Diffusion of epicenter of earthquake aftershock, Omori’s law, and generalized continuous-time random walk models [Helmstetter & Sornette, 2002b] 2017.5.29 So Ozawa (ERI, Hatano Lab, M1) 2017/5/29 Seismogenesis Seminar 1
In these series of paper, authors derive many of empirical laws of earthquake by ETAS model. l Sornette and Sornette, 1999 l Helmstetter and Sornette, 2002a l Sornette and Helmstetter, 2002 In this paper [Helmstetter and Sornette, 2002b], we investigate aftershock diffusion . 2017/5/29 Seismogenesis Seminar 2
Aftershock diffusion • from 1 km/h to 1 km/year • Not universally observed Why diffuse ? Mogi, 1968 2017/5/29 Seismogenesis Seminar 3
Why diffuse ? ・ Viscous relaxation process ( Rydelek and Sacks, 2001) ・ Fluid transfer (Noir et al, 1997 , Nur and Booker, 1972, Hudnut et al, 1989) ・ Rate and State friction’s law and non-uniform stress ( Dieterich, 1994) ・ Cascade process : Large aftershocks reproduce their secondary aftershocks close to them. ( this paper) 2017/5/29 Seismogenesis Seminar 4
Flow 2. The ETAS model Formulate ETAS model and refer the property of the model. Numerical simulation. 3. Mapping of the ETAS model on the CTRW model Derive the master equation of ETAS. Establish a correspondence between the ETAS model and the CTRW (Continuous Time Random Walk model). 4. critical regime n=1 Derive the joint probability distribution N(t,r) Calculate the average distance between mainshock and its aftershock R as a power law function of elapsed time. (R~t^H) 5. New Question on Aftershocks derived from the CTRW Analogy 6. Discussion Summarize result of different regime Comparison to related study 7. Conclusion 2017/5/29 Seismogenesis Seminar 5
2. The ETAS model Formulate ETAS model and refer the property of the model. Numerical simulation. 2017/5/29 Seismogenesis Seminar 6
ETAS Model � � r � i � � � � m i � � � t � t i � � � r � � r � i � . � m i � t � t i , r 𝑛 " ∶ magnitude 𝑠 " ∶ positon 𝑢 " ∶ time ‘bare propagator’ = seismic rate directly induced by a single ‘mother’ i (1) Large earthquake reproduce many aftershocks. � � m i � � K 10 � ( m i � m 0 ) , (2) Normalized waiting time distribution = ‘bare’ omori’s law � c � � � t � � � t � c � 1 � � H � t � , 𝜄 > 0, 𝐼 𝑢 is Heaviside function (3) Normalized spatial ‘jump’ distribution = isotropic elastic Green function dependence � � � � d � d � 1 � � � r 1 � � , � � � r 𝜈 > 0 2017/5/29 Seismogenesis Seminar 7
𝛽 and b event-size distribution = GR law number of daughter � � m i � � K 10 � ( m i � m 0 ) , (6) (3) P � m � � b ln � 10 � 10 � b ( m � m 0 ) , value usually close to 1. is 𝛽 > 𝑐 : large event dominate earthquake triggering 𝛽 < 𝑐 : small event dominate earthquake triggering recent reanalysis of seismic catalogs indicates 𝛽 < 𝑐 and 𝛽 =0.8 (Helmstetter, 2003) but case of 𝛽 >0.5 is difficult to analyze (infinite variance 𝜍(𝑛) ) therefore our model uses 𝑐 = 1, 𝛽 =0.5 2017/5/29 Seismogenesis Seminar 8
branching ratio n (Helmstetter & Sornette, 2002a) n : average # of daughter created per mother event (summed by all possible magnitude) n � � dr � � dt � � � � � � � r � i � dm i P � m i � � m i � t � t i , r t i m 0 � � Kb � � dm i P � m i � � � m i � � b � � , m 0 due to cascades of aftershocks, total # of event is larger by the factor 1/(1-n) ~ 10 → n is a branching parameter n < 1 : subcritical regime (finally die out) n > 1 : supercritical regime (exponentially increase) n = 1 : critical regime (border between birth and death) 2017/5/29 Seismogenesis Seminar 9
n is branching parameter all aftershock PHYSICAL REVIEW E 66 , 061104 � 2002 � 𝑞 = 1 − 𝜄 n=1.0003 n=1 direct aftershock 𝑞 = 1 + 𝜄 n=0.9997 with 0.001 origin fixed t* s the t * � c � � 1 � n � � 1/ � characteristic time n � � 1 � � � , (1) t<t*, all regime behave identically 2017/5/29 Seismogenesis Seminar 10
