interferometric prediction and least squares subtraction of surface waves
DESCRIPTION
Interferometric Prediction and Least Squares Subtraction of Surface Waves. Shuqian Dong and Ruiqing He University of Utah. Land Field Data Test. OUTLINE. Motivation: Surface Wave Filtering. Interfer. Surface Wave Theory. Conclusions. Land Field Data Test. OUTLINE. - PowerPoint PPT PresentationTRANSCRIPT
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Interferometric Prediction and Interferometric Prediction and Least Squares Subtraction of Least Squares Subtraction of
Surface WavesSurface Waves
Shuqian Dong and Ruiqing HeShuqian Dong and Ruiqing He
University of UtahUniversity of Utah
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OUTLINEOUTLINE
Motivation: Surface Wave FilteringMotivation: Surface Wave Filtering
Interfer. Surface Wave TheoryInterfer. Surface Wave Theory
ConclusionsConclusions
Land Field Data TestLand Field Data Test
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OUTLINEOUTLINE
Motivation: Surface Wave FilteringMotivation: Surface Wave Filtering
Interfer. Surface Wave TheoryInterfer. Surface Wave Theory
ConclusionsConclusions
Land Field Data TestLand Field Data Test
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MotivationMotivation
Problem:Problem:
Surface waves = strong coherent Surface waves = strong coherent
noise blurs seismogramnoise blurs seismogram. . Moveout-Moveout-based filtering not always effective for based filtering not always effective for dispersive waves.dispersive waves.
Solution:Solution:
Interfer. Predict. + Least Squares Interfer. Predict. + Least Squares Subtraction. Accounts for dispersion.Subtraction. Accounts for dispersion.
Offset (m)Offset (m)T
ime
(s)
Tim
e (s
)00
1.01.0
00
72007200
A CSG with Strong Surface WavesA CSG with Strong Surface Waves
Offset (m)Offset (m)T
ime
(s)
Tim
e (s
)00
1.01.0
00
72007200
A CSG with Strong Surface WavesA CSG with Strong Surface Waves
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OUTLINEOUTLINE
Motivation: Surface Wave FilteringMotivation: Surface Wave Filtering
Interfer. Surface Wave TheoryInterfer. Surface Wave Theory
ConclusionsConclusions
Land Field Data TestLand Field Data Test
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Prediction of multiples by convolution (SRME)Prediction of multiples by convolution (SRME)
Prediction of Primaries by Crosscorrelation (Interferometry)Prediction of Primaries by Crosscorrelation (Interferometry)
AA BB CC AA BB
**⊕⊕
BB CC
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u (s,g’)= A(s,g’)u (s,g’)= A(s,g’)eeikxikxsg’sg’
u (s,g)= A(s,g)u (s,g)= A(s,g) eeikxikxsgsg
u (s,g)u (s,g) u (s,g’)u (s,g’)
gg g’g’
u(g,g’)u(g,g’)u(g,g’)u(g,g’)
⊕⊕== u (s,g)u (s,g)u (s,g’)u (s,g’) **
eeik(xik(xSg’Sg’==A(s,g)A(s,g) A(s,g’)A(s,g’)
-x-xsgsg))
Predict Surface Waves by CrosscorrelationPredict Surface Waves by Crosscorrelation
⊕⊕g’g’SS
gg
} xxgg’gg’
ττ
ττ
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A B C A B⊕⊕
B C⊕⊕
B C
++A’ B C A’ B
Predict Surface Waves by CrosscorrelationPredict Surface Waves by Crosscorrelation
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Coherent Stacking: surface waves (all src pts = stationary)Coherent Stacking: surface waves (all src pts = stationary)
SSgg g’g’
11
gg g’g’
SS22SSNN ……
Incoherent Stacking: primariesIncoherent Stacking: primaries
Coherent Stacking: FS Multiples?Coherent Stacking: FS Multiples? Avoid stationary source pointsAvoid stationary source points
⊕⊕
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Surface Waves PredictionSurface Waves PredictionOffset (m)Offset (m)
Tim
e (s
)T
ime
(s)
00
2.02.0
00 36003600
Original DataOriginal Data
3600360000
Tim
e (s
)T
ime
(s)
00
2.02.0
Offset (m)Offset (m)
Predcted Surface WavesPredcted Surface Waves
00 Time (s)Time (s) 2.02.0
Am
pli
tud
eA
mp
litu
de
-1-1
11
00
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Least Square Matching FilterLeast Square Matching Filter
dd (t)(t)Refl.Refl. Surf.Surf.
== dd (t)(t) dd (t)(t)++
Pred.Pred.
≈≈dd (t)(t) ** ff (t)(t)dd (t)(t)Refl.Refl.
dd (t)(t)--
-- **ff (t)(t) ==
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Surface Waves Filtering ResultsSurface Waves Filtering ResultsT
ime
(s)
Tim
e (s
)
00
2.02.0Offset (m)Offset (m)00 72007200 Offset (m)Offset (m)00 72007200
Tim
e (s
)T
ime
(s)
00
2.02.0
Original DataOriginal Data Filtered DataFiltered Data
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Tim
e (s
)T
ime
(s)
00
2.02.0
Tim
e (s
)T
ime
(s)
00
2.02.0Offset (m)Offset (m)00 72007200 Offset (m)Offset (m)00 72007200
Results of f-k methodResults of f-k method Results of interferometric methodResults of interferometric method
Result ComparisonResult Comparison
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ConclusionsConclusions
Preliminary results promising for interfer. Preliminary results promising for interfer. Prediction + subtraction surface waves.Prediction + subtraction surface waves.
Future work: iterative prediction + Future work: iterative prediction + subtraction.subtraction.
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Can Interferometric Prediction+Subtraction Can Interferometric Prediction+Subtraction work for Irregular 3D Arrays? work for Irregular 3D Arrays?
Answer?:Answer?:Irregular S. Calif. Earthquake ArrayIrregular S. Calif. Earthquake Array
StationsStations
(Andrew Curtis, The Leading Edge, 2006)(Andrew Curtis, The Leading Edge, 2006)
Predicted SurfacePredicted SurfaceWavesWaves
Sta
tio
n O
ffs
et
(km
)S
tati
on
Off
se
t (k
m)
Time (s)Time (s)LongitudeLongitude120 W120 W 115 W115 W
32
N3
2 N
La
titu
de
La
titu
de
38
N3
8 N
Predicted Surface WavesPredicted Surface Waves
-200-200 200200
004
00400
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AcknowledgementsAcknowledgements
We thank the UTAM sponsors for the We thank the UTAM sponsors for the support of the research.support of the research.
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ThanksThanks