CONCLUSIONS

In order to see the effect of data uncertainty upon model uncertainty, we must have a good understanding of the properties of our noise and the accuracy of our modeling and regularization operators. If we are able to make reasonable estimates on these properties we can produce multiple, equi-probable estimates. This methodology shows great promise in fields like velocity analysis where we understand the errors in our data but its complex interaction with the model makes inferring model uncertainty difficult. Preliminary work using a simple RMS to interval velocity estimation shows promise.