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Chapter 3: Wave-mode separation in the continuous wavelet

domain using combined translational and rotational data

Vertical component

P waves

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Current state of the art

2

Data components:

Hydrophone

Vertical

Radial

Transverse

​

“Coherent noise” /

“Source generated noise”

S-waves

Wave modes:

Surface waves

“ground roll”

“Vz noise”

P-waves

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Wave-mode selectivity using translational + rotational data

3

Data components:

Hydrophone

Vertical

Radial

Transverse

Yaw

Roll

Pitch

…

Wave modes:

​

P-waves

S-waves

Rayleigh

Love

….

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Agenda

4

  • Current coherent noise removal methods

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Agenda

5

  • Current coherent noise removal methods
  • Polarization template matching
    • Continuous Wavelet Transform (CWT)
    • Singular Value Decomposition (SVD)

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Agenda

6

  • Current coherent noise removal methods
  • Polarization template matching
    • Continuous Wavelet Transform (CWT)
    • Singular Value Decomposition (SVD)
  • Kettleman six-component survey

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Agenda

7

  • Current coherent noise removal methods
  • Polarization template matching
    • Continuous Wavelet Transform (CWT)
    • Singular Value Decomposition (SVD)
  • Kettleman six-component survey
  • Polarization-based wave mode separation

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Coherent Noise

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Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

  • Waves generated by the seismic source that do not contain information about the deep subsurface
  • Typically surface waves
  • Much stronger than the P-wave reflection data

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Ground roll / Rayleigh / Surface waves

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Ground roll / Rayleigh / Surface waves

10

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Data courtesy of Shell

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Ground roll / Rayleigh / Surface waves

11

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Data courtesy of Shell

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Ground roll suppression: Mute

12

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Data courtesy of Shell

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Ground roll suppression with F-K

13

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Receivers spaced at 2m intervals

Courtesy of Gustavo Alves

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Ground roll suppression with F-K

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Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Receivers spaced at 8m intervals

Courtesy of Gustavo Alves

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Ground roll suppression with polarization analysis

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Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

  • Multicomponent data (2C or 3C geophone)
  • Analysis of signal attributes of each multicomponent trace
  • Threshholding based on signal attributes to identify and separate noise from data

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Ground roll suppression with polarization analysis

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  • Multicomponent data (2C or 3C geophone)
  • Analysis of signal attributes of each multicomponent trace
  • Threshholding based on signal attributes to identify and separate noise from data

Not applicable to single component data

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Ground roll suppression with polarization analysis

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  • Multicomponent data (2C or 3C geophone)
  • Analysis of signal attributes of each multicomponent trace
  • Threshholding based on signal attributes to identify and separate noise from data

Can operate on spatially aliased data (3D surveys)

​

Insensitive to statics

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Ground roll suppression with polarization analysis

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  • Multicomponent data (2C or 3C geophone)
  • Analysis of signal attributes of each multicomponent trace
  • Threshholding based on signal attributes to identify and separate noise from data

Need to have some prior model of data or noise signal attributes

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Ground roll suppression with polarization analysis

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​

  • Vidale, 1986: Eigenvalue decomposition of covariance matrix of analytic signal
  • Kendall et al., 2005: First polarization vector from singular value decomposition of data covariance matrix after low-pass filter
  • de Meersman et al., 2006: Similar to Kendall et al., but with better noise weighting
  • Diallo et al. 2006: analytic signal of data after continuous wavelet transform

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Ground roll suppression with polarization analysis

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Diallo et al., 2006

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Frequency-dependent polarization template matching

21

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

  • Extract multiple attributes of input multicomponent signal
  • Insensitive to spatial aliasing

​

  • Frequency is an attribute
  • Data comprise translations and rotations
  • No prior model for noise. Noise model is learned directly from the data.

