multisource least-squares reverse time migration
DESCRIPTION
Multisource Least-squares Reverse Time Migration. Wei Dai. Outline. Introduction and Overview Chapter 2: Multisource least-squares reverse time migration Chapter 3: Frequency-selection encoding LSRTM Chapter 4: Super-virtual inteferometric diffractions Summary. - PowerPoint PPT PresentationTRANSCRIPT
Multisource Least-squares Reverse Time Migration
Wei Dai
Outline• Introduction and Overview
• Chapter 2: Multisource least-squares reverse time
migration
• Chapter 3: Frequency-selection encoding LSRTM
• Chapter 4: Super-virtual inteferometric diffractions
• Summary
Introduction: Least-squares Migration
• Seismic migration: Given: Observed data
modelling operator
Goal: find a reflectivity model to explain by solving
the equation
Direct solution: expensive
Conventional migration:
Iterative solution:
Migration velocity
0 X (km) 60 X (km) 6
30
Z (k
m)
• Problems in conventional migration image
Introduction: Motivation for LSM
migration artifacts
imbalanced amplitude
• Least-squares migration has been shown to
produce high quality images, but it is considered
too expensive for practical imaging.
• Solution: combine multisource technique and
least-squares migration (MLSM).
Problem of LSM
Motivation for Multisource
Multisource LSMTo: Increase efficiency Remove artifacts Suppress crosstalk
• Problem: LSM is too slow
• Solution: multisource phase-encoding techniqueMany (i.e. 20) times slower than standard migration
Multisource Migration Image
Multisource Crosstalk
Overview• Chapter 2 : Multisource least-squares reverse time
migration is implemented with random time-shift and
source-polarity encoding functions.
• Chapter 3: Multisource LSRTM is implemented with
frequency-selection encoding for marine data.
• Chapter 4: An interferometric method is proposed to
extract diffractions from seismic data and enhance its
signal-to-noise ratio.
Outline• Introduction and Overview
• Chapter 2: Multisource least-squares reverse time
migration
• Chapter 3: Frequency-selection encoding LSRTM
• Chapter 4: Super-virtual inteferometric diffractions
• Summary
Random Time Shift𝑳𝟏𝒎=𝒅𝟏
O(1/S) cost!
Encoding Matrix
Supergather
Random source time shifts
𝑳𝟐𝒎=𝒅𝟐
𝒅=𝑵𝟏𝒅𝟏+𝑵𝟐𝒅𝟐
Encoded supergather modeler
𝑳𝒎=[𝑵 ¿¿𝟏𝑳𝟏+𝑵𝟐𝑳𝟐]𝒎¿
Random Time Shift𝑳𝟏𝒎=𝒅𝟏
Encoding Matrix
Supergather
𝑳𝟐𝒎=𝒅𝟐
𝒅=𝑵𝟏𝒅𝟏+𝑵𝟐𝒅𝟐
Encoded supergather modeler
𝑳𝒎=[𝑵 ¿¿𝟏𝑳𝟏+𝑵𝟐𝑳𝟐]𝒎¿
× (-1) × (+1)
Conventional Least-squares
Find: an s.t. min
Given: &
Direct solution:
Iterative solution:
Note: subscripts agree
If is too big
In general, hugedimension matrix
Problem: Each prediction is a FD solve
Solution: Multisource technique
Conventional Least-squares
Multisource Least-squares
Find: an s.t. min
Given: &
Direct solution:
In general, smalldimension matrix
If is too big
Iterative solution:
+ crosstalk
0 X (km) 18
0Z
(km
)7.
5
4.5
1.5
km/s
Size: 1800 x 750Grid interval: 10 mSource number: 1800Receiver number: 1800
FD kernel: 2-4 staggered gridSource: 15 Hz
HESS VTI Model
HESS VTI ModelDelta and Epsilon Models
0Z
(km
)7.
5
1.5
0
0 X (km) 18
0Z
(km
)7.
5
2.5
0
Delta
Epsilon
Migration Velocity and Reflectivity
0Z
(km
)7.
5
4.5
1.5
0 X (km) 18
0Z
(km
)7.
5
0.2
-0.4
km/sMigration Velocity
Reflectivity
RTM VS Multisource LSRTM
0 X (km) 18
0Z
(km
)7.
5
0 X (km) 18
0Z
(km
)7.
