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Volcanic Earthquake Timing
Using Wireless Sensor Networks
Guojin Liu1,2 Rui Tan2,3 Ruogu Zhou2 Guoliang Xing2
Wen-Zhan Song4 Jonathan M. Lees5
1Chongqing University, P.R. China2Michigan State University, USA
3Advanced Digital Science Center, Illinois at Singapore4Georgia State University, USA
5University of North Carolina at Chapel Hill, USA
Volcano Hazards
• 7% world population live near active volcanoes
• 20 - 30 explosive eruptions/year
Eruption in Chile, 6/4, 2011
$68 M instant damage, $2.4 B future relief.www.boston.com/bigpicture/2011/06/volcano_erupts_in_chile.html
Eruptions in Iceland 2010
A week-long airspace closure
[Wikipedia]
2
Volcano Monitoring
• Seismic activity monitoring– Earthquake localization, tomography, early warning etc.
• Traditional seismometer– Expensive (~$10K/unit), difficult to install & retrieve
– Only ~10 nodes installed for most threatening volcanoes!
Photo credit: USGS, http://volcanoes.usgs.gov/activity/methods/ 3
Sensor Networks for Volcano Monitoring
• Sensor systems for volcano monitoring
– Harvard , OASIS@GSU, VolcanoSRI@GSU/MSU/UNC
– Raw data collection@100Hz & centralized analysis
– Short lifetime (~1 week)
• In-network earthquake detection [Tan 2010]
– Distributed seismic signal processing
– 83% energy reduction from raw data collection
OASIS node
Harvardnode
4
Earthquake Timing
Node 05
Node 04
Node 06
0 1 2 3
P-phase
• Key to localization, seismic tomography, etc.– Usually done manually, automation is expensive
Time (second)0 1 2 3
Earthquake timingSource localization
Seismic tomography
5
Earthquake Timing
0 1 2 3 4
Node 09
Node 10?
• Key to localization, seismic tomography, etc.– Usually done manually, automation is expensive
• In-situ P-phase picking w/ limited transmission– Data intensive
– Sensors have limited compute & comm. capabilities
Source localization
Seismic tomography
0 1 2 3 4
5
Seismic Signal: Sparsity
Original @ 100Hz
Time (second)
6
Seismic Signal: Sparsity
Original @ 100Hz
Time (second)
Sparsity=0.57
%5||||
||||
2
2)( <−
s
ss kK-sparse signal:
length signalsparsity
k=
wavelet K largest
points
6
Seismic Signal: Sparsity
Original @ 100Hz
Wavelet
Time (second)
Sparsity=0.57
Sparsity=0.14
Time-frequency domain
%5||||
||||
2
2)( <−
s
ss kK-sparse signal:
length signalsparsity
k=
wavelet K largest
points
Sparsity=0.14
6
Seismic Signal: Sparsity
Original @ 100Hz
Wavelet
Time (second)
Sparsity=0.57
Sparsity=0.14
Time-frequency domain
%5||||
||||
2
2)( <−
s
ss kK-sparse signal:
length signalsparsity
k=
wavelet K largest
points
Sparsity=0.14
• Observation 1: wavelet sparsifies signal
6
Seismic Signal: Frequency-Time
4-level wavelet transform (length=1600)
thumbnail (length=100)
Low-pass band (0, 6.25Hz)
original
Time (unit: 160ms)
Low-pass band (0, 6.25Hz)
P-wave < 5Hz
7
Seismic Signal: Frequency-Time
4-level wavelet transform (length=1600)
thumbnail (length=100)
Low-pass band (0, 6.25Hz)
original
Time (unit: 160ms)
Low-pass band (0, 6.25Hz)
P-wave < 5Hz
Original (length=1600)
7
Seismic Signal: Frequency-Time
4-level wavelet transform (length=1600)
thumbnail (length=100) Rough P-phase
estimate
original
Time (unit: 160ms)
estimate
Original (length=1600)
7
Seismic Signal: Frequency-Time
4-level wavelet transform (length=1600)
thumbnail (length=100) Rough P-phase
estimate
original
Time (unit: 160ms)
estimate
• Observation 2: P-phase estimate from thumbnail
Original (length=1600)
7
Seismic Signal: Diversity
Node 1sparsity=0.1
Node 10sparsity=0.38
Earthquake 1
8
Seismic Signal: Diversity
Node 1sparsity=0.1
Node 10sparsity=0.38
Earthquake 1
Earthquake 2
Node 10sparsity=0.14
8
Seismic Signal: Diversity
Node 1sparsity=0.1
Node 10sparsity=0.38
Earthquake 1
• Observation 3: sensors have different sparsities
Earthquake 2
Node 10sparsity=0.14
8
Outline
• Problem statement
• Approach overview
• Earthquake timing algorithms
• Performance evaluation• Performance evaluation
• Conclusion
9
Approach Overview
Cluster head
10
Approach Overview
Cluster head
10
Approach Overview
preliminary picksignal sparsity
preliminary picksignal sparsity
preliminary picksignal sparsity
preliminary picksignal sparsitypreliminary pick
signal sparsity
Cluster head
• Lightweight signal processing algorithms– Signal sparsity
– Preliminary P-phase from thumbnail
10
Approach Overview
preliminary picksignal sparsity
preliminary picksignal sparsity
preliminary picksignal sparsity
preliminary picksignal sparsitypreliminary pick
signal sparsity
Cluster head
• Lightweight signal processing algorithms– Signal sparsity
– Preliminary P-phase from thumbnail
10
Approach Overview
Cluster head
• Lightweight signal processing algorithms– Signal sparsity
– Preliminary P-phase from thumbnail
• Select most informative sensors to TX
10
Approach Overview
××××0010
1001
0100
××××0010
1001
0100
0100
Cluster head
• Lightweight signal processing algorithms– Signal sparsity
– Preliminary P-phase from thumbnail
• Select most informative sensors to TX– Compressive sampling & transmission
××××0010
1001
10
Approach Overview
Signal
reconstruction
Signal
reconstruction
accurate pick
accurate pick
accurate pick
Source localization
Seismic tomography
…
• Lightweight signal processing algorithms– Signal sparsity
– Preliminary P-phase from thumbnail
• Select most informative sensors to TX– Compressive sampling & transmission
10
Outline
• Problem statement
• Approach overview
• Earthquake timing algorithms
– Pre-processing @ sensors– Pre-processing @ sensors
– Sensor selection & compressive sampling
• Performance evaluation
• Conclusion
11
Preliminary P-phase Pick4-level wavelet transform (length=1600)
thumbnail (length=100)
Time (unit: 160ms)
12
Preliminary P-phase Pick4-level wavelet transform (length=1600)
thumbnail (length=100)
preliminary pick
Time (unit: 160ms)
p
p
p beforeenergy signal
after energy signalmaxarg2picky preliminar
thumbnail
4
∈×=
12
Preliminary P-phase Pick4-level wavelet transform (length=1600)
thumbnail (length=100)
preliminary pick
Time (unit: 160ms)
p
p
p beforeenergy signal
after energy signalmaxarg2picky preliminar
thumbnail
4
∈×=
Map thumbnail domain back
to original time domain
12
Preliminary P-phase Pick4-level wavelet transform (length=1600)
thumbnail (length=100)
preliminary pick
Time (unit: 160ms)
p
p
p beforeenergy signal
after energy signalmaxarg2picky preliminar
thumbnail
4
∈×=
Map thumbnail domain back
to original time domain
• Lightweight: O(signal length)
– Suitable for resource-constrained sensors12
Outline
• Problem statement
• Approach overview
• Earthquake timing algorithms
– Pre-processing @ sensors– Pre-processing @ sensors
– Sensor selection & compressive sampling
• Performance evaluation
• Conclusion
13
Impact of Timing on Source Localization
• Source localization
– Basis for many volcano monitoring applications
– Complex non-linear inverse problem
z1z2
z
• Information-theoretic error metric
( )( )1tr
−= TE GG scaled Fisher matrix:
zi, z0
z1
z0
z3Vt1
t2
t3
),,(gray tracin 0 Vzzt ii =
