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waveloc's Introduction

waveloc

Migration code for seismic event location. Unstable development version. The latest stable version is available on the Downloads page. The documentation is available here: http://amaggi.github.com/waveloc.

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waveloc's Issues

Add simple parallelization to processing

Simple parallelization will make waveloc run faster. The simplest task to start with is the waveform processing, as each waveform is processed independently of the others.

Rewrite to use unstructured grids

Using rectangular grids for search regions involves doing a lot of calculations that are actually unnecessary. The code would be speeded up significantly if an un-ordered sequence of search points were used instead. This upgrade would also make it possible to craft a seismicity zone that more closely matches the observed pattern.

rt-waveloc development already uses such an unstructured grid.

Optimize sliding window kurtosis

Set up a 2D array where each row is a window, and send to ss.kurtosos . To set up sliding window, try following code:

def window(seq, n=2):
    "Returns a sliding window (of width n) over data from the iterable"
    "   s -> (s0,s1,...s[n-1]), (s1,s2,...,sn), ...                   "
    it = iter(seq)
    result = tuple(islice(it, n))
    if len(result) == n:
        yield result    
    for elem in it:
        result = result[1:] + (elem,)
        yield result

Systematically sort after glob readings

glob.glob reads files sorted in alphabetical order on a mac, but not on a linux system. To avoid problems, systematically check that all glob.glob calls are followed by sort calls.

Add simple parallelization to migration

This may be a little tricky, but conceptually at least each point can be migrated on its own. Don't attempt this until the unstructured grids are in place.

refactor options.py

verification code in options.py is dreadful and poised to break - refactor it !!

Recursive kurtosis broken for variances != 1

Nadège reports that the recursive kurtosis gives values of -3 for gaussian data whose variance is not equal to 1.0.

[ ] Reproduce the error in a unit test
[ ] Look up better ways of estimating means and variances recursively
[ ] Test
[ ] Rerun

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