Numerical simulation : method (Ogata, 1998 & 1999) • initial condition t=0 r=0 M7 event occur • algorithm decide time of next event by nonstationary poisson process (8) � c � � � t � � � t i � t K 10 � ( m i � m 0 ) � t � t i � c � 1 � � , → decide magnitude by GR law ( )/ , and and are the times and → select mother in all preceding events by (2) � � r � i � � � � m i � � � t � t i � � � r � � r � i � . � m i � t � t i , r → decide location of new event by (5) � � � � d � d � 1 � � � r 1 � � , � � � r • parameter set 𝜄 = 0.2, 𝑐 = 1, 𝛽 = 0.5, 𝑜 = 1, 𝜈 = 1, 𝑛 S = 0, 𝑒 = 1km, 𝑑 = 0.001day 2017/5/29 Seismogenesis Seminar 11
Numerical simulation : Result 30-70 years 0-0.3 day FIG. 1. Maps of seismicity generated by the ETAS model with considerable diffusion occurs 2017/5/29 Seismogenesis Seminar 12
[30,70] yrs : fractal distribution correlation dimension D ~1.5 [0,70]yrs : D ~1.85 [7,70]yrs : D ~1.7 reported active fault system: D = [1.65:1.95] 2017/5/29 Seismogenesis Seminar 13
3. Mapping of the ETAS model on the CTRW model Derive the master equation of ETAS. Establish a correspondence between the ETAS model and the CTRW (Continuous Time Random Walk model). 2017/5/29 Seismogenesis Seminar 14
From direct Omori’s law To renormalized Omori’s law direct Omori law mother ( 𝑛 " , 𝑠 " , 𝑢 " ) � � r � i � � � � m i → m � � � t � t i � � � r � � r � i � , � m i → m � t � t i , r The ETAS model � � r � i ) at � m i → m ( t � t i , r � � m i → m � � n ln � 10 �� b � � � 10 � ( m i � m 0 ) 10 � b ( m � m 0 ) . � 11 of magnitude m m daughter (𝑛, 𝑠, 𝑢) renormalized Omori law source term (mainshock must occur at t = 0) � , m � � � � t � � � m � M � � � r � � , S � t , r 𝑂 W (𝑠, 𝑢) � , m � � � d � r � � � � � � S � t , r N m � t , r dm � m 0 � � t � � r � � � N m � � � , r � � � . � d �� m � → m � t � � , r (17) 0 # of event by cascade process convolution 2017/5/29 Seismogenesis Seminar 15
� , m � � � d � r � � � � � � S � t , r N m � t , r dm � (17) m 0 � � t � � r � � � N m � � � , r � � � . � d �� m � → m � t � � , r 0 assumption : daughter’s magnitude is independent of its mother (GR preserved all time. It is adequate only if 𝛽 ≤ 𝑐/2 ) 𝑂 W (𝑢, 𝑠) = 𝑄(𝑛)𝑂(𝑢, 𝑠) for 𝑢 > 0 magnitude m vanishes � � � � dr � � � t � � � S M � t , r � � r � � � N � � , r � � � , N � t , r d �� � t � � , r 0 � 18 � t � 0, � � � � � r � � � t � � � M � / n , S M � t , r Master Equation of ETAS = renormalized Omori’s law 𝑂 𝑢, 𝑠 = 𝐹 𝜇 𝑢 Φ 𝑠 ∶ Expectation value 1 st moment 2017/5/29 Seismogenesis Seminar 16
Continuous time random walk model (Montroll & Weiss, 1965) • generalization of naïve Random Walk model continuous distribution 𝜚(𝑠, 𝑢) of spatial step (jump length) and time step (wating time) • master equation of CTRW is identical to ETAS � � � � dr � � � t � � � S M � t , r � � r � � � N � � , r � � � , d �� � t � � , r N � t , r 0 t � 0, � 18 � A) N(t, r) : PDF for the random walker to Just arrive at r at t. B) S e (t, r) : initial condition of random walk, C) integral on (18) denote superposition of all possible paths just having arrived at r at t, weighted by a transfer function 𝜚 • Therefore we can borrow the deep knowledge of CTRW for the understanding Earthquake clustering. 2017/5/29 Seismogenesis Seminar 17
• N and W 𝑂(𝑢, 𝑠) : PDF of just arriving at position r at time t 𝑋(𝑢, 𝑠) : PDF of being at position r at time t � � � � dr � � � dt � � 1 � � t � t � dt � � � t � � � N � t � , r t � � � � t � � � S M � t , r � � r � � � N � � , r � � � , � � . N � t , r d �� � t � � , r W � t , r 0 0 0 (18) (19) • using Laplace-Fourier transform ˆ M � � , k � � ˆ � � � S 1 � � ˆ � � , k � � � N , ˆ � � , k � � � ˆ � � , k � � . W N ˆ � � � � ˆ � k � � 1 � n � � (20) (21) • CTRW models transport phenomena in heterogeneous media. considering earthquake as transport of stress in heterogeneous crust, correspondence between ETAS and CTRW is natural ? 2017/5/29 Seismogenesis Seminar 18
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