Can We Predict Earthquakes?
Andrea Nemeth
Advisor: Dr. Mark Schilling
1
Earthquake Prediction
location
time
magnitude
probability of
occurrence
reliable
accurate
The collapse of part of Jefferson Junior High
School in Long Beach in 1933.
(Photo: Portland Cement Association)
2
Methods Employed In
Earthquake Prediction
statistical probability
physical measurements
geochemical observations
observations of animal
behavior
Seismicity of California (USGS)
3
Real Data or Simulated?
Significant CA Earthquakes 2 (1800-2003)
(Magnitude M>=6)
Significant CA Earthquakes1 (1800-2003)
(Magnitude M>=6)
8
8
7.8
7.8
7.6
7.6
7.4
7.4
7.2
7.2
7
7
6.8
6.8
6.6
6.6
6.4
6.4
6.2
6.2
6
6
0
10
20
30
40
50
60
70
80
0
10
20
30
40
50
60
4
70
80
Popular media statements
“the Big One is overdue”
“the longer it waits, the
bigger it will be”
(USGS)
5
Statistical Models
time-independent
Poisson (exponential)
model
time-dependent
Gaussian
gamma
log-normal
Weibull distributions
Brownian Passage
Time
6
Poisson Model
Magnitudes of EQs
and the time intervals
between EQs are each
assumed to be
independently
distributed.
Weibull Model
The probability of
rupture is a function of
the accumulated strain.
memoryless
F (t )  1  e
 t
F (t )  1  e
7
 t n
Parkfield and Wrightwood
Parkfield area
medium-sized EQs
occur here fairly
regularly
Wrightwood area
long term data is
available
LA
(USGS)
8
The
Experiment
1857, 1881, 1901,
1922, 1934, 1966
USGS prediction:
an earthquake of ~M6
would occur in
Parkfield between 1983
and 1993
9
So how regular are the recurrence
times of these earthquakes?
The intervals between these EQs:
24, 20, 21, 12, 32, 38
The Recurrence Times of the EQs in the Parkfield Experiment
Mean:
40
24.5 years
35
Standard
deviation:
9.25 years.
Time (years)
30
25
20
15
10
5
0
0
1
2
3
4
5
6
10
7
Probability Plots
Weibull Probability Plot for T in the PE
Exponential Probability Plot for T in the PE
99
99
ML Estimates
Mean:
24.5
98
97
Percent
Percent
95
90
80
ML Estimates
95
90
80
70
60
50
40
30
Shape: 3.20754
Scale:
20
10
5
70
60
50
3
2
30
10
1
0
50
100
150
Data
200
250
10
100
Data
11
27.4210
Can we rule out the possibility that even
EQs at Parkfield are random in time?
T    ln(1  R)
24
7
3
10
25
31
Result: 8.8% of all
20
20
25
29
7
53
simulated interval
sequences had
standard deviation
less than 9.25.
21
4
5
27
20
5
12
13
9
7
47
18
32
17
16
32
2
22
38
11
41
27
39
12
9.25
5.79
1
Conclusion:
This sequence is
somewhat regular,
but not extremely
unusual.
14.59 10.63 17.75 16.93
12
Wrightwood
534, 634, 697, 722, 781, 850, 1016, 1116, 1263, 1360,
1470, 1536, 1610, 1690, 1812, 1857
13
The Recurrence Times of the EQs
at Wrightwood
The time intervals between successive EQs:
100, 63, 25, 59, 69, 166, 100, 147, 97, 110, 66, 74, 80,
122, and 45 years.
180
140
120
Time (years)
mean:
88.2 years
standard
deviation:
37.8 years.
160
100
80
60
40
20
0
0
2
4
6
8
10
EQ Interval Index (1-15)
12
14
14
16
Probability Plots
Exponential Probability Plot for the Time Intervals
between EQs at Wrightwood
99
Weibull Probability Plot for the Time
Intervals between EQs at Wrightwood
99
ML Estimates
Mean:
88.2000
98
97
Percent
Percent
95
90
80
ML Estimates
95
90
80
70
60
50
40
30
Shape: 2.59754
Scale:
20
10
5
70
60
50
3
2
30
10
1
0
100
200
300
Data
400
500
600
10
100
Data
15
99.4288
Simulation for the Wrightwood
area
Result:
Only 1.5% of all simulated interval
sequences had standard deviation less than
37.8 years.
Conclusion:
This sequence of 16 EQs at Wrightwood
is more regular than the Parkfield
sequence.
16
Summary
Several factors make EQ
prediction difficult:
the cycle of EQs is long
the fundamental physics of EQ
faulting is not yet understood
no clearly recognizable
precursor has been observed
EQ history is short for most
faults
17
Potential Future Work
Further investigation of the Wrightwood
data
Analysis of other data sets from the
San Andreas Fault
Study of other statistical models with our
data
18
Acknowledgments
This project was sponsored by the NASA/JPL
PAIR program.
I thank Dr. Carol Shubin for her continuous
support, interest and encouragement.
I’m very grateful to Dr. Mark Schilling, my
advisor, for his comments on the data analysis and
preparation, for his valuable insights and
observations.
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