Detection and replenishment of missing data in earthquake catalogs · 2020-02-14 · Detection and...
Transcript of Detection and replenishment of missing data in earthquake catalogs · 2020-02-14 · Detection and...
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Detection and replenishment of missing data inearthquake catalogs
Jiancang Zhuang
Institute of Statistical Mathematics [email protected]
Based on
1. Zhuang, J., T. Wang, K. KiyoSugi (2019) Detection and
replenishment of missing data in marked point processes.
Statistica Sinica. published online.
2. Zhuang, J., Y. Ogata, T. Wang (2017) Earth, Planet, and Space.
2019-5-10 NCU Seminar
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报告提纲
➢模型问题
➢数据问题
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地震震级模型和b值
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Gutenberg-Richter magnitude-frequency relation
Freq
ue
ncy
Magnitude
Energy
G-R magnitude-frequency relationlog10𝑁 ≥ 𝑚 = 𝑎 − 𝑏𝑚
Pr 𝑀 > 𝑚 𝑀 > 𝑚0 ≈𝑁 ≥ 𝑚
𝑁(≥ 𝑚0)
=10𝑎−𝑏𝑚
10𝑎−𝑏𝑚0= 10−𝑏 𝑚−𝑚0
Probability density function
𝑓 𝑚 = 𝑏 10−𝑏 𝑚−𝑚0 𝑙𝑛 10
= 𝛽𝑒−𝛽 𝑚−𝑚0 ; 𝑚 > 𝑚0
Power law distribution for energies, momentsand stress drops.
Pr 𝐸 > 𝑥 ∼ 𝐶𝑥−𝛼
for 𝛼 > 0, 𝑥 > 𝐸0 > 0.
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Truncated exponential distribution
𝑓 𝑚 = ൞𝛽𝑒−𝛽 𝑚−𝑚0
1 − 𝑒−𝛽 𝑀−𝑚0, 𝑚0 ≤ 𝑚 ≤ 𝑀
0, otherwise
Tapered exponential distribution
𝑓 𝑚 = 𝛽 + 1.5𝛾101.5 𝑚−𝑚𝑐 ln 10 exp −𝛽 𝑚 −𝑚0 − 𝛾101.5 𝑚−𝑚𝑐
In moment, Pr moment < 𝑥 = 1 −𝑥
𝑆0
−𝑘
exp𝑆0−𝑠
𝑆𝑐(Kagan) distribution
𝑚𝑐: corner magnitude
𝑆𝑐: corner moment
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相对频次
5 6 7 8 9 10震级
GR截断G-RTapered G-R
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b值的估计和地震震级的模拟
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(1) b-值最大似然估计
𝑏 =1
ഥ𝑀 −𝑚𝑐 +Δ2
ln 10
𝑠𝑡𝑑. 𝑒𝑟𝑟 𝑏 =𝑏
𝑛
平均震级 震级下限 震级最低位
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(1) b-值最大似然估计 绘图法
𝑚𝑖 −𝑚𝑐 + 0.05 ln10
1 𝑖 ∼
𝑘=1
𝑖
𝑚𝑘
斜率 = b值
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(1) b-值最大似然估计 绘图法检验b值变化点
b值变化点
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(1) b-值最大似然估计 绘图法检验b值变化点
b值变化点
把地震分别按照发震时刻、纬度、经度、深度、或者沿断层投影的位置进行排序,就可以看b值
在时间、纬度、经度、深度、或者断层上的变化。
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短期地震活动模型--- Omori-Utsu公式
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0[0, ]
log log ( ) ( )i
T
i
t T
L t t dt
= − Likelihood function
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pct
Ktn
)()(
+=
Omori-Utsu Formula (Utsu 1956)
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15
1
1 1 1 1( ) ( ) ( ) ( ) ...
