Transcript Imageodesy on MPI & grid for co-seismic shift study using satellite

Knowledge discovery from massive data
processing for earthquake study
Imageodesy on MPI & GRID for Co-seismic
Shift Study Using Satellite Optical Imagery
Jian Guo Liu and Jinming Ma
Department of Earth Science and Engineering
Imperial College London
South Kensington campus, London SW7 2AZ, UK
[email protected]
Outline of the presentation
1. Introduction: data mining software
development for geohazard study
2. Imageodesy: the principle and
implementation on MPI and GRID
3. Case study: Co-seismic shift of the Ms 8.1
Kunlun earthquake
4. Conclusions
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Acknowledgement
This research is part of DiscoveryNet project
(GR/R67750/01) supported by EPSRC e-science pilot
project grant.
Computing Centre of Imperial College London provided
parallel processing facilities and technical support.
MIT Phase Correlation website provided free access and
technical support for software development.
ER Mapper image processing software has been used for
data visualisation and analysis.
Xinjiang Bureau of Seismology is acknowledged for
providing field photos and some reference materials of
Kunlun earthquake.
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a UK e-Science Pilot Project
1. Introduction
Geohazard study in DiscoveryNet project
Geohazard monitoring and assessment is one of the major application areas of
DiscoveryNet project aiming to scientific knowledge discovery.
Remote sensing
A typical characteristic of geohazards is that movement and displacement will be
produced. For instance, an earthquake produces co-seismic displacement while a
landslide is characterised by slope failure and mess movement. Remotely sensed
imagery data enable detection and measurement of these changes and thus
provide vital information for hazard assessment and prevention.
Algorithm and software development
Fast imageodesy algorithms and software for high accuracy change detection
have been developed and integrated into DiscoveryNet workbench. The massive
data processing is possible only with advanced algorithms using powerful
MPI/GRID computing.
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2. Imageodesy: the principle
The ‘Imageodesy’ technique (Crippen 1992; Crippen & Blom 1996)
is capable of measuring the horizontal shift of image features, at a
sub-pixel level accuracy, through normalized cross-correlation
(NCC) between pre- and post-event images.
This technique is a complementary to the well-established radar
interferometry technique that is sensitive to vertical deformation.
The major technical challenge of high accuracy imageodesy is the
huge demand on computing to handle with massive data
processing.
We have developed software to implement the imageodesy on MPI
parallel processor based on the conventional template normalised
cross-correlation (NCC) algorithm. Furthermore we developed new
algorithm and software based on phase correlation algorithm.
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2. Imageodesy: FNCC algorithm
The NCC is defined as:
_
R(u, v) 
 x, y [ f ( x, y)  f u ,v ][t ( x  u, y  v)  t ]
1/ 2

2
2
[
f
(
x
,
y
)

f
]
[
t
(
x

u
,
y

v
)

