
Earthquake detection pipeline via efficient time series similarity search - aburan28
https://github.com/stanford-futuredata/FAST
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amelius
I think this is quite similar to how music fingerprinting (Shazam, etc.)
works, except it deals with multiple channels.

EDIT: Their paper confirms it: "FAST adapts a data mining algorithm,
originally designed to identify similar audio clips within large databases"

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chroem-
It's surprising that people don't rely more on automatic arrival time picking
+ simple outlier rejection. It's so much more efficient than these waveform
correlation approaches while being much simpler.

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googlethrow
Can you please elaborate or provide some pointers?

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chroem-
The FAST article mentions how using the ratio of short term seismic activity
to long term activity to detect seismic events is prone to false positives.
With this approach, you "pick" a discrete time when the seismic wave first
reaches a given station, and then using knowledge of the speed of sound in the
subsurface you perform nonlinear regression to locate the the point in time
and space that minimizes the travel time error. However, this gets tricky when
your algorithm picks the wrong arrival time, and picks a truck driving by
instead of the actual seismic wave. Now your estimate of when and where the
earthquake occurred has a substantial amount of error.

If you have enough stations for the seismic detection to be over defined (n >
4), one way around this is to consider _all_ spikes in seismic activity as
event picks, and then iteratively discard outlier picks until you're either
left with an earthquake detection that has a residual travel time error within
some acceptance threshold, or you run out of picks to make an over defined
solution. This approach ends up being very, very fast.

