Academia.eduAcademia.edu

Figure 5 – uploaded by Junchao Xia

See full PDFdownloadDownload figure
FIG. 5: The mean displacement <u> (slip) of the blocks as a function of the number of blocks s in an event. (Each block is counted once even if it fails multiple times.)

Figure 5 The mean displacement <u> (slip) of the blocks as a function of the number of blocks s in an event. (Each block is counted once even if it fails multiple times.)

Related Figures (4)

FIG. 1: Distribution of the moment M of the events P(M). For a_= 2.5 and R = 1 the slope b & 2, consistent with Ref. 5}; b does not change significantly as R increases. For a = 0.5 the scaling range becomes better defined as R in- creases and the slope converges to b+ 1.5. Fora =0,b 1.5 for R = 100; there is no power law behavior for R = 1 and small a. All plots have been displaced vertically for clarity. R = 100, b appears to approach 1.5 as R increases, and there are fewer characteristic earthquakes (see Fig.[1(b)p. The results in Fig. [I(c)] for a = 0 are consistent with the ong-range CA models for which b = 3/2. The distribution of events, P(s), does not exhibit power law behavior for R = 1 and a = 2.5, consistent with Ref. 5, but does so for larger R with P(s) ~ s~° and b © 2 (see Fig. 2). For small a, b = 1.5 for R = 100.
FIG. 1: Distribution of the moment M of the events P(M). For a_= 2.5 and R = 1 the slope b & 2, consistent with Ref. 5}; b does not change significantly as R increases. For a = 0.5 the scaling range becomes better defined as R in- creases and the slope converges to b+ 1.5. Fora =0,b 1.5 for R = 100; there is no power law behavior for R = 1 and small a. All plots have been displaced vertically for clarity. R = 100, b appears to approach 1.5 as R increases, and there are fewer characteristic earthquakes (see Fig.[1(b)p. The results in Fig. [I(c)] for a = 0 are consistent with the ong-range CA models for which b = 3/2. The distribution of events, P(s), does not exhibit power law behavior for R = 1 and a = 2.5, consistent with Ref. 5, but does so for larger R with P(s) ~ s~° and b © 2 (see Fig. 2). For small a, b = 1.5 for R = 100.
FIG. 2: Distribution of events of size s, P(s). P(s) does not exhibit power law behavior for a = 2.5 and R = 1, but does so for larger R with b ~ 2. For a = 0, P(s) ~ s~? with b 1.5 for R = 100. Note that s can exceed N because a block can fail multiple times during an event.
FIG. 2: Distribution of events of size s, P(s). P(s) does not exhibit power law behavior for a = 2.5 and R = 1, but does so for larger R with b ~ 2. For a = 0, P(s) ~ s~? with b 1.5 for R = 100. Note that s can exceed N because a block can fail multiple times during an event.
FIG. 3: The inverse stress metric versus the number of loading times. Note the different vertical scales. The system appears ergodic for a = 2.5 and R = 1, but is nonergodic for larger values of R. For a = 0.5, the slope of Q;*(t) becomes larger as R increases. For a = 0.5 and R = 100, the system exhibits punctuated ergodicity similar to the behavior of the long- range CA model.
FIG. 3: The inverse stress metric versus the number of loading times. Note the different vertical scales. The system appears ergodic for a = 2.5 and R = 1, but is nonergodic for larger values of R. For a = 0.5, the slope of Q;*(t) becomes larger as R increases. For a = 0.5 and R = 100, the system exhibits punctuated ergodicity similar to the behavior of the long- range CA model.
FIG. 4: The mean stress <f> as a function of the number of loading times for R = 500. Note the fluctuating behavior for a@ = 0.5 and the quasiperiodic behavior for a = 2.5.
FIG. 4: The mean stress <f> as a function of the number of loading times for R = 500. Note the fluctuating behavior for a@ = 0.5 and the quasiperiodic behavior for a = 2.5.
Related topics:
AlgorithmsNonlinear Dynamics and ChaosQuantum and classical statistical mechanicsStress Transfer
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved