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# System prepended metadata

title: 書報討論 新震模信息法與地震群湯模型之研究

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# 書報討論 新震模信息法與地震群湯模型之研究
> [name=TingKai Kuo] [time=Mon, Mar 18, 2019 14:10 PM] [color=#907bf7]

## SOG (soup of group)
### grained media
### clustering and splitting
- $v_coal$ coalition rate
- $v_frag$ fragment rate
- terrorism event (Bohorquez, 2009)
    - idea: 2d ARCH
- Number of clusters N
    - $n_s$ : # of size-s cluster
- simulation
    - steady state (all n_s constant)
    - $n_s \sim s^{-5/2}$
- Magnitude (M)
    - logE = 5.24+1.44M (Bath, 1966)
- Intensity (~ acceleration)
- logN = a - bM (Gutenberg-Richter relation)
    - turning point $M_c$
- SOC and Earthquake
    - Fault system
    - power law F(m>M)~$10^{-bM}$
    - F(e>E) ~ $E^{-b}$
    - p(e) ~ $e^{-2}$
    - $S_max$個小單元可造成 $M_max$ 
    - exist S cluster cause M 
    - 剩下的小單元所能產生的最大地震規模 $M_1 = 1og_{10}(10^{M_{max}}-10^M)$
    - spacing time of earthquake

## Convensional method
### Pattern Informatics of Nonlinear threshold system
- Karhunen-Loeve expension
- Rundle et al., 2000a (Fault in Carlifornia)
- 將空間上的錯動轉化成矩陣表示(use KLE)
    - use state vector to represent the phase change of earthquake activity
    - machanism
        - $\delta$應力 -> $\delta$相位 > high value > prediction
- Critical transition
    - locking to slide
    - 不穩定均衡
        - 恢復力下降使得變異度增加
    - calculation
        - grid 0.1deg x 0.1 deg (~ 10km x 10km)
        - trancated scale: Mc
        - time parameter
            - 孕震時間
        - 變化區間 預測區間
        - reference time $t_b$ 
- Demonstration
- Receptor operating characteristics (ROC, maching learning common used) (logorithmic scale)
    - Top 100 value spatial distribution
    -  AUC(area under curve)

- novel earthquake modeling informatics
    - conventional + SOG
    - Scaled segmentic choosing 分不同的規模段
        - scale window
        - stride
        - joint probability
- Data
    - Central Weather Bureau 地震目錄
    - Depth distribution plot
        - <= 25 km (淺層 90%)
        - M >= 5.5
        - 1984/1/31 ~2016/12/31

- absolute PI value (normalized by the total spatial-temporal maximum)

- Pros and Cons
    - Pros
        - need not to know underground structure
    - Cons
        -  spatial-temporal accuracy
        -  data-intensed