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Agenda

22

  • Current coherent noise removal methods
  • Polarization template matching
    • Continuous Wavelet Transform (CWT)
    • Singular Value Decomposition (SVD)
  • Kettleman six-component survey
  • Polarization-based wave mode separation

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Singular Value Decomposition (SVD)

23

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Singular Value Decomposition (SVD)

24

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Scaled singular vectors

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Singular Value Decomposition (SVD)

25

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Polarization vectors

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Singular Value Decomposition (SVD)

26

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Data1

​

Comp 1

Comp 2

​

Sample 1

1

0

​

Sample 2

-0.2

0

Data2

​

​

​

​

Sample 1

0

0.2

​

Sample 2

0

0.6

Data3

​

​

​

​

Sample 1

1

0.2

​

Sample 2

-0.2

0.6

Polarization vectors

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Singular Value Decomposition (SVD)

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Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Data1

​

Comp 1

Comp 2

​

Sample 1

1

0

​

Sample 2

-0.2

0

Data2

​

​

​

​

Sample 1

0

0.2

​

Sample 2

0

0.6

Data3

​

​

​

​

Sample 1

1

0.2

​

Sample 2

-0.2

0.6

Polarization vectors

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Singular Value Decomposition (SVD)

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Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Data1

​

Comp 1

Comp 2

​

Sample 1

1

0

​

Sample 2

-0.2

0

Data2

​

​

​

​

Sample 1

0

0.2

​

Sample 2

0

0.6

Data3

​

​

​

​

Sample 1

1

0.2

​

Sample 2

-0.2

0.6

Polarization vectors

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Continuous Wavelet Transform

29

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

data

mother wavelet (Morlet)

daughter wavelet

dilation

time delay

​

​

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Continuous Wavelet Transform

30

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

data

mother wavelet (Morlet)

daughter wavelet

dilation

time delay

​

​

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Continuous Wavelet Transform

31

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

64

16

32

8

2

4

1

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Agenda

32

  • Current coherent noise removal methods
  • Polarization template matching
    • Continuous Wavelet Transform (CWT)
    • Singular Value Decomposition (SVD)
  • Kettleman six-component survey
  • Polarization-based wave mode separation

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Chevron’s six-component Kettleman survey

33

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Chevron’s six-component Kettleman survey

34

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

  • 1 survey line
  • Maximum offset = 1600 m
  • 3C MEMS accelerometers at 2 m intervals
  • 3C electrokinetic rotation sensors
  • 4 different source types:
    1. Accelerated weight-drop
    2. Vibroseis
    3. Dynamite at depth = 25 m
    4. Dynamite at depth = 50 m

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35

Vertical

Radial

Transverse

Yaw

Roll

Pitch

Accelerated weight-drop source

​

Shot interval 6.25 m

Data courtesy of Chevron

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36

Vertical

Radial

Transverse

Yaw

Roll

Pitch

Vibroseis source

​

​

Shot interval 25 m

Data courtesy of Chevron

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37

Vertical

Radial

Transverse

Yaw

Roll

Pitch

Dynamite source at depth = 25 m

​

Shot interval 25 m

Data courtesy of Chevron

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38

Vertical

Radial

Transverse

Yaw

Roll

Pitch

Dynamite source at depth = 50 m

​

Shot interval 25 m

Data courtesy of Chevron

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Rotations from geophone differencing on a free surface

39

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Pitch

r-sensor

Pitch

geo-diff

Dynamite source

d

1

2

Data courtesy of Chevron

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Rotations from geophone differencing on a free surface

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Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Pitch

r-sensor

Pitch

geo-diff

Dynamite source

3 components:

Vertical

Radial

Pitch

Data courtesy of Chevron

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Vibroseis source after NMO

41

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Vertical

Radial

Pitch

Data courtesy of Chevron

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Dynamite source at depth=25m after NMO

42

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Vertical

Radial

Pitch

Data courtesy of Chevron

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Dynamite source at depth=50m after NMO

43

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Vertical

Radial

Pitch

Data courtesy of Chevron

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Objectives

44

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Vertical

Radial

Pitch

  • Separate undesired wave modes from all data components
  • Insensitive to spatial aliasing
  • Does not rely on an a priori model of the noise

Data courtesy of Chevron

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Attributes that distinguish between wave modes