5
8 supergather30 iterationsSpeedup: 3.75
Standard RTM
Multisource LSRTM, 1 Supergather Multisource LSRTM, 4 Supergather Multisource LSRTM, 8 Supergather
Artifacts removed
Resolution Enhanced
Signal-to-noise Ratio
SNR ∞
3D SEG/EAGE Model400 Shots Evenly Distributed
Size: 676 x 676 x 201Grid interval: 20 mReceiver: 114244
Source: 5.0 hz
13.5 km
4.0 km 13.5 km
Smooth Migration Velocity
20
13.5 km
4.0 km 13.5 km
Obtained by 3D boxcar smoothing
Conventional RTM
13.5 km
4.0 km13.5 km
400 Shots, Migrated One by One
13.5 km
4.0 km13.5 km
LSRTM400 Shots, 25 Shots/Supergather
13.5 km
4.0 km13.5 km
Conventional RTM100 Shots
13.5 km
4.0 km13.5 km
LSRTM100 Shots, 10 Shots/Supergather
Chapter 2: Conclusions• MLSM can produce high quality images efficiently.
LSM produces high quality image.
Multisource technique increases computational
efficiency.
SNR analysis suggests that not too many iterations
are needed.
• Random encoding is not applicable to marine streamer data.
Fixed spread geometry (synthetic) Marine streamer geometry (observed)
6 traces 4 traces
Mismatch between acquisition geometries will dominate the misfit.
Chapter 2: Limitations
Outline• Introduction and Overview
• Chapter 2: Multisource least-squares reverse time
migration
• Chapter 3: Frequency-selection encoding LSRTM
• Chapter 4: Super-virtual inteferometric diffractions
• Summary
28
observeddata
simulateddata
misfit = erroneous
misfit
Problem with Marine Data
29
Solution• Every source is encoded with a unique
signature.
observed simulated
• Every receiver acknowledge the contribution from the ‘correct’ sources.
4 shots/group
R(w)
Group 1
Nw frequency bands of source spectrum:
Frequency Selection
2 km
wAccommodate up to Nw shots
Single Frequency Modeling
(𝜵𝟐+𝝎𝟐
𝒗𝟐 )~𝑷=−𝐖 (𝝎 )𝛅(𝒙 −𝒔)
Helmholtz Equation
(𝜵𝟐− 𝟏𝒗𝟐
𝝏𝟐𝝏𝟐 𝒕 )𝐏=−𝐑𝐞 {𝐖 (𝝎 )𝐞𝐱𝐩 (−𝒊𝝎𝒕 )}𝛅(𝒙−𝒔)
Acoustic Wave Equation
• Advantages: Lower complexity in 3D case. Applicable with multisource technique.
Harmonic wave source
Single Frequency Modeling
(𝜵𝟐− 𝟏𝒗𝟐
𝝏𝟐𝝏𝟐 𝒕 )𝐏=−𝐑𝐞 {𝐖 (𝝎 )𝐞𝐱𝐩 (−𝒊𝝎𝒕 )}𝛅(𝒙−𝒔)
Am
plitu
de
T T
Single Frequency ModelingA
mpl
itude
0 Freqency (Hz) 50
Am
plitu
de
20 Freqency (Hz) 30
Marmousi2
0 X (km) 8
0Z
(km
)3.
5
4.5
1.5
km/s
• Model size: 8 x 3.5 km• Shots: 301
• Cable: 2km
• Receivers: 201
• Freq.: 400 (0~50 hz)
0 X (km) 8
0Z
(km
)3.
5
0 X (km) 8
Z (k
m)
3.5
Conventional RTM0
LSRTM Image (iteration=1)LSRTM Image (iteration=20)LSRTM Image (iteration=80) Cost: 2.4
Frequency-selection LSRTM of 2D Marine Data
0 X (km) 18.7
0Z
(km
)2.
5
2.1
1.5
km/s
• Model size: 18.7 x 2.5 km • Freq: 625 (0-62.5 Hz) • Shots: 496 • Cable: 6km• Receivers: 480
Conventional RTM
Frequency-selection LSRTM
Z (k
m)
2.5
0Z
(km
)2.
50
0 X (km) 18.7
Freq. Select LSRTM
Conventional RTM Conventional RTM
Freq. Select LSRTM
Zoom Views
Chapter 3: Conclusions• MLSM can produce high quality images efficiently.
LSM produces high quality image.
Frequency-selection encoding applicable to marine
data.
• Limitation:
High frequency noises are present.
Outline• Introduction and Overview
• Chapter 2: Multisource least-squares reverse time
migration
• Chapter 3: Frequency-selection encoding LSRTM
• Chapter 4: Super-virtual inteferometric diffractions
• Summary
Chapter 4: Super-virtual inteferometric diffractions
• Diffracted energy contains valuable
information about the subsurface structure.• Goal: extract diffractions from seismic data
and enhance its SNR.
