Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time •...
Transcript of Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time •...
![Page 1: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/1.jpg)
Drift time estimation by dynamic time warping
Tianci Cui and Gary F. Margrave
1
![Page 2: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/2.jpg)
Outline
• Drift time
• Well‐based 1D seismogram models
• Matching stationary and nonstationary seismograms
• Dynamic time warping
• Inclusion of internal multiples
• Conclusions and future work
2
![Page 3: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/3.jpg)
Drift time estimation by DTW
• Drift time
• Well‐based 1D seismogram models
• Matching stationary and nonstationary seismograms
• Dynamic time warping
• Inclusion of internal multiples
• Conclusions and future work
3
![Page 4: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/4.jpg)
Drift time: well logs
4
![Page 5: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/5.jpg)
Drift time: fake Q log
: Well logging frequency : Seismic frequency
Kjartanssen (1979)
5
![Page 6: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/6.jpg)
Drift time: frequency‐dependent velocity
: Well logging frequency : Seismic frequency
Kjartanssen (1979)
6
![Page 7: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/7.jpg)
Drift time
7
![Page 8: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/8.jpg)
Drift time estimation by DTW
• Drift time
• Well‐based 1D seismogram models
• Matching stationary and nonstationary seismograms
• Dynamic time warping
• Inclusion of internal multiples
• Conclusions and future work
8
![Page 9: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/9.jpg)
Stationary seismogram: s(t)
=
minimum‐phasedominant frequency: 30 Hz
9
![Page 10: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/10.jpg)
Nonstationary seismogram: sq(t)Synthetic zero‐offset VSP model with Q effects
(Margrave and Daley, 2014)
10
![Page 11: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/11.jpg)
Nonstationary seismogram: sq(t)Synthetic zero‐offset VSP model with Q effects
(Margrave and Daley, 2014)Surface receiver
11
![Page 12: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/12.jpg)
1 D seismogram models
Q effects: 1 Diminishing amplitude2 Widening wavelets3 Delaying events Drift time
12
![Page 13: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/13.jpg)
1 D seismogram models
13
![Page 14: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/14.jpg)
Drift time estimation by DTW
• Drift time
• Well‐based 1D seismogram models
• Matching stationary and nonstationary seismograms
• Dynamic time warping
• Inclusion of internal multiples
• Conclusions and future work
14
![Page 15: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/15.jpg)
Matching without drift time correctionTime‐variant balancing and
time‐variant constant‐phase rotation
15
![Page 16: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/16.jpg)
Matching with theoretical drift time correction
: drift time: stationary seismogram after drift time correction
Drift time correction
16
![Page 17: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/17.jpg)
Matching perfection: time‐variant balancing and time‐variant constant‐phase rotation
Matching with theoretical drift time correction
17
![Page 18: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/18.jpg)
Matching perfection: time‐variant balancing and time‐variant constant‐phase rotation
Matching with theoretical drift time correction
Drift time correction is necessary to match the stationaryto nonstationary seismograms.
Calculation of drift time in industrial practice needs oneof these:
• Knowledge of Q or
• A check‐shot survey or
• Manually stretching and squeezing the syntheticseismogram
18
![Page 19: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/19.jpg)
Drift time estimation by DTW
• Drift time
• Well‐based 1D seismogram models
• Matching stationary and nonstationary seismograms
• Dynamic time warping
• Inclusion of internal multiples
• Conclusions and future work
19
![Page 20: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/20.jpg)
Dynamic Time Warping
Dynamic time warping (Hale, 2012):• Estimates the time shift between two seismograms• Based on constrained optimization algorithm• Realized by dynamic programming• Similar to time‐variant crosscorrelation but more
sensitive to the rapid‐varying time shift
We use dynamic time warping (DTW) to estimate thedrift time between the stationary and nonstationaryseismograms caused by anelastic attenuation
20
![Page 21: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/21.jpg)
DTW: drift time estimation
21
![Page 22: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/22.jpg)
DTW: drift time estimation
22
![Page 23: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/23.jpg)
DTW: drift time estimation
,
23
![Page 24: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/24.jpg)
DTW: drift time estimation
,
24
![Page 25: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/25.jpg)
DTW: drift time estimation
, ,
25
![Page 26: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/26.jpg)
DTW: drift time estimation
, ,
: sample number, : time sample rate: drift time, : drift lag
theoretical drift lag
26
![Page 27: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/27.jpg)
DTW: drift time estimationtheoretical drift lag
• The alignment error is nearly zero along the theoretical drift lag.