( ) ( ) n
pp
p
n n n n
n t K t c H t T K t T c
H t T K t T c
−−
−
= + + − − + + +
− − +(Utsu, 1970; Ogata, 1983)
0( )
0
( )( )
( )
i
i
i
i
pt t i
M M
pt t i
Kt
t t c
eK
t t c
−
= +− +
= +− +
(Ogata, 1988; 1989)
:Conditional intensity, hazard function conditioning on the past history
( )t
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16
Temporal ETAS model
Conditional intensity
:
( ) ( ) ( )i
i i
i t t
t m g t t
= + −
( )( ) ( 1) 1 / , 0p
g t p t c / c t−
= − +
( )( ) , Cm m
Cm Ae m m −
= 1. Direct productivity:
2. Time p.d.f (Omori-Utsu):
Likelihood function0
[0, ]
log log ( ) ( )i
T
i
t T
L t t dt
= − (Ogata, 1988)
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➢数据问题 直后余震缺失问题
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Short-term missing of aftershocks
Missing events after the mainshock (from Omi etc, 2013)
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林芝地震 (2017-11-18 06:34:17.30 29.875 95.057 M6.9)
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The following is the earthquake probability forecast
for the 31th (2008-6-12 14:28 to 2008-6-13 14:28) day after the mainshock
Expected # Prob. Expected.WT 50% 95% 99% waiting time
M>=4.0 1.14 0.68 0.87 0.60 2.59 3.97
M>=4.5 0.54 0.41 1.83 1.26 5.50 8.48
M>=5.0 0.18 0.16 5.41 3.79 16.15 24.57
M>=5.5 0.05 0.05 16.86 12.48 46.94 72.50
M>=6.0 0.03 0.03 25.21 18.63 71.77 117.31
跳开最震后短时间内的地震进行拟合,结合使用较大震级下限• 汶川地震 M4.0以上
2008-07-15 17:26 15 Jul 2008 09:26 Mianzhu, Sichuan 31.57103.98 5.0 Ms
2008-07-24 03:54 23 Jul 2008 19:54 Ningqiang, Shaanxi 32.8105.5 5.6 Ms
2008-07-24 15:09 24 Jul 2008 07:09 Qingchuan, Sichuan 32.82105.47 6.0 Ms
2008-08-01 16:32 01 Aug 2008 08:32 Pingwu and Beichuan, Sichuan32.1 104.7 6.1 Ms
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The Kumamoto aftershock sequence data
Data Selection
Time: 2016/4/1~2016/4/21Mag.: 1.0+Depth: < 100 kmSpace: 128°-- 133°
30°-- 35°
2016-04-14 21:26 (130.81 32.74) M6.5 2016-04-15 00:03 (130.78 32.70) M6.42016-04-16 01:25 (130.76 32.75) M7.3
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Application to the recent Kumamoto aftershock sequence data
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Previous studies for fixing the problems of short-term missing aftershocks
Observational approaches◆ waveform-based earthquake detection methods (e.g., Enescu et al.,
2007, 2009; Peng et al., 2007; Marsan and Enescu, 2012; Hainzl, 2016).
◆Energy based description (Sawazaki and Enescu, 2014)
Statistical approaches◆ (Ogata, Omi, Iwata) Bayesian, assuming GR relation for whole range
◆ (Marsan and Enescu, 2012) Assuming Omori-Utsu formula or ETAS model
◆This study: Independence between magnitudes and occurrence times
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When data is complete ……
𝑡𝑖 → 𝜏𝑖 =𝑖
𝑁=# 𝑜𝑓 𝑡𝑖𝑚𝑒𝑠 < 𝑡𝑖
𝑁
Biscale empirical transformation
𝑚𝑖 → 𝑠𝑖 =σ𝑘 𝟏(𝑚𝑘 < 𝑚𝑖)
𝑁=# 𝑜𝑓 𝑚𝑎𝑟𝑘𝑠 < 𝑚𝑖
𝑁
Homogeneous process Original process
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When data missing exists……
Biscale empirical transformation
Non-homogeneous process Original process with missing
𝑡𝑖 → 𝜏𝑖 =𝑖
𝑁=# 𝑜𝑓 𝑡𝑖𝑚𝑒𝑠 < 𝑡𝑖
𝑁
𝑚𝑖 → 𝑠𝑖 =σ𝑘 𝟏(𝑚𝑘 < 𝑚𝑖)
𝑁=# 𝑜𝑓 𝑚𝑎𝑟𝑘𝑠 < 𝑚𝑖
𝑁
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Key points for Replenishing
Non-complete dataset
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Key points for Replenishing
Complete dataset
Non-complete dataset
Restore missing area withoutknowing missing data(red dots)?
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Replenishing algorithm
Step 1. Transform the process using the biscale empirical transformation
𝑡𝑖 → 𝜏𝑖 =𝑖
𝑁=# 𝑜𝑓 𝑡𝑖𝑚𝑒𝑠 < 𝑡𝑖
𝑁
𝑚𝑖 → 𝑠𝑖 =σ𝑘 𝟏(𝑚𝑘 < 𝑚𝑖)
𝑁=# 𝑜𝑓 𝑚𝑎𝑟𝑘𝑠 < 𝑚𝑖
𝑁
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Heuristic illustration for estimating empirical probability distribution function when missing happens in homogeneous process
𝐹𝑋 𝑥 =σ𝑖𝑤 𝑒𝑖 𝐼 𝑥𝑖 < 𝑥
σ𝑖𝑤 𝑒𝑖
𝑤 𝑒𝑖 =1
1 − 01𝐼 𝑥𝑖 , 𝑦 ∈ 𝑆 𝑑𝑦
Heuristic illustration
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Replenishing algorithm
Step 2. Specify area S that contains the missing data
The missing area S satisfies
𝑀𝟏 𝑡,𝑚 ∉ 𝑆 𝑑𝐹2(𝑚) > 0
for all 𝑡 ∈ [0, 𝑇] and
0𝑇𝟏 𝑡,𝑚 ∉ 𝑆 𝜇𝑔 𝑡 𝑑𝑡 > 0
for all 𝑚 ∈ 𝑀.
