Can survival analysis be used to predict earthquakes? Given survival analysis relates to an analysis/prediction of time to an event, I was wondering if it was possible to be used to predict eathquakes. If so, how would one go about carrying out that analysis on say one particular region say Japan or California?
So I guess a question related to that say...for example if we took a univariate time-series based dataset looking at the tremors over time in Japan, could this be converted in a manner that would applicable to be useable for survival analysis... this is assuming that potentially prior small tremors might be indicative of a much larger earthquake to come.
 A: No.
Survival analysis is used to summarize the survival times (or times to any event that can only happen once) of a bunch of people. And, of course, the survival curve of two groups (or two treatments) can be compared. So a survival curve can show that certain percentage of a certain group of people have died within a certain time after a defined starting period. Say that 30% of men between 50 and 60 years old with a certain kind of tumor being treated with a certain protocol  will have died within five years. 
The key point is that the event - death in this case - can only happen once to each person, and we are tracking lots of people. I don't see how the earthquake example fits this mindset.  
A: Yes, survival analysis may be used to model earthquake data, but perhaps not in the way you originally envisioned. 
Specifically you can trade out time-to-death (survival) for another interval variable, and it need not be time-based.  It could be a set of financial thresholds, losses on an insurance policy, or perhaps the max magnitude of an earthquake experienced for a particular region (the studied unit in this example) between 2000-2010. 
For n = 100 measurement regions, grouped by richter scale band: 
9.0+:        1 
8.0-8.9:     2 
7.0-7.9:     6 
6.0-6.9:     9 
etc……           
<2.0:         40 
From there all of the standard statistics apply:  Kaplan-Meier estimator, Nelson-Aelan hazard rate, their variance approximations, cumulative survival rates, conditional 'survival' analyses, etc.  And assuming you have appropriate covariates then Cox regression as well. 
Not sure this is the best case to use or a great example.  But such survival analysis techniques are sometimes employed in insurance, particularly for cases where there's notable truncation, censoring, or grouping of the data you have to work with (though given the ease of capturing data these days this is less of an issue).   