• Choose a path traveling from to , sum alignmenterrors along this path. The estimated drift lag sequence is thepath of the minimal cumulative alignment error.
27
![Page 28: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/28.jpg)
DTW: drift time estimationtheoretical drift lag
infinite
Constraint: | |≤1
28
![Page 29: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/29.jpg)
Dynamic Programming
4 7 1
3 5 8
6 2 9
Alignment error array
1
0
‐1
1 2 3n
m
27 possible paths
29
![Page 30: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/30.jpg)
Dynamic Programming: accumulation
4 7 1
3 5 8
6 2 9
Alignment error array
1
0
‐1
1 2 3n
m
Cumulative alignment error array
1
0
‐1
m
n1 2 3
27 possible paths
30
![Page 31: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/31.jpg)
Dynamic Programming: accumulation
Constraint: | |≤1
4 7 1
3 5 8
6 2 9
4
3
6
Alignment error array
1
0
‐1
1 2 3n
m
Cumulative alignment error array
1
0
‐1
m
n1 2 3
27 possible paths
?
31
![Page 32: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/32.jpg)
Dynamic Programming: accumulation
Constraint: | |≤1
4 7 1
3 5 8
6 2 9
4
3
6
Alignment error array
1
0
‐1
1 2 3n
m
Cumulative alignment error array
1
0
‐1
m
n1 2 3
27 possible paths
?
32
![Page 33: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/33.jpg)
Dynamic Programming: accumulation
Constraint: | |≤1
4 7 1
3 5 8
6 2 9
4
3
6
Alignment error array
1
0
‐1
1 2 3n
m
Cumulative alignment error array
1
0
‐1
m
n1 2 3
27 possible paths
?
33
![Page 34: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/34.jpg)
Dynamic Programming: accumulation
Constraint: | |≤1
4 7 1
3 5 8
6 2 9
4 10
3
6
Alignment error array
1
0
‐1
1 2 3n
m
Cumulative alignment error array
1
0
‐1
m
n1 2 3
27 possible paths
34
![Page 35: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/35.jpg)
Dynamic Programming: accumulation
Constraint: | |≤1
4 7 1
3 5 8
6 2 9
4 10
3
6
Alignment error array
1
0
‐1
1 2 3n
m
Cumulative alignment error array
1
0
‐1
m
n1 2 3
27 possible paths
?
35
![Page 36: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/36.jpg)
Dynamic Programming: accumulation
Constraint: | |≤1
4 7 1
3 5 8
6 2 9
4 10
3
6
Alignment error array
1
0
‐1
1 2 3n
m
Cumulative alignment error array
1
0
‐1
m
n1 2 3
27 possible paths
?
36
![Page 37: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/37.jpg)
Dynamic Programming: accumulation
Constraint: | |≤1
4 7 1
3 5 8
6 2 9
4 10
3 8
6
Alignment error array
1
0
‐1
1 2 3n
m
Cumulative alignment error array
1
0
‐1
m
n1 2 3
27 possible paths
37
![Page 38: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/38.jpg)
Dynamic Programming: accumulation
Constraint: | |≤1
4 7 1
3 5 8
6 2 9
4 10
3 8
6 5
Alignment error array
1
0
‐1
1 2 3n
m
Cumulative alignment error array
1
0
‐1
m
n1 2 3
27 possible paths
38
![Page 39: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/39.jpg)
Dynamic Programming: accumulation
Constraint: | |≤1
4 7 1
3 5 8
6 2 9
4 10 9
3 8 13
6 5 14
Alignment error array
1
0
‐1
1 2 3n
m
Cumulative alignment error array
1
0
‐1
m
n1 2 3
27 possible paths 3 possible paths
39
![Page 40: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/40.jpg)
Dynamic Programming: backtracking
Constraint: | |≤1
4 7 1
3 5 8
6 2 9
4 10 9
3 8 13
6 5 14
Alignment error array
1
0
‐1
1 2 3n
m
Cumulative alignment error array
1
0
‐1
m
n1 2 3
27 possible paths 3 possible paths
40
![Page 41: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/41.jpg)
Dynamic Programming: backtracking
Constraint: | |≤1
4 7 1
3 5 8
6 2 9
4 10 9
3 8 13
6 5 14
Alignment error array
1
0
‐1
1 2 3n
m
Cumulative alignment error array
1
0
‐1
m
n1 2 3
27 possible paths 3 possible paths
Estimated drift lag:
41
![Page 42: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/42.jpg)
Dynamic Programming
4 10 9
3 8 13
6 5 14
1
0
‐1
1 2 3n
m
Cumulative alignment error array
1
0
‐1
m
n1 2 3
, ,
Cumulative alignment error array
4 10
3 8
6 5
,
Dynamic: optimal solution varies at different stageWarping path: drift lag
42
![Page 43: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/43.jpg)
DTW: drift time estimationestimated drift lag
infinite
Constraint: | |≤1Further Constraint: ∑ ≤1
The drift lag sequence is constrained to change in blocks of samples
( )
43
![Page 44: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/44.jpg)
DTW: drift time estimationestimated drift lag
infinite
Constraint: | |≤1Further Constraint: ∑ ≤1
The drift lag sequence is constrained to change in blocks of samples
( )
44
![Page 45: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/45.jpg)
DTW: drift time estimation∗
: estimated drift lag: estimated drift time
45
![Page 46: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/46.jpg)
DTW: matching seismograms
46
![Page 47: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/47.jpg)
Drift time estimation by DTW
• Drift time
• Well‐based 1D seismogram models
• Matching stationary and nonstationary seismograms
• Dynamic time warping
• Inclusion of internal multiples
• Conclusions and future work
47
![Page 48: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/48.jpg)
Inclusion of internal multiplessq(t)
sqi(t)
48
![Page 49: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/49.jpg)
Inclusion of internal multiples: sqi(t)
=(Richards and Menke, 1983)
49
![Page 50: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/50.jpg)
Inclusion of internal multiples
50
![Page 51: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/51.jpg)
Conclusions
• DTW succeeds in estimating drift time automaticallywithout knowledge of Q or a check‐shot survey.
• Application of drift time correction results in a muchsimpler residual phase.
• DTW estimates drift time associated with apparent Qincluding both intrinsic and stratigraphic effects.
51
![Page 52: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/52.jpg)
Future work
• Conduct stationary and nonstationary deconvolution onthe seismic trace and tie the deconvolved seismic traceto well log reflectivity by DTW
• Estimate Q value from the drift time estimated by DTW
52
![Page 53: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/53.jpg)
Acknowledgements
• CREWES sponsors
• NSERC: grant CRDPJ 379744‐08
• CREWES staff and students
53
![Page 54: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/54.jpg)
Choosing values
54
![Page 55: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/55.jpg)
Choosing values
55
![Page 56: Drift time estimation by dynamic time warping...Drift time estimation by DTW • Drift time • Well‐based 1D seismogram models • Matching stationary and nonstationary seismograms](https://reader033.fdocuments.in/reader033/viewer/2022052816/60a7f10a99712d317809d597/html5/thumbnails/56.jpg)
Applications of DTW
• Tying synthetic to recorded seismograms
• Registration of P– and S–wave images
• Residual normal moveout correction
• Alignment of images computed for differentsource‐receiver offsets or propagation angles.
56