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.
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.
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. |
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. |