As () points out, the selection of the cooling
function is very important and can greatly speed convergence.
In my method, I treat the sample interval function as a cooling function.
To find a good cooling function, I first created a linear cooling
function and plotted how the energy decreased as a function of
sample interval.
This is shown in Figure
.
In this particular example, there appear to be three sample intervals
associated with large drops in energy: 185, 130, and 70.
I decided to create a cooling schedule that treats the sized 70
sample interval as the critical temperature.
This is shown in Figure
.
The critical temperature is where convergence is most significant.
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.
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Notice that in Figure
, the energy drops off
much quicker than in Figure
and therefore
requires fewer iterations.
Figure
shows the result of applying the cooling
schedule in Figure
.
It has converged to the desired event.
Figure
shows the application of the calculated
displacements to the left side of the fault.
Its results are about the same quality as Figure
,
which shows the results of applying the actual known displacement to
the left side of the fault.
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|
makefinalmod
Figure 15 Applied result, the ``model'' in the center shows the result of applying the calculated displacement to the left side of the fault. | ![]() |
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makefinalideal
Figure 16 Ideal applied result, the ``ideal'' in the center shows the result of applying the known displacement to the left side of the fault. | ![]() |