14
Impact of Timing on Source Localization
• Source localization
– Basis for many volcano monitoring applications
– Complex non-linear inverse problem
z1z2
z
sensor
position
• Information-theoretic error metric
( )( )1tr
−= TE GG scaled Fisher matrix:
zi, z0
z1
z0
z3Vt1
t2
t3
),,(gray tracin 0 Vzzt ii =
14
Impact of Timing on Source Localization
• Source localization
– Basis for many volcano monitoring applications
– Complex non-linear inverse problem
z1z2
z
sensor
position
source
location
• Information-theoretic error metric
( )( )1tr
−= TE GG scaled Fisher matrix:
zi, z0
z1
z0
z3Vt1
t2
t3
),,(gray tracin 0 Vzzt ii =
14
Impact of Timing on Source Localization
• Source localization
– Basis for many volcano monitoring applications
– Complex non-linear inverse problem
z1z2
z
sensor
position
source
location
volcano
model
• Information-theoretic error metric
( )( )1tr
−= TE GG scaled Fisher matrix:
zi, z0
z1
z0
z3Vt1
t2
t3
),,(gray tracin 0 Vzzt ii =
14
Dynamic Sensor Selection
• Find a subset of sensors S to minimize E s.t.
CimcSi
i ≤⋅∑∈
)sensor ofsparsity (
15
Dynamic Sensor Selection
• Find a subset of sensors S to minimize E s.t.
CimcSi
i ≤⋅∑∈
)sensor ofsparsity (
unit TX cost
15
Dynamic Sensor Selection
• Find a subset of sensors S to minimize E s.t.
CimcSi
i ≤⋅∑∈
)sensor ofsparsity (
unit TX cost
TX volume
15
Dynamic Sensor Selection
• Find a subset of sensors S to minimize E s.t.
CimcSi
i ≤⋅∑∈
)sensor ofsparsity (
unit TX cost
TX volume cost budget
15
Dynamic Sensor Selection
• Find a subset of sensors S to minimize E s.t.
CimcSi
i ≤⋅∑∈
)sensor ofsparsity (
• Brutal-force search
– 8 seconds on Imote2 for 16 sensors
– Information gain diminishes for larger clusters
unit TX cost
TX volume cost budget
15
Compressive Sampling (CS)
• Apply CS to wavelet coefficients
=x
originalcompressedrandom matrix
m
n
n m
• Apply CS to wavelet coefficients
– Known TX volume before compression
– Unselected sensors avoid compression overhead
nm ××= sparsity5.1
16
Compressive Sampling (CS)
• Apply CS to wavelet coefficients
=x
originalcompressedrandom matrix
m
n
n m
• Apply CS to wavelet coefficients
– Known TX volume before compression
– Unselected sensors avoid compression overhead
nm ××= sparsity5.1
best trade-off b/w TX volume
and signal reconstruction error
16
Outline
• Problem statement
• Approach overview
• Earthquake timing algorithms
• Performance evaluation• Performance evaluation
– Testbed experiments
– Extensive trace-driven simulations
• Conclusion
17
Testbed Experiments
• Implementation on 12 TelosB
– Seismic data from Mt St Helens -> mote flash
– Real-time data acquisition @ 100 Hz
4
Exe
cuti
on
tim
e (
seco
nd
)
1 124 8
Sensor ID
0
1
2
3
Exe
cuti
on
tim
e (
seco
nd
)
End-to-end delay
< 3 seconds
18
Trace-driven Simulation
• Data traces from 12 sensors on Mt St Helens
• 30 significant earthquakes in 5.5 months
30
11
# o
f se
lect
ed
se
nso
rsLance
[SenSys’08]
200 400 600 200 400 6000
10
20
5
7
9
TX bound (# of pkts) TX bound (# of pkts)
Err
or
me
tric
# o
f se
lect
ed
se
nso
rs
our sensor
selection approach
[SenSys’08]
Configurable trade-off between system performance and energy consumption
19
Impact of Packet Loss
= xcompressed signal
30
Re
lati
ve r
eco
nst
ruct
ion
err
or
(%)
Lossy compression:
encodes largest
reconstructed signal
65 70 75 80 85 90 95 100Packet reception ratio (%)
0
10
20
Re
lati
ve r
eco
nst
ruct
ion
err
or
(%)
Compressive
Sensing
encodes largest
wavelet coefficients
CS is resilient to
packet loss!
