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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
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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]
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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
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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
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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
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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
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Seismic Signal: Sparsity
Original @ 100Hz
Time (second)
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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
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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
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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
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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
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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
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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)
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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)
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Seismic Signal: Diversity
Node 1sparsity=0.1
Node 10sparsity=0.38
Earthquake 1
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Seismic Signal: Diversity
Node 1sparsity=0.1
Node 10sparsity=0.38
Earthquake 1
Earthquake 2
Node 10sparsity=0.14
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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
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Outline
• Problem statement
• Approach overview
• Earthquake timing algorithms
• Performance evaluation• Performance evaluation
• Conclusion
9
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Approach Overview
Cluster head
10
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Approach Overview
Cluster head
10
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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
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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
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Approach Overview
Cluster head
• Lightweight signal processing algorithms– Signal sparsity
– Preliminary P-phase from thumbnail
• Select most informative sensors to TX
10
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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
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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
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Outline
• Problem statement
• Approach overview
• Earthquake timing algorithms
– Pre-processing @ sensors– Pre-processing @ sensors
– Sensor selection & compressive sampling
• Performance evaluation
• Conclusion
11
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Preliminary P-phase Pick4-level wavelet transform (length=1600)
thumbnail (length=100)
Time (unit: 160ms)
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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
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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
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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
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Outline
• Problem statement
• Approach overview
• Earthquake timing algorithms
– Pre-processing @ sensors– Pre-processing @ sensors
– Sensor selection & compressive sampling
• Performance evaluation
• Conclusion
13
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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
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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
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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
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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
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Dynamic Sensor Selection
• Find a subset of sensors S to minimize E s.t.
CimcSi
i ≤⋅∑∈
)sensor ofsparsity (
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Dynamic Sensor Selection
• Find a subset of sensors S to minimize E s.t.
CimcSi
i ≤⋅∑∈
)sensor ofsparsity (
unit TX cost
15
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Dynamic Sensor Selection
• Find a subset of sensors S to minimize E s.t.
CimcSi
i ≤⋅∑∈
)sensor ofsparsity (
unit TX cost
TX volume
15
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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
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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
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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
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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
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Outline
• Problem statement
• Approach overview
• Earthquake timing algorithms
• Performance evaluation• Performance evaluation
– Testbed experiments
– Extensive trace-driven simulations
• Conclusion
17
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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
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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
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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
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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
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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
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Accuracy of Timingfine-grained pick on
original
fine-grained pick on
reconstructed
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Accuracy of Timingfine-grained pick on
original
fine-grained pick on
reconstructed
16% data TX16% data TX0.6 km localization error
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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
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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
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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
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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