sismo can we use the spectral ridges to estimate q ? marcílio castro de matos...
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SISMOwww.sismo.srv.br
Can we use the spectral ridges to estimate Q ?
Marcílio Castro de Matos
[email protected] www.matos.eng.br
1
Attribute-Assisted Seismic Processing and Interpretation
http://geology.ou.edu/aaspi/ Signal Processing Research, Training & Consultingwww.sismo.srv.br
Summary
• Spectral ridges• Q estimation• Examples• Conclusions
SyntheticReflectivity CWT Magnitude
CWTmagnitude
0
pos
CWT MML
Spectral ridges
Introduction
Continuous Wavelet Transform (very brief review)
ICWTdec
Examples
Conclusions
3
Tim
e F
requ
ency
Reflectivity
r(t)
Source wavelet
s(t)
Noise
n(t)
Seismic data
u(t)* +
Bandlimited white spectrum
Modified from Kurt Marfurt course(Partyka et al, 1999)
Long window spectral
decomposition and the
convolutional model
White spectrum
Colored spectrum
Tim
e F
requ
ency
Reflectivity
r(t)
Source wavelet
s(t)
Noise
n(t)
Seismic data
u(t)* +
Bandlimited colored spectrum
Short window spectral
decomposition and the
convolutional model
Modified from Kurt Marfurt course(Partyka et al, 1999)
1822 Fourier book
From: http://books.google.com/
TeFtf
k
tjkkT
2.)( 0
0
Tt
t
tjkk dtetfT
F0
0
0 .).(1
Animated plot of the first five successive partial Fourier series. From wikipedia.org
f t t
(Yilmaz, 2001)
Seismic zero phase wavelet
Summation of co-sinusoids with zero phase
f t t
8
Short Time Fourier Transform – STFT
The simplest time-frequency representation
duetuhuxhtF ujx
2*;,
f t t
9
Short Time Fourier Transform – STFTAmplitude and Phase spectrum
tim
e
20 40 60 80 100 120 140 160 180 200 220
2000
2200
2400
2600
2800
3000
3200-2
-1
0
1
2
x 104
frequency
tim
e
0 50 100 150 200
2000
2200
2400
2600
2800
3000
3200
Time-frequency pattern???
f t t
Spectral ridges
Introduction
Continuous Wavelet Transform (very brief review)
ICWTdec
Examples
Conclusions
11
Continuous Wavelet Transform
0 1 2 3 4 5
-1
-0.5
0
0.5
1
1.5
2
2.5
3
tempo
Am
plitu
de
0.02 0.04 0.06 0.08 0.1 0.12 0.14
0
2
4
6
8
10
12
14
16
18
frequencia normalizada (x )
Am
plit
ude
Cdd0 2
0
2 ˆˆ
0
dtt
s
ut
stsu 1
, 00ˆ
dtt
-5 -4 -3 -2 -1 0 1 2 3 4 5-0.4
-0.2
0
0.2
0.4
0.6
0.8
1Wavelet Chapeu Mexicano
tempo
Am
plitu
de
(x) L2() is called a wavelet
100 200 300 400 500 600 700 800 900 1000
-0.2
0
0.2
0.4
Amostras
Am
plitu
de
Amostras
Esc
ala
100 200 300 400 500 600 700 800 900 1000
10
20
30
40
50
60
Continuous Wavelet Transform (CWT)
time
Amplitude
( ),
1( , ) ( , ) , ( )x u s
t uCWT u s Wf u s f f t dt
ss
1
( , ) ( ) s
t uWf u s f t dt f u
ss
s
t
sts 1
ˆ ˆs s s The CWT can be interpreted
as a band pass filter response at each scale s
Sca
les
Time (ms)
Time (ms)
f t t
Grossmann and Morlet introduced CWT formally
in 1984
Inverse CWT
SyntheticReflectivity CWT Magnitude Voices ICWT
CWTmagnitude
0
pos
Σ
Summary
Introduction
Continuous Wavelet Transform (very brief review)
Pseudo deconvolution (icwtdec)
Examples
Conclusions
16
Math behind…
100 200 300 400 500 600 700 800 900 1000
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
amostras
Am
plitude
20
40
60
80
100
120
Abs. and by scale Values of Ca,b Coefficients for a = 1 2 3 4 5 ...
