25
Least Squares Migration Least Squares Migration of of JAPEX JAPEX Data and PEMEX Data and PEMEX Data Data Naoshi Aoki Naoshi Aoki
Least Squares Migration ofLeast Squares Migration of JAPEX JAPEX Data and PEMEX DataData and PEMEX Data
Naoshi AokiNaoshi Aoki
OutlineOutline
1. Theory2. LSM resiliency to artifacts from poor acquisition
geometry3. LSM image sensitivity to wavelet estimation errors4. Multi-scale LSM applied to poststack JAPEX data5. Target-oriented LSM applied to poststack PEMEX
data6. Conclusions
TheoryTheoryPoststack 2D Syncline Model
Ricker wavelet (15 Hz)
Kirchhoff Migration
LSM
Forward modeling
Inversion
Steepest descent algorithm
OutlineOutline
1. Theory2. LSM resiliency to artifacts from poor acquisition
geometry3. LSM image sensitivity to wavelet estimation errors4. Multi-scale LSM applied to poststack JAPEX data5. Target-oriented LSM applied to poststack PEMEX
data6. Conclusions
LSM Resiliency to Artifacts fromLSM Resiliency to Artifacts from Poor Acquisition Geometry Poor Acquisition Geometry
3D U Model Model Description• Model size:
– 1.8 x 1.8 x 1.8 km • U shape reflectivity anomaly
• Cross-spread geometry– Source : 16 shots, 100 m int.– Receiver : 16 receivers , 100 m int.
Depth (m) Reflectivity
250 1
500 -1
750 1
1000 -1
1250 1
● Source● Receiver
U model is designed for testing Prestack 3D LSM with arbitrary 3D survey geometry.
CSG0
5
TW
T (
s)
0 1.8X (m)
Kirchhoff Migration vs. LSMKirchhoff Migration vs. LSMApplied to the 3D Applied to the 3D UU Model Model
(c) Z = 250 m (e) Z = 750 m (g) Z=1250m(a) Actual Reflectivity
Kirchhoff Migration Images
(b) Test geometry(d) Z=250m
LSM Images after 30 Iterations(f) Z=750m (h) Z=1250m
● Source● Receiver
LSM Resiliency to ArtifactsLSM Resiliency to Artifacts
• Test Summary– LSM showed a significant resiliency to artifacts
from poor acquisition geometry.
– LSM has an ability to reduce data acquisition expense.
OutlineOutline
1. Theory2. LSM resiliency to artifacts from poor acquisition
geometry3. LSM image sensitivity to wavelet estimation errors4. Multi-scale LSM applied to poststack JAPEX data5. Target-oriented LSM applied to poststack PEMEX
data6. Conclusions
LSM Image Sensitivity to LSM Image Sensitivity to Wavelet Estimation ErrorsWavelet Estimation Errors
• LSM algorithm requires a source wavelet.
• I tested LSM image sensitivity to wavelet estimation errors in the following 2 cases :1. LSM with correct wavelet,2. LSM with a Ricker wavelet (15 Hz).
LSM Image with Correct Source WaveletLSM Image with Correct Source Wavelet
0
2D
epth
(km
)0 2
X (km)
0
2
TW
T (
s)
0 2X (m)
Data LSM ImageActual Model
LSM Image with a Ricker Wavelet (15 Hz)LSM Image with a Ricker Wavelet (15 Hz)
Actual Model 0
2D
epth
(km
)0 2
X (km)
LSM ImageKirchhoff Migration
Image
LSM Image Sensitivity to Errors in the Source LSM Image Sensitivity to Errors in the Source WaveletWavelet
• Test Summary– An accurate estimate of the source wavelet is
important to obtain an accurate LSM image.
– However, LSM images are usually better than the standard migration image.
2D Poststack Data from Japan Sea2D Poststack Data from Japan SeaJAPEX 2D SSP marine data description:Acquired in 1974, Dominant frequency of 15 Hz.
0
5
TW
T (
s)
0 20X (km)
Multi-scale LSMMulti-scale LSM
• Starts by estimating a low wavenumber reflectivity model in order to avoid getting trapped in a local minimum.
• Band-pass filters, where the frequency bandwidth increases with the number of iterations, were iteratively applied to the input data.
Multi-scale LSM applied to JAPEX DataMulti-scale LSM applied to JAPEX Data
Multi-scale (MS) LSM vs. Standard LSM Convergence Curves
20 Hz
25
3032
34 36 3840
MS LSM Image
0.7
1.9
Dep
th (
km)
2.4 4.9X (km)
Standard LSM Image
0.7
1.92.4 4.9
X (km)
X10 5
3.0
0.5R
esid
ual
0 40Iteration
Multi-scale LSM
Standard LSM
LSM vs. Kirchhoff MigrationLSM vs. Kirchhoff Migration
LSM Image0.7
1.9
Dep
th (
km)
2.4 4.9X (km)
0.7
1.9
Dep
th (
km)
2.4 4.9X (km)
Kirchhoff Migration Image
Resolution comparisonResolution comparison
LSM vs. Standard Migration Magnitude Spectrum of Migration Image
1
0
Mag
nitu
de
0 0.04Wavenumber (1/m)
0.7
1.2
Dep
th (
km)
3.7 4.3X (km)
0.7
1.2
Dep
th (
km)
3.7 4.3X (km)
LSM Image Kirchhoff Migration Image
OutlineOutline
1. Theory2. LSM resiliency to artifacts from poor acquisition
geometry3. LSM image sensitivity to wavelet estimation errors4. Multi-scale LSM applied to poststack JAPEX data5. Target-oriented LSM applied to poststack PEMEX
data6. Conclusions
PEMEX 3D OBC Data from GOMPEMEX 3D OBC Data from GOM
0
4
TW
T (
s)
1 1001XL Number
IL3100 Stacked Section
Acquired in1990s.Since acquisition geometry is sparse, noise is dominant in the shallow part.
LSM Image0.7
1.9
Dep
th (
m)
2.4 4.9X (m)
LSM vs. Kirchhoff MigrationLSM vs. Kirchhoff Migration from PEMEX Data IL3100 from PEMEX Data IL3100
0.7
1.9
Dep
th (
m)
2.4 4.9X (m)
Kirchhoff Migration Image
Resolution comparisonResolution comparison
LSM vs. Standard Migration Magnitude Spectrum of Migration Image
1
0
Ma
gn
itude
0 0.04Wavenumber (1/m)
LSM Image
1
2.2
Dep
th (
km)
551 650XL Number 551 650
XL Number
Kirchhoff Migration Image
LSM
Kirchhoff Migration
TO LSM Applied for 3D DataTO LSM Applied for 3D Data
Preliminary Result of LSM Image after 4 iterations
Kirchhoff Migration Image
ConclusionsConclusions• Numerical results show:
• LSM has a significant resilience to artifacts from poor acquisition geometries .
• an accurate wavelet estimate provides an accurate LSM image.
• Results from JAPEX and PEMEX data show:– faster convergence rate is provided by a multi-scale migration
scheme.– 2D LSM is a practical means for improving quality image.– Encouraging results for TO LSM obtained from the 3D data
subset.
Future workFuture work
• GOAL: 3D LSM in less than 10 iterations.– Further improvement in efficiency will be
investigated.
AcknowledgementsAcknowledgements
• We thank PEMEX Exploration and Production for permission to use and publish its Gulf of Mexico data.
• I would like to thank JOGMEC and JAPEX for supporting my study at the University of Utah.
• We also thank the UTAM consortium members for supporting my work.