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Replenishing algorithm
Step 3. Calculate the missing area in the biscale transformation domain based on complete data
𝐹1∗ 𝑡 =
σ𝑗=1𝑛 𝑤1 𝑡𝑗 ,𝑚𝑗 , 𝑆 𝟏 𝑡𝑗 < 𝑡
σ𝑗=1𝑛 𝑤1 𝑡𝑗 , 𝑚𝑗 , 𝑆
𝐹2∗ 𝑚 =
σ𝑗=1𝑛 𝑤2 𝑡𝑗 ,𝑚𝑗 , 𝑆 𝟏 𝑚𝑗 < 𝑚
σ𝑗=1𝑛 𝑤2 𝑡𝑗 , 𝑚𝑗 , 𝑆
𝑤1 𝑡,𝑚, 𝑆 =𝟏 𝑡,𝑚 ∉ 𝑆
𝑀 𝟏 𝑡, 𝑠 ∉ 𝑆 𝑑𝐹2∗ 𝑠
𝑤2 𝑡,𝑚, 𝑆 =𝟏 𝑡,𝑚 ∉ 𝑆
𝑀 𝟏 𝜏,𝑚 ∉ 𝑆 𝑑𝐹1∗ 𝜏
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Replenishing algorithm
Step 4. Generate data point in the missing area
Generate events uniformly distributed in the missing region S*(image of S)
#events ~ 𝑁𝐵 𝑘, 1 − 𝑆∗
𝑘: # of observed events outside of 𝑆∗
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Replenishing algorithm
Step 4. Remove sequentially a simulated data point for each existing point in the missing area
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Replenishing algorithm
Step 5. Transform back all the events into the original domain
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How to test existence of missing: Testing method
𝐿2 columns
𝐿1rows
𝑅 =min 𝐶1, 𝐶2, ⋯ , 𝐶𝐿max 𝐶1, 𝐶2, ⋯ , 𝐶𝐿
𝐷 = max 𝐶1, 𝐶2, ⋯ , 𝐶𝐿−min 𝐶1, 𝐶2, ⋯ , 𝐶𝐿
𝐶𝑖: #events in cell 𝑖
𝑳 = 𝑳𝟏 × 𝑳𝟐
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Testing method
Observed data
𝐿1 = 𝐿2 = 5𝐿 = 25
Distributions of R and D when the same number of data points are completely observed
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Testing method
Replenished data
𝐿1 = 𝐿2 = 5𝐿 = 25
Distributions of R and D when the same number of data points are completely observed
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More Simulations
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If wrong selection of missing area (1)
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If wrong selection of missing area (1)
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Application 1: Volcanic eruption record
• Data: eruptions at Hakone volcano
• Data source: – Smithsonian's
Global Volcanism Program database
– Large Magnitude Explosive Volcanic Eruptions database (LaMEVE database)
– additional Japanese databases
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Application 1: Volcanic eruption record
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Application to the Wenchuan aftershock sequence data
Data selection:
Time: 1990/1/1~2013/4/20Magnitude: 3.0+
Wenchuan EQ: Mw7.9 (Ms8. 2) 2008/5/12
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Application to the Wenchuan aftershock sequence data
Observed data Biscale transformedEstimate transformation under complete data
Transformed replenished data Original replenished data Cumulative freq. vs time
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Influence of short-term aftershock missing on estimating the Omori formula
Omori-Utsu formula:
𝜆 𝑡 =𝐾 𝑝 − 1
𝑐1 +
𝑡
𝑐
−𝑝
, 𝑡 > 0
𝐾 𝑐 𝑝
Replenisheddata
Observed dataStart fitting at 0
Observed dataStart fitting at 54th day
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Application 3: the Kaikoura aftershock sequence data
Data selection:
Time: 2014/1/1~2017/2/17Magnitude: 2.0+
Kaikoura EQ: Mw7.8 2016/11/14
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Application 3: the Kaikoura aftershock sequence data
Observed data Biscale transformedEstimate transformation under complete data
Transformed replenished data Original replenished data Cumulative freq. vs time
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On the biased estimate of earthquake clustering parameters caused by short-term aftershock missing
Errors in estimated parameters propagate in forecasting.