Rotate
Guide Stars
Step 1: Virtual Diffraction Moveout + Stacking
y zw3
dt
w2 w1 y z
y’
dt
dt
dt
w
y z
y’
=
Super-virtual stacking theory
Step 2: Redatum virtual refraction to known surface position
y z
y’
y zx y zx
=*
y z x x
=
y z
i.e.y’
Super-virtual stacking theory
Step 3: Repeat Steps 1&2 for a Different Master Trace
y z
y’
y zx y zx
=*
y z x x
=
y z
i.e.y’
Super-virtual stacking theory
Stacking Over Master Trace Location
x zDesired shot/
receiver combination
Common raypaths
Super-virtual stacking theory
Super-virtual Diffraction Algorithm
=w z
=
+
*
1. Crosscorrelate and stack to generate virtual diffractions
2. Convolve to generate super-virtual diffractions
3. Stack super-virtual diffractions to increase SNR
w
w z w z
w z
Virtual srcexcited at -tzz’ z’
w z
w z w z w z
+
Synthetic Results: Fault Model
0 X (km) 6
0Z (k
m)
3
3.4
1.8
km/s
Synthetic Shot Gather: Fault Model
0 Offset (km)
6
0tim
e (s
)3
Diffraction
Synthetic Shot Gather: Fault Model0.
5tim
e (s
)1.
5Raw Data
0 Offset (km) 6
0.5
time
(s)
1.5
0 Offset (km) 6
Our Method
0.5
time
(s)
1.5
Median Filter
Estimation of Statics
0 Offset (km) 6
0.5
time
(s)
1.0
Picked Traveltimes
Predicted Traveltimes
Estimate statics
Experimental Cross-well Data
0 Depth (m) 300
0.3
time
(s)
1.0
180 Depth (m) 280
0.6
time
(s)
0.9
Picked Moveout0.
6tim
e (s
)0.
9
180 Depth (m) 280
Experimental Cross-well Data
180 Depth (m) 280
0.6
time
(s)
0.9
180 Depth (m) 280
0.6
time
(s)
0.9
Median Filter
Time Windowed
180 Depth (m)
0.6
time
(s)
0.9
280
Super-virtual Diffractions
Experimental Cross-well Data
0 Depth (m) 300
0.3
time
(s)
1.0
180 Depth (m) 280
0.6
time
(s)
0.9
Super-virtual Diffraction0.
6tim
e (s
)0.
9
Median Filtered
180 Depth (m) 280
• Super-virtual diffraction algorithm can greatly improve
the SNR of diffracted waves..
Limitation• Dependence on median filtering when there are other coherent
events.• Wavelet is distorted (solution: deconvolution or match filter).
Chapter 4: Conclusions
Outline• Introduction and Overview
• Chapter 2: Multisource least-squares reverse time
migration
• Chapter 3: Frequency-selection encoding LSRTM
• Chapter 4: Super-virtual inteferometric diffractions
• Summary
Chapter 2: Multisource LSRTM• Multisource LSRTM is implemented with random encoding
functions.
LSM produces high quality image. Multisource technique increases computational
efficiency.Multisource LSRTM, 8 Supergather
Chapter 2: Frequency-selection LSRTM
• Multisource LSRTM is implemented with frequency-
selection encoding functions.
Applicable to marine data.
Frequency-selection LSRTM
• Super-virtual diffraction algorithm can extract diffraction
waves and greatly improve its SNR.
Chapter 4: Super-virtual inteferometric diffractions
Before Before After
Acknowledgements
I thank the sponsors of CSIM consortium for their financial support.
I thank my advisor Prof. Gerard T. Schuster and other committee members for the supervision
over my program of study.
I thank my fellow graduate students for the collaborations and help over last 4 years.
WorkflowRaw data
Pick a master trace
Cross-correlate all the traces with the master trace
Repeat for all the shots and stack the result to give virtual diffractions
Convolve the virtual diffractions with the master trace to restore original traveltime
Stack to generate Super-virtual Diffractions
Diffraction Waveform Modeling
BornModeling
0 Distance (km) 3.8
0D
epth
(km
)1.
20
Dep
th (k
m)
1.2
0tim
e (s
)4.
0
0 Distance (km) 3.8
Velocity
Reflectivity
Diffraction Waveform Inversion
0 Distance (km) 3.8
0D
epth
(km
)1.
20
Dep
th (k
m)
1.2
Initial Velocity
Estimated Reflectivity
0D
epth
(km
)1.
2
Inverted Velocity
0 Distance (km) 3.8
0D
epth
(km
)1.
2
True Velocity