Surface deployed Distributed Acoustic Sensing (DAS) for seismic monitoring: An example of the Stanford Array Data Gang Fang 1 , Yunyue Elita Li 1 , Yumin Zhao 1 ,Eileen Martin 2 and Diming Yu 3 2018-10-18 1, National University of Singapore 2, Virginia Tech 3, Cambridge Sensing Pte. Ltd.
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Surface deployed Distributed Acoustic Sensing (DAS) for seismic monitoring: An example of the Stanford Array Data
Gang Fang1, Yunyue Elita Li1, Yumin Zhao1,Eileen Martin2 and Diming Yu3
2018-10-18
1, National University of Singapore
2, Virginia Tech
3, Cambridge Sensing Pte. Ltd.
2
Near-surface geohazards in urban environments
➢ Urban sinkhole
A sinkhole appeared in the centre lane of Clementi Road of Singapore on 06 Mar 2013
3
➢ Urban sinkhole
A sinkhole appeared in the centre lane of Clementi Road of Singapore on 06 Mar 2013
Near-surface geohazards in urban environments
4
A giant sinkhole caused by the rains
of tropical storm Agatha in Guatemala
City, June 2010. At least 175 people
are killed in this disaster.
➢ Urban sinkhole
Near-surface geohazards in urban environments
5
Landslide in eastern China on Nov 16, 2015 that has killed at least 25 people. Shenzhen landslide on 20 December 2015, 73 people was dead and 4 people
reported missing.
➢ Landslide
Near-surface geohazards in urban environments
• Distributed Acoustic Sensing (DAS)
• Near-surface monitoring with Stanford DAS data
Outlines
6
7
Distributed Acoustic Sensing (DAS)
8
Distributed Acoustic Sensing
It is a laser-based sensor system measuring vibration along the
length of a fiber optic sensing cable.
DAS Advantages:
✓Non-intrusive
✓Permanent
✓Low-cost
✓High spatial resolution
9
➢ Downhold DAS monitoring
• Hydraulic fracturing characterization
• Time-lapse monitoring of the reservoirs fluids
• CO2 injection monitoring
➢ Surface DAS
• Earthquakes analysis
• Near surface geophysical detection
(From OptaSense)
(Lindsey et al. 2017)
Distributed Acoustic Sensing
Near-surface monitoring with Stanford DAS data
10
⚫ Stanford DAS Array
⚫ Quarry blasts data
⚫ Seismic interferometry
⚫ Construction monitoring
11
Stanford DAS Array
Construction sites138
156
241
48
5277
108
184 Fiber line
184 Channel number
Front array
Back array
Layout of Stanford DAS Array (Biondi et al., 2017; E. Martin et al., 2017)
12
138
156
241
48
5277
108
184
Stanford DAS Array
Layout of Stanford DAS Array (Biondi et al., 2017; E. Martin et al., 2017)
13
138
156
241
48
5277
108
184
Stanford DAS Array
Layout of Stanford DAS Array (Biondi et al., 2017; E. Martin et al., 2017)
14
138
156
241
48
5277
108
184
DAS Array parameters
- 2 x 2.45 km fiber-optic cable
- 2 x 305 channels
- ~8 m sensor spacing
- 7 m gauge length
- Continuous recording
- 0-25 Hz digital recording
- OptaSense DAS (ODH 3.1)
- Single-mode fibre
Stanford DAS Array
Layout of Stanford DAS Array (Biondi et al., 2017)
Near-surface monitoring with Stanford DAS data
15
⚫ Stanford DAS Array
⚫ Quarry blasts data
⚫ Seismic interferometry
⚫ Construction monitoring
16
Quarry blasts data
138
156
241
48
5277
108
184
17
Construction A is begin at
2016/11/7.Compare blast
data before and after the
construction.
Challenges:
• High frequency noise
Quarry blasts data — raw data
18
Quarry blasts data — BP filtering(0.25~2.5HZ)
Construction A is begin at
2016/11/7.Compare blast
data before and after the
construction.
Challenges:
• High frequency noise
19
Quarry blasts data — Surface wave
Construction A is begin at
2016/11/7.Compare blast
data before and after the
construction.
Challenges:
• High frequency noise
Surface waves from a blast
20
Traffic noise
Quarry blasts data — Traffic noise
Construction A is begin at
2016/11/7.Compare blast
data before and after the
construction.
Challenges:
• High frequency noise
21
Traffic noise
Quarry blasts data — Traffic noise
Construction A is begin at
2016/11/7.Compare blast
data before and after the
construction.
Challenges:
• High frequency noise
• Traffic noise
22
2016/10/12 2016/11/15
Challenges:
• High frequency noise
• Traffic noise
• Inconsistent source wavelets
Quarry blasts data — Denoise
23
2016/10/12 2016/11/15
Solution:
• Seismic interferometry
Challenges:
• High frequency noise
• Traffic noise
• Inconsistent source wavelets
Quarry blasts data — Denoise
Near-surface monitoring with Stanford DAS data
24
⚫ Stanford DAS Array
⚫ Quarry blasts data
⚫ Seismic interferometry
⚫ Construction monitoring
25
Seismic interferometry
RS
RA
RB
26
Seismic interferometry
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Wavefield at RA :
Wavefield at RB :
Seismic interferometry
28
Source wavelet
Wavefield at RA :
Wavefield at RB :
Seismic interferometry
29
Source wavelet
Green function
Seismic interferometry
Wavefield at RA :
Wavefield at RB :
30
Cross-correlation :
Source wavelet
Green function
Seismic interferometry
Wavefield at RA :
Wavefield at RB :
31
Cross-correlation :
Source wavelet
Green function
Seismic interferometry
Wavefield at RA :
Wavefield at RB :
Redatum the source position from RS to RA.
32
Cross-correlation :
Source wavelet
Green function
Seismic interferometry
Wavefield at RA :
Wavefield at RB :
Redatum the source position from RS to RA.
Normalized Cross-correlation :
33
Cross-correlation :
Source wavelet
Green function
Seismic interferometry
Wavefield at RA :
Wavefield at RB :
Redatum the source position from RS to RA.
Normalized Cross-correlation :
Remove the influences from amplitudes.
Near-surface monitoring with Stanford DAS data
34
⚫ Stanford DAS Array
⚫ Quarry blasts data
⚫ Seismic interferometry
⚫ Construction monitoring
35
Applied normalized cross-correlation
Wavelets of virtual source
36
Normalized Cross-correlation :
Applied normalized cross-correlation
Wavelets of virtual source
37
Normalized Cross-correlation :
Applied normalized cross-correlation
Wavelets of virtual source
Spectra of the normalized cross-correlation
2016/10/12
2016/11/15
0
0.6
1.2
0 1 2 3 4 5 (Hz)
38
Velocity changes
Normalized cross-correlation
Calculated time-delays according to the reference velocity, 816m/s
Picked time-delays of the normalized cross-correlation
Front Back
2016/10/12Front Back
2016/11/15
• Surface DAS
An efficient geophysical tool to monitor near-surface changes;