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The ETAS model• Conditional intensity (Ogata, 1998)
:
( , , ) [ ( ) ] | |
( , ) ( ) ( ) ( , , )
/
i
i i i i i
i t t
tt x y E N dtdxdy dtdxdy
x y m g t t f x x y y m
=
= + − − −
∣H
( ) 0 ,/1)1()( +−=−
t/cctptgp
11
)|,()(
22
)(
q
mmmm cc De
yx
De
qmyxf
−
−−
++
−=
C
mmmmAem C =
− ,)(
)(1. Direct productivity:
2. Time p.d.f (Omori-Utsu):
3. Location p.d.f:
𝐴 =𝐾𝑐1−𝑝
𝑝−1= 0
∞ 𝐾
𝑡+𝑐 𝑝 𝑑𝑡
expected # of direct offspring from 𝑚𝑐
𝜆 𝑡 = 𝜇 + 𝐾
𝑖:𝑡𝑖<𝑡
𝑒𝛼(𝑚𝑖−𝑚𝑐)
𝑡 − 𝑡𝑖 + 𝑐 𝑝
Temporal version (Ogata 1988, JASA)
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What influence the estimate of the ETAS parameters
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What influence the estimate of the ETAS parameters
Missing links!
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What influence the estimate of the ETAS parameters: Missing links!
1. Missing links in space
target window
auxiliary window
x
y
Missing
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What influence the estimate of the ETAS parameters: Missing links!
2. Missing links in time
t
m
Starting time of catalog
Starting of target time interval
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What influence the estimate of the ETAS parameters: Missing links!
3. Missing links in magnitude
t
m
mc
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The influence of the above missing links has been studied in
➢ Sornette & Werner (2005) JGR, 110, B09303.
➢ Sornette & Werner (2005) JGR, 110, B08304.
➢ Wang, et al. (2010) BSSA, 100 , 1989 – 2001.
➢ Wang, Jackson & Zhuang. (2013) GRL., 37 , L21307.
➢ etc..
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What influence the estimate of the ETAS parameters
4. Missing links caused by short-term missing of aftershocks.
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What influence the estimate of the ETAS parameters
4. Missing links caused by short-term missing of aftershocks.
Missing events after the mainshock (from Omi etc, 2013)
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Previous studies for fixing the problems of short-term missing aftershocks
Observational approaches◆ waveform-based earthquake detection methods (e.g., Enescu et al.,
2007, 2009; Peng et al., 2007; Marsan and Enescu, 2012; Hainzl, 2016).
◆Energy based description (Sawazaki and Enescu, 2014)
Statistical approaches◆ (Ogata, Omi, Iwata) Bayesian, assuming GR relation for whole range
◆ (Marsan and Enescu, 2012) Assuming Omori-Utsu formula or ETAS model
◆This study: Conditional independence between magnitudes and occurrence times
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Application to the Kumamoto aftershock sequence data
Data Selection
Time: 2016/4/1~2016/4/21Mag.: 1.0+Depth: < 100 kmSpace: 128°-- 133°
30°-- 35°
2016-04-14 21:26 (130.81 32.74) M6.5 2016-04-15 00:03 (130.78 32.70) M6.42016-04-16 01:25 (130.76 32.75) M7.3
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Application to the recent Kumamoto aftershock sequence data
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Replenish the missing data
Observed data Biscale transformed
Estimated transformation under complete data
Transformed replenished data Original replenished data Cumulative freq. vs time
Replenished data
Obs. data
Method (Zhuang et al, 2016)
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Consider only the temporal ETAS model
𝐴 =𝐾𝑐1−𝑝
𝑝−1= 0
∞ 𝐾
𝑡+𝑐 𝑝 𝑑𝑡 : expected # of direct offspring from 𝑚𝑐
𝜆 𝑡 = 𝜇 + 𝐾
𝑖:𝑡𝑖<𝑡
𝑒𝛼(𝑚𝑖−𝑚𝑐)
𝑡 − 𝑡𝑖 + 𝑐 𝑝 (Ogata 1988, JASA)
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Influence of short-term aftershock missing on estimating the ETAS model
𝜇
𝑐 𝛼 𝑝
𝐾 𝐴
……Replenisheddata
……Observed data
𝐴 =𝐾𝑐1−𝑝
𝑝 − 1# of direct offspring from 𝑚𝑐
Swarm like
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2010年西部论坛Quiescence related to the ETAS model
1. Transformed Time sequence (Ogata, 1992, JGR)
0( )
it
i it u du → = If {ti} is the observation of a process determined by conditional intensity λ(t), the
{τi} is a standard Poisson process.
Relative quiescence
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Relative quiescence --- original dataset
M2+
2nd M6+shock
2nd M6+shock
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Relative quiescence --- Replenished dataset M1+
2nd M6+shock
2nd M6+shock
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Conclusions
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Future Researches
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Thank you for listening.