time (or space) b
scal
es a
100 200 300 400 500 600 700 800 900 1000 1 5 913172125293337414549535761
100 200 300 400 500 600 700 800 900 1 5 913172125293337414549535761
Local Maxima Lines
Singularities detection and characterization through Continuous Wavelet Transform (CWT): Lipschitz (Hölder) Coefficients
10 20 30 40 50 60
1
2
3
s
|Wf(
u,s)
|
0 1 2 3 4 5 6
-1
-0.5
0
0.5
1
1.5
log2(s)lo
g2(|W
f(u,
s)|)
+1/2=1/2
sAsuWf 222 log2
1log,log
CWT modulus maxima line
SyntheticReflectivity CWT Magnitude
CWTmagnitude
0
pos
CWT MML
WTMMLA seismic applications
ICWT “deconvolution” workflow
SyntheticReflectivity CWT Magnitude
CWTmagnitude
0
pos
CWT MML
CWTMorlet
CWT MML
ICWTShrunken
Morlet
CWT MML voices
Σ
Relative acoustic impedance from ICWTDEC
WORKFLOW
- Re-scale seismic trace: |s(t)|<<1- Integration filter (Peacock, 1979) - High-pass filter
Summary
Introduction
Continuous Wavelet Transform (very brief review)
ICWTdec
Examples
Conclusions
24
Case 1: Synthetic seismic channel
10 ms thickness trace
Case 1: Synthetic seismic channel
30 ms thickness trace
Synthetic channel and its ICWTdec
ICWTDEC RAI
Adding random noise
Case 2: Barnet Shale
Original Seismic
ICWTdec
THINMAN™
Marble Falls
Upper Barnett Lm
Upper Barnett Sh
Forestburg
Lower Barnett Sh
Viola
ICWTdec
60Amplitude
60
0
Marble Falls
Upper Barnett Lm
Upper Barnett Sh
Forestburg
Lower Barnett Sh
Viola
THINMAN™
10Amplitude
10
0
Case 3: Pre-stack (Hampson&Russel 2D demo data set)
Twt (s)
0
0.7
Offset
ICWTdec
Twt (s)
0
0.7
Offset
RAI
Twt (s)
0
0.7
Offset
Spectral ridges Conclusions
• CWT spectral decomposition filtering process described dear generates high resolution events that correlate to major acoustic impedance changes.
• Since this broadening is a trace-by-trace independent process, laterally-consistent thin bed terminations and other truncations can be interpreted with confidence.
Q estimation
• Anelasticity and wave propagation “very brief” review• Q estimation and spectral ridges• Conclusions
Anelasticity
Berea Sandstone
Wyllie, et al, 1958
From Carl Sondergeld Rock Physics Course Notes, 2009
Anelasticity review
From Carl Sondergeld Rock Physics Course Notes, 2009
Wave equation
From Carl Sondergeld Rock Physics Course Notes, 2009
Attenuation per wavelength
From Carl Sondergeld Rock Physics Course Notes, 2009
Normal incidence anelastic reflections
From Carl Sondergeld Rock Physics Course Notes, 2009
Seismic wave behavior in absorptive media defined by v, ρ and Q.
Figure 2.4 of Seismic Absorption Estimation and Compensation by Changjun Zhang M.Sc., The University of British Columbia, 2009
• Q is inversely proportional to attenuation. The greater the Q value, the smaller the loss or attenuation!
• The phase lag Ψ is a direct measure of attenuation. The greater the phase lag, the greater the attenuation.
• Q for rock lies in the range of 10 to 200.• If Q = Q(ω) then M must also be a function of frequency!• Moduli must depend upon frequency!
Q estimation
• Anelasticity and wave propagation “very brief” review• Q estimation and spectral ridges• Conclusions
Q estimation from spectral ratio
freqAdrinal Ilyas, 2010, Estimation of q factor from seismic reflection data, MsC Curtin University
Chopra, Alexeev, and Sudhakar, TLE 2003, High-frequency restoration of surface seismic data
Q estimation from spectral ratio
• Synthetic
• Reflectivity
• CWT Magnitude
• CWT• magnitude
• 0
• pos
• CWT MML
Spectral ridges can guide Q estimation from spectral ratio
In Q computation, we need to compute the amplitude spectra of two adjacent events (Taner, 2000)
Q estimation from Peak Frequency variation
Ricker wavelet
Zhang & Ulrych, 2002, Geophysics, Estimation of Quality factors from CMP records
Q estimation from Peak Frequency variation
Zhang & Ulrych, 2002, Geophysics, Estimation of Quality factors from CMP records
Q estimation from Peak Frequency variation
Zhang_Ulrych_2007_Geophysics_Seismic absorption compensation A least squares inverse scheme
Frequency decay caused by thin-bed tuning and absorption
Figure 4.2 of Seismic Absorption Estimation and Compensation by Changjun Zhang M.Sc., The University of British Columbia, 2009
Absorption and specdecomp phase components
SyntheticReflectivity CWT Magnitude
CWTmagnitude
0
pos
CWT MML
180
-180
CWT phase
10 70
Frequency (Hz)
CWT Magnitude and Phase overlaid by spectral ridgesThe phase spectra will provide information for dispersion estimation. Attributes picked at the peak of the envelope represent the average of the wavelet attribute. That is why we pick the amplitude spectrum at the time of envelop peak for Q computation. Phase spectra is picked the same way. If we look at the phase spectra, we observe that most of the spectra of the events are horizontal, which means that these wavelets are zero phase, and their rotation angle is the phase corresponding to the envelop peak. Therefore, computation of dispersion consists of determining the phase differences at each sub-band trace and compute an average phase delay per cycle per second. Since dispersion is related to absorption, higher levels of dispersion will point to higher levels of absorption, which may indicate fracture in carbonates or unconsolidated snads in clastic environment. (Taner, 2000).
Conclusions
• CWT spectral decomposition filtering process described dear generates high resolution events that correlate to major acoustic impedance changes.
• It seems we can correlate spectral ridges with Q estimation
Acknowledgements
Attribute-Assisted Seismic Processing and Interpretation
http://geology.ou.edu/aaspi/
[email protected] www.matos.eng.br
Thank you for your attention!!!
Thanks to DEVON for providing a license to one of the seismic data volume used herein.
Thanks to Carl Sondergeld
Thanks to Roderick Perez from OU for his Barnet shale interpretation.
Thanks also to PETROBRAS Reservoir Geophysics Management friends for their comments.
Signal Processing Research, Training & Consultingwww.sismo.srv.br