20
Impact of Packet Loss
= xcompressed signal
received
30
Re
lati
ve r
eco
nst
ruct
ion
err
or
(%)
Lossy compression:
encodes largest
reconstructed signal
65 70 75 80 85 90 95 100Packet reception ratio (%)
0
10
20
Re
lati
ve r
eco
nst
ruct
ion
err
or
(%)
Compressive
Sensing
encodes largest
wavelet coefficients
CS is resilient to
packet loss!
20
Impact of Packet Loss
= xcompressed signal
received
30
Re
lati
ve r
eco
nst
ruct
ion
err
or
(%)
Lossy compression:
encodes largest
reconstructed signal
65 70 75 80 85 90 95 100Packet reception ratio (%)
0
10
20
Re
lati
ve r
eco
nst
ruct
ion
err
or
(%)
Compressive
Sensing
encodes largest
wavelet coefficients
CS is resilient to
packet loss!
20
Accuracy of Timingfine-grained pick on
original
fine-grained pick on
reconstructed
21
Accuracy of Timingfine-grained pick on
original
fine-grained pick on
reconstructed
16% data TX16% data TX0.6 km localization error
21
Conclusions
• Energy-efficient earthquake timing
– Lightweight algorithms for sensors
– Dynamic sensor selection
– Compressive sampling
• Testbed experiments
– Feasibility of our approach on motes
• Trace-driven simulations
– Accurate timing with 16% data transmitted
22
Hierarchical Network Architecture
6.7km
sensor
coordinator
cluster
STA/LTA detectorBayesian detector
0.1
0.2
0.3
sen
sor
/ co
ord
ina
tor
• Sensors
– Limited capability, large spatial coverage
• Coordinators
– Powerful, limited number
500 nodes on Tungurahua, Ecuador, 2015
[VolcanoSRI project]
0
sen
sor
/ co
ord
ina
tor
10 100 200
# earthquakes per day
MCU & radio energy ratio
TelosB vs. Imote2
23
Earthquake Source Localization
10
15
20
25
% o
f tr
an
smit
ted
da
ta
0.4
0.6
0.8
Sou
rce
loca
liza
tio
n e
rro
r (k
m)
Packet reception ratio
85%
180 220 260 300 360 220 260 300 3600
5
10
# of packets
% o
f tr
an
smit
ted
da
ta
0
0.2
Sou
rce
loca
liza
tio
n e
rro
r (k
m)
# of packets
Source localization result for an earthquake
16:56:47 Nov 03 2009 @ Mt St Helens
24
Earthquake Source Localization
10
15
20
25
% o
f tr
an
smit
ted
da
ta
0.4
0.6
0.8
Sou
rce
loca
liza
tio
n e
rro
r (k
m)
Packet reception ratio
85%
180 220 260 300 360 220 260 300 3600
5
10
# of packets
% o
f tr
an
smit
ted
da
ta
0
0.2
Sou
rce
loca
liza
tio
n e
rro
r (k
m)
# of packets
Source localization result for an earthquake
16:56:47 Nov 03 2009 @ Mt St Helens
Localization error below 1km, common in volcano seismology
Only 16% data transmission
24
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