45

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Vertical

Radial

Pitch

Data courtesy of Chevron

  • Rayleigh wave appearance on each component
  • Rayleigh wave frequency
  • Incorporate a time-frequency dependence into polarization analysis

​

  • Polarization in the continuous wavelet domain
  • Learn noise polarization from the data

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Agenda

46

  • Current coherent noise removal methods
  • Polarization template matching
    • Continuous Wavelet Transform (CWT)
    • Singular Value Decomposition (SVD)
  • Kettleman six-component survey
  • Polarization-based wave mode separation

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Dynamite source at 50 m depth before removal of shear-induced wave

47

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Vertical

Radial

Pitch

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Dynamite source at 50 m depth after removal of shear-induced wave

48

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Vertical

Radial

Pitch

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3C data in the continuous wavelet domain

49

Vertical

Radial

Pitch

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

CWT

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3C data in the continuous wavelet domain

50

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

component

frequency

time

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CWT polarization

51

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

P-wave CWT

S-wave CWT

Singular value decomposition

Scaled singular vectors =

polarizations

1st

3rd

2nd

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CWT polarization

52

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

S-wave CWT

Scaled singular vectors =

polarizations

1st

Vz

Vx

Ry

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CWT polarization

53

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

S-wave polarization

P-wave polarization

S-wave CWT

P-wave CWT

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Polarization template

54

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

S-wave polarization template

Vz

Vx

Ry

1

0

weight

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Polarization template

55

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Some other arrival’s polarization

Vz

Vx

Ry

1

0

weight

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Weighting function

56

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

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Continuous wavelet domain polarization filtering flow

57

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

CWT

SVD

CWT+SVD

CWT-1

polarization template

Weighting function

58 of 107

Dynamite source at 50 m depth before removal of shear-induced wave

58

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Vertical

Radial

Pitch

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Dynamite source at 50 m depth after removal of shear-induced wave

59

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Vertical

Radial

Pitch

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Polarization filter vs frequency filter

60

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Input data

Polarization filter

High-pass filter

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Dynamite source at 25 m depth before removal of shear-induced wave

61

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Vertical

Radial

Pitch

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Dynamite source at 25 m depth after removal of shear-induced wave

62

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Vertical

Radial

Pitch

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Vibroseis source before removal of Rayleigh waves

63

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Vertical

Radial

Pitch

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Vibroseis source after removal of Rayleigh waves

64

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Vertical

Radial

Pitch

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Vibroseis source before removal of Rayleigh waves

65

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Vertical

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Vibroseis source after removal of Rayleigh waves

66

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Vertical

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Summary

67

  • Frequency-dependent polarization filtering using CWT and SVD
  • Matching wavelet-domain polarization template of an undesired wave-mode
  • Multicomponent translational + rotational field data
  • No spatial sampling requirement
  • Assumption of stationarity of undesired wave-mode

​

68 of 107

Summary

68

  • Frequency-dependent polarization filtering using CWT and SVD
  • Matching wavelet-domain polarization template of an undesired wave-mode
  • Multicomponent translational + rotational field data
  • No spatial sampling requirement
  • Assumption of stationarity of undesired wave-mode

Chapter 4: Automatic wave-mode identification with machine learning

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Machine-learning classification at station 337

Manual wave-mode identification / Support Vector Machines / Automatic wave-mode identification

69

Training: Class 1=

Slow ground roll

Training: Class 1=

Fast ground roll

Training: Class 1=

Slow+fast ground roll

Class 1

Class 0

Class 1

Class 0

Class 1

Class 0

Data courtesy of Chevron

70 of 107

Acknowledgements

  • Fred Herkenhoff and Ranjan Dash (Chevron)
  • Robert Brune (Geokinetics)
  • Everhard Muyzert (Schlumberger)

71 of 107

Spare slides

71

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Comparison of weighting function with different components

72

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

C1

C2

C3

Cone

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Comparison of weighting function with different components

73

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Six components in Kettleman dataset:

  • Vertical
  • Radial
  • Transverse
  • Yaw
  • Roll
  • Pitch

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Comparison of weighting function with different components

74

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Compare filtering using:

Vertical

Radial

Transverse

+ Pitch

Vertical

Radial

Transverse

vs

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Comparison of weighting function with different components

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Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

C1

C2

C3

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Comparison of weighting function with different components

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Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

C3

C1

C2

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Comparison of weighting function with different components

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C1

C2

C3

Slice

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Comparison of weighting function with different components

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Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

C1

C2

C3

Wedge

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Comparison of weighting function with different components

79

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

C1

C2

C3

Cone

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Comparison of weighting function with different components

80

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

C1

C2

Cone

C4

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Comparison of weighting function with different components

81

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Compare filtering using:

Vertical

Radial

Pitch

Vertical

Radial

Transverse

vs

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Transverse vs Pitch component, dynamite 25 m

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Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Vertical

Radial

Transverse

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Transverse vs Pitch component, dynamite 25 m

83

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Vertical

Radial

Pitch

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Before removal of shear-induced wave, dynamite 25 m

84

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Vertical

Radial

Transverse

Pitch

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After removal of shear-induced wave with vertical, radial, transverse

85

Transverse

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Vertical

Radial

Pitch

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After removal of shear-induced wave with vertical, radial, pitch

86

Pitch

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Vertical

Radial

Transverse

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Before removal of slow ground roll, vibroseis

87

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Vertical

Radial

Transverse

Pitch

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After removal of slow ground roll with vertical, radial, transverse

88

Transverse

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Vertical

Radial

Pitch

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After removal of slow ground roll with vertical, radial, pitch

89

Pitch

Transverse

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Vertical

Radial

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Before removal of shear-induced wave, dynamite 50 m

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Vertical

Radial

Transverse

Pitch

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After removal of shear-induced wave with vertical, radial, transverse

91

Transverse

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Vertical

Radial

Pitch

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After removal of shear-induced wave with vertical, radial, pitch

92

Transverse

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Vertical

Radial

Pitch

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Ground roll suppression with Stack-Array

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(Morse and Hildebrandt, 1989)

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Ground roll suppression with Stack-Array

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(Morse and Hildebrandt, 1989)

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Ground roll suppression with Stack-Array

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(Morse and Hildebrandt, 1989)

  • Topography (Statics correction)
  • Assumption of surface wave velocity
  • Large scale 3D acquisition?
  • Acquisition design focused on noise suppression

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Vz noise in ocean bottom node data

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Upgoing and downgoing wavefields

97

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(M. Wong, 2014)

Upgoing

Downgoing

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Upgoing and downgoing wavefields

98

Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

(M. Wong, 2014)

Upgoing

Downgoing

PZ summation:

Combine Hydrophone and vertical geophone to separate upgoing data from downgoing data

99 of 107

Vz noise in ocean bottom node data

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Hydrophone

Vertical with Vz noise

(Zhou et al., 2011)

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Vz noise in ocean bottom node data

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Hydrophone

Vertical with Vz noise

(Zhou et al., 2011)

P wave

S wave

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Vz noise suppression with match filtering

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Hydrophone

Vertical with Vz noise

(Zhou et al., 2011)

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Vz noise suppression with match filtering

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Hydrophone

Vertical with Vz noise

Vertical after Vz noise suppression

(Zhou et al., 2011)

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Vz noise suppression with match filtering

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Introduction / Polarization, CWT, SVD / Kettleman 6C survey / Polarization-based wave-mode separation

Hydrophone

(Zhou et al., 2011)

  • Extension of the data dimensions to a domain where noise is separable from data

​

  • Requires an input model of “clean” data
  • Spatial transforms that rely on sufficient spatial sampling of the wavefield

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Ground roll suppression with adaptive subtraction

104

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(Edme et al., 2014)

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Ground roll suppression with adaptive subtraction

105

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(Edme et al., 2014)

  • No spatial sampling requirements

​

  • Requires a model of the noise
  • Assumption that noise appears only on particular data components

106 of 107

106

“FD Pressure” Idea

-Put the FD Stencil in the Earth-

(Courtesy of Fred Herkenhoff)

107 of 107

Thanks for listening

1C

3C

6C

email: ohad@sep.stanford.edu