Department of Earth Science and Engineering Imperial College London Meng-Che Wu
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ADAPTIVE LOCAL KRIGING (ALK) TO RETRIEVE THE SLANT RANGE SURFACE MOTION MAPS OF WENCHUAN EARTHQUAKE Department of Earth Science and Engineering Imperial College London Meng-Che Wu [email protected] Jian Guo Liu [email protected]
description
ADAPTIVE LOCAL KRIGING (ALK) TO RETRIEVE THE SLANT RANGE SURFACE MOTION MAPS OF WENCHUAN EARTHQUAKE. Department of Earth Science and Engineering Imperial College London Meng-Che Wu [email protected] Jian Guo Liu [email protected]. Outline. Background & Purpose - PowerPoint PPT Presentation
Transcript of Department of Earth Science and Engineering Imperial College London Meng-Che Wu
ADAPTIVE LOCAL KRIGING (ALK) TO RETRIEVE THE SLANT RANGE SURFACE MOTION MAPS OF WENCHUAN EARTHQUAKE
Department of Earth Science and EngineeringImperial College LondonMeng-Che [email protected] Guo [email protected]
Outline•Background & Purpose•Method Development•Experimental Results•Conclusions•Future works
Background & Purpose
Background & PurposePath 471Path 472
Path 473
Path 474
Path 475Path 476
Azimuth
Range
2π
0
Background & Purpose
≈ 1 m
≈ -1 m
Azimuth
Range
Path 471Path 472
Path 473
Path 474
Path 475Path 476
Ordinary kriging:Γ * λ = g
Γ is a matrix of the semivariance between each sampled point.
λ is a vector of the kriging weights.
g is a vector of the semivariance between a unknown point and each sampled point.
Semivariance = FSM(D)
FSM is the fitted semivariogram model.
D is the distance bewteen each sampled point or the distance between a unknown point and each sampled point.
Ordinary kriging concept
)Z(sλΣ )(sZ ii
N
1i0
S = (x, y) is a location
Example of semivariogram model
≈ 1 m
≈ -1 m
Gaussian model
Method: Adaptive Local Kriging
≈ 1 m
≈ -1 m
Azimuth
Range
Hang wall
Foot wall
1. Window based kriging scan to calculate the linear fitting of local semivariance.
2. Window size is locally adaptive to ensure adequate data points and high processing efficiency.
Semivariance
Distance
Averaged semivariance Fitted semivariance
x = 1024, y = 230
Local gradient: 1.258×10-5
ALK local semivariogram model: Towards the seismic fault (Hang wall side)
Semivariance
Distance
Averaged semivariance Fitted semivariance
ALK local semivariogram model: Towards the seismic fault (Hang wall side)
x = 1024, y = 460
Local gradient: 5.812×10-5
Semivariance
Distance
Averaged semivariance Fitted semivariance
ALK local semivariogram model: Towards the seismic fault (Hang wall side)
x = 1024, y = 580
Local gradient: 7.313×10-5
Semivariance
Distance
Averaged semivariance Fitted semivariance
ALK local semivariogram model: Towards the seismic fault (Foot wall side)
x = 745, y = 1200
Local gradient: 1.624×10-5
Semivariance
Distance
Averaged semivariance Fitted semivariance
ALK local semivariogram model: Towards the seismic fault (Foot wall side)
x = 745, y = 1000
Local gradient: 3.613×10-5
Semivariance
Distance
Averaged semivariance Fitted semivariance
ALK local semivariogram model: Towards the seismic fault (Foot wall side)
x = 745, y = 870
Local gradient: 7.652×10-5
ALK(Decoherence
zone)
ALK multi-step processing flow chart
Input data
Hang wall & foot wall separation
Final ALK
result
Ordinary kriging
ALK
Give some sampled points in the large decoherence gaps
Artificial discontinuity elimination
H
F
H
F
Coherencethresholding
Coherencethresholding
ALK data
≈ 1 m
≈ -1 m
Azimuth
Range
2π
0
ALK rewrapped interferogram
Azimuth
Range
Original interferogram
2π
0
Azimuth
Range
ALK results assessment
Azimuth
Range
Original unwrapped image profile
ALK data profile
A
A’
A A’Path 471 profiles
RMSE:0.0053591572meters
Correlationcoefficient:0.99999985
≈ 1 m ≈ -1 m
ALK results assessment
Original unwrapped image profile
ALK data profile
A A’
Azimuth
RangeA’
APath 472 profiles
RMSE:0.00909682429meters
Correlationcoefficient:0.99939712
≈ 1 m ≈ -1 m
ALK results assessment
Original unwrapped image profile
ALK data profile
Traced fault line Initial fault
A A’
Azimuth
RangeA’
A
Path 473 profiles
RMSE:0.0083477924meters
Correlationcoefficient:0.99973365
≈ 1 m ≈ -1 m
ALK results assessment
Original unwrapped image profile
ALK data profile
Traced fault lineInitial fault
A A’
Azimuth
RangeA’
A
Path 474 profiles
RMSE:0.017175553meters
Correlationcoefficient:0.99792644
≈ 1 m ≈ -1 m
ALK results assessment
Original unwrapped image profile
ALK data profile
Traced fault lineInitial fault
A A’
Azimuth
Range
A’
APath 475 profiles
RMSE:0.0059325138meters
Correlationcoefficient:0.99969193
≈ 1 m ≈ -1 m
ALK results assessment
Original unwrapped image profile
ALK data profile
A A’
Azimuth
Range
A’
≈ 1 m ≈ -1 m
APath 476 profiles
RMSE:0.0071013203meters
Correlationcoefficient:0.99929831
3D visualization of ALK data
≈ 1 m
≈ -1 m
Refined ALK data
≈ 1 m
≈ -1 m
Azimuth
Range
2π
0
Azimuth
Range
Refined ALK rewrapped data
3D view of refined ALK unwrapped data
≈ 1 m
≈ -1 m
Local semivariogram is more representive to the local variation of spatial pattern of the interferogram than a global semivariogram model.
Dynamical local linear model represents a nonlinear global model for the whole interferogram.
ALK multi-step processing procedure avoids the error increases in large decoherence gaps.
Conclusions
Conclusions The ALK interpolation data revealed dense
fringe patterns in the decoherence zone and show high fidelity to the original data without obvious smoothing effects.
The initial fault line separating the data does not affect the final interpolation result of ALK processing.
The seismic fault line that can be denoted in the ALK is different from that in publications. The discrepancy needs further investigation.
Geological structural numerical modeling to explain the discrepancy of trend of seismic fault line.
Three dimensional surface deformation maps development.
Future works
THANK YOU
Any questions ?
