domain To suppress aliased energy Yilmaz, 1987

28. CMP gathers with strong multiples VM1= slow (water-bottom) multiples VM2= fast (peg-leg) multiples NMO corrected data using primary velocities Yilmaz, 1987

29. CMP stack using former gathers Yilmaz, 1987

30. Use of radon transformation  Velocity filter  Suppression of multiples  Interpolation of traces  Analysis of guided waves

31. Hyperbola maps onto an ellipse

32.  -p transformation for various arrivals

33. Yilmaz, 1987 (1/p) P1 and P2 are primaries W is water bottom which results in multiples

34. Dipping event in different domains Yilmaz, 1987 p(s/km) 1/181/1.5

35. Reducing source-generated noise in shallow seismic data using linear and hyperbolic  p transformations Roman Spitzer, Frank Nitsche and Alan G. Green

36. 2-D velocity model 48 receivers 5 m. interval Source location: 5 m from first geophone 3m depth

37. Shot gather (a)Raw shot gather (b)Time and offset varying gain (c) Spectral balancing (80-250 Hz)

38. Result of filtering Difference Between (a) And (c) Linear  -p transformation

39. Hyperbolic  -p transformation Amplitude of each sample is squared Hyperbolic  -p transformation Inverse hyperbolic  -p transformation

40. Shot gather along a high-resolution seismic line in northern Switzerland (a)Raw shot gather (b)Time and offset varying gain (c) Spectral balancing (80-250 Hz)

41. Linear  -p transformation Result of filtering Difference Between (c) And (c)

42. Hyperbolic  -p transformation Amplitude of each sample is squared Hyperbolic  -p transformation Inverse hyperbolic  -p transformation

43. Stacked sections Processing: CMP sorting NMO corrections NMO stretch mute Stacking Reflections were found to extend to shallower depths and more continuous