t
]
 x , y

u ,v  x , y


_
The Fast NCC algorithm (FNCC) reduces large quantity of
repeated operations and further improvement including
conditional jumps and smart sampling avoids unnecessary
operations over homogeneous image features. Thus the
processing can be speed up by 5-10 times depending on the
size of processing windows and the image characteristics.
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2. Imageodesy: FNCC MPI implementation
Image
“before”
Image
“after”
Read dataset
Read dataset
Set
searching
window
Set
calculation
window
Maximum
correlation
Move
calculation
window
N
Y
Shift-X
Shift-Y
Correlation
coefficient
With an improved FNCC
algorithm, operating on a
MPI UNIX parallel
computer with 24
processors, it takes 10
hours to complete
imageodesy processing
for one pair of cross-event
Landsat-7 ETM+ Pan
imagery data. The image
size is 3.75GB after
interpolating to 3m pixel
size.
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2. Imageodesy: FNCC GRID implementation
•
FNCC is a neighbourhood processing, each image line (the minimal
data unit) must carry its neighbour image lines with it in order to
conduct the processing. This wipe off 50% efficiency of FNCC and
increases data communication by m times, where m is the search
window size.
•
The experiments on GRID using a small image (512512) completed
much slower than local processing using a single PC (2GHz
processor). Submission larger images of a few thousands lines and
columns to the GRID simply blocked the processing pipe line and
failed to complete the task.
•
The current status of GRID is not sufficient for the massive
neighbourhood processing of FNCC imageodesy. The future of GRID
for dealing with the type of processing of imageodesy lies on very fast
high throughput network.
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2. Imageodesy: Phase Correlation algorithm
Image “before”
Image “after”
Read Dataset
Read Dataset
Hamming
Windowing
Hamming
Windowing
FFTW
FFTW
Phase Correlation
Inverse FFTW
Delta X
Delta Y
Correlation
coefficient
Phase Correlation Imageodesy algorithm scheme
We have implemented phase
correlation algorithm for
imageodesy operating on
single UNIX and PC. By
transforming the image data
within a matching window into
frequency domain via FFT, the
phase correlation can pinpoint
the best matching position
directly as the peak of the
overlap between the
frequency distribution of the
two images, without the time
consuming searching .
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3. Case study: Co-seismic shift of Ms 8.1 Kunlun
earthquake
Kunlun Earthquake
On 14 November 2001, at 09:26:18 UTC, an Ms 8.1 earthquake occurred
in the East Kunlun Mountains, along the Kusai Lake segment of Kunlun
fault. A 400 km surface rupture zone of E-W to WNW-ESE orientation was
produced and, according to the field observations of Chinese scientists,
the fault displacement was as large as 16.3 m (Lin et al. 2002, 2003).
Remote sensing study
Landsat TM and SPOT images have already been used to locate and map
the visible surface rupture features (Fu & Lin 2003).
SAR interferometry (InSAR) would be an ideal technique to reveal the
stress field of the earthquake and to provide two-dimensional quantitative
measurements of the fault movement. The lack of high quality cross-event
ERS SAR fringe pairs for this region and in this particular time
unfortunately hindered the use of interferometry.
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3. Case study data: ETM+ imagery
Landsat ETM+ imagery data was chosen because of its large coverage,
improved 15 m resolution panchromatic band, and the availability of
suitable pre- and post-earthquake, almost cloud-free scenes.
The output images of imageodesy are: X-shift, Y-shift and R (the NCC
coefficient).
Scene Names (Path/Row)
Upper-Left corner coordinate
(Lat/Lon, Dec. Deg.)
Acquisition Date
Kusai Lake (KL)
scene: (138/035)
Pre
36.9984856N
91.2616043E
3 Oct. 2001
Post
37.0025253N
91.2769623E
15 May 2002
Buka Daban (BD)
scene: (139/035)
Pre
36.9999619N
89.7614136E
26 Oct. 2001
Post
36.9977531N
89.7337036E
26 Aug. 2002
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3. Case study results: the left-lateral
movement of Kunlun fault
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3. Case study results: Kusai Lake scene
Histograms of smoothing filtered X-shift image of KL scene with 0.7 correlation
threshold. Left: The whole scene: the high peak on the left is at 0.7 and the shoulder
on the right is centred at 2.5. Middle: The south side of the Kunlun fault zone, the
peak is at 2.5. Right: The north side of the Kunlun fault zone, the peak is at 0.7.
The range of the left-lateral shift is 1.5~8.1 m, while shift is mostDiscovery
commonly
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e-Science
Pilot
Project
5.4 m. The maximum net left-lateral displacement can be as great as 13 m.
3. Case study results: Kusai Lake scene
The co-seismic shift
vectors of the Kusai
Lake area overlaid
on the post
earthquake ETM+
Pan image.
The vectors were
derived from X and
Y-shift images, with
371371 window
averaging, 20% cutoff for elimination of
extreme values, and
a 0.8 NCC
coefficient criterion.
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3. Case study results: Kusai Lake scene
The co-seismic
shift vectors of the
Kusai Lake area
overlaid on the
post earthquake
ETM+ Pan image.
The vectors were
derived with X-shift
compensated by 3.6 m.
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The earthquake fault zone from
space
3. Case study results: Buka Daban scene
Histograms of smoothing filtered X-shift image of BD scene with 0.7 correlation
threshold. Left: The whole scene: the high peak on the left is at 0.35 and the low peak
on the right is centred at 1.7. Middle: The south side of the southern branch, the peak
is at 1.7. Right: The north side of the northern branch, the peak is at 0.3.
The range of the left-lateral shift is 1.0 to 8.2 m, while the most representative
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figure is 4.2 m.
3. Case study summary
Kusai Lake scene: Fault is consistently in W-E to WNW-ESE
direction. Left-lateral shift range 1.5 m~8.1 m, average 4.8 m,
the maximum 13 m.
Buka Daban scene: With the splayed nature of the fault in
this section, the displacement patterns become complicated
and stepwise. The average left-lateral shift over the broad
fault zone is 4.6 m and ranges from 1.0 m to 8.2 m.
Both sides of the faults moved toward the east. the south side
of the fault has been displaced significantly to the right (east)
relative to the largely stable, or slightly right-shifting, northern
block. The relative movement of the fault is left-lateral and the
south side of the fault is the active block.
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4. Conclusions
•
As an essential function of remote sensing data mining for geohazard
study in DiscoveryNet project, FNCC imageodesy technique has been
implemented on the workbench operating on MPI.
•
The FNCC implementation on GRID yields a disappointing
performance because the demand for data communication increases
dramatically when the neighbourhood processing of imageodesy is
distributed to many nodes.
•
The phase correlation based algorithm is currently not operational on
MPI or GRID processing mode. It is only more efficient when the
forward and inverse FFT operations in phase correlation take less
time than searching in FNCC.
•
Our imageodesy results present the first regional 2-D picture of the
co-seismic displacement of Kunlun fault as the result of the Ms 8.1
Kunlun earthquake, in a vast area, of about 320 km W-E and 180 km
N-S. It is an important scientific knowledge discovery.
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Picture provided by Xinjiang Bureau of Seismology
a UK e-Science Pilot Project