Sounds like a good idea. By how much does the density of the rocks
actually change?

On 9 March 2011 18:38, Geoffrey  wrote:
> I heard of an idea we used to use in Sonar to
> look at Seawater characteristics.
>
> Monitor the speed of sound through deep rocks
> and look for any speed changes over time
> and possibly see stresses as they actually build.
>
> I saw this idea on TV just a few days ago
> and we did they very same thing in Sea Water
> to understand how sound travels wherever we were.
>
> Sound Velocity is a physical characteristic fairly easy to measure.
>
>
> -----Original Message----- From: Dan Bolser
> Sent: Monday, March 07, 2011 11:39 PM
> To: PSNLIST@.............. ; Oliver Boyd ; Anyone? ; webmaster@........ ;
> leslie@........ ; tim@................... ; kent@........ ;
> james.gridley@........ ; John Taber ; admin@.........
> Subject: Representative stations (and IRIS station list)
>
> Hi all,
>
> I've been looking to collect a dataset of seismograms for an
> 'earthquake prediction competition' idea (see below for more
> information), and struggling quite a lot along the way!
>
> I'm looking for a list of 20 or so 'representative' stations from
> around the world with extensive and consistent records. If possible
> I'd like a list of station and network codes so I can collect data via
> BREQ FAST.
>
> Is there a concept of a representative station in the field? (I'm not
> an expert). Anyone want to go out on a limb and suggest such a list?
> ;-)
>
>
> As a first step, I thought I'd just get a list of stations (and
> network codes), however, the list I found here:
> http://neic.usgs.gov/neis/station_book/ seems to use 3-4 letter
> network codes (or blanks), while the BREQ FAST instructions here:
> http://www.iris.edu/manuals/breq_fast.htm suggests that I need two
> letter network codes (indeed, my test bfast request failed).
>
> For your interest, I scraped a "year / network / station" table from
> here: http://www.iris.edu/SeismiQuery/day_f.html - Please just let me
> know if you want that table in spreadsheet format. Using that I found
> 688 stations with data between 2002 and 2010.
>
> Other than suggested reading material:
> * http://cseptesting.org/experiments/RELM
> * http://www.iaspei.org/downloads/Ex_Sum_v5_THJ9_A4format.pdf
>
> I haven't received much feedback on the overall idea, which is
> presented below for your feedback, comments and criticism. Please just
> let me know if you'd like to help with this project, as currently I'm
> more or less stabbing in the dark!
>
> Faithfully,
> Dan.
>
>
> Dear colleagues,
>
> I'm trying to put together a dataset of seismograms for a community
> data prediction competition that would be hosted on Kaggle
> (http://www.kaggle.com/). The idea is to improve earthquake prediction
> methods and raise awareness of approaches, tools, data, charities and
> organizations like yours.
>
> I'm very inexperienced at handling seismograms, so I'm having
> difficulty deciding the form of the data to use and the actual data
> collection for the competition. I would greatly appreciate a) your
> feedback on my proposal, described briefly below, and b) help
> collecting the required data.
>
> I know this is a highly optimistic project, but in the words of the
> Kaggle website: "By exposing problems to a wide audience, with
> different participants trying different techniques, competitions can
> very quickly reach the frontier of what's possible for a given
> dataset."
>
> Thanks for any help or advice that you can provide.
>
>
> Yours faithfully,
> Dan Bolser.
>
>
>
> Here is the outline my naive plan:
>
> 1) Pick a set of (perhaps 20?) representative stations distributed
> around the world.
>
> 2) Pick around 1000 points in time at random for which all 20 stations
> have data, and collect 30 days (?) of seismic information from each
> station leading up to this point. This dataset will form the set of
> 'negatives' for training.
>
> 3) Pick around 1000 'events' at random for which all 20 stations have
> data, and collect 30 days (?) of seismic information from each station
> leading up to this 'event'. This dataset will form the set of
> 'positives' for training.
>
> 4) Repeat step 2 and 3 to generate a private testing set.
>
> 5) Host the competition on Kaggle (see,
> http://www.kaggle.com/About-Us/how-it-works).
>
>
> My ignorance of the field leads me to question weather the number of
> stations (20), number of events (1000) and number of days of seismic
> information (30) all look reasonable (assuming the overall scheme
> looks reasonable). Do any of these values look inappropriate or
> problematic? Can you suggest better values?
>
> Also, two other weaknesses / questions come to mind:
>
> 1) How should we define 'leading up to'? Current predictions are in
> the order of minutes, but it would be useful to predict hours, days or
> weeks in advance (if possible). Should we try to develop predictions
> (and therefore collect data) in all these ranges?
>
> 2) Should we try to predict event severity as a continuous numeric
> variable, or should we define a binary or some other ordinal scale? If
> so, where would you suggest we place the ranges?
>
>
> Here is my first working(!!!) BFAST request:
>
> .NAME Dan Bolser
> .INST NA
> .MAIL 8c Dalgleish Road
> .EMAIL dan.bolser@.........
> .PHONE +44 (0)1382 521 383
> .FAX
> .MEDIA FTP
> .ALTERNATE FTP
> .ALTERNATE FTP
> .LABEL Test request 03
> .QUALITY B
> .END
>
> PPNAF PN 2005 01 17 00 00 00.0000 2005 02 16 00 00 00.0000 1 BHZ
> MHD =A0 UU 2005 01 17 00 00 00.0000 2005 02 16 00 00 00.0000 1 BHZ
> HONU =A0UU 2005 01 17 00 00 00.0000 2005 02 16 00 00 00.0000 1 BHZ
> USIN =A0NM 2005 01 17 00 00 00.0000 2005 02 16 00 00 00.0000 1 BHZ
> PD11 =A0IM 2005 01 17 00 00 00.0000 2005 02 16 00 00 00.0000 1 BHZ
> JTS =A0 II 2005 01 17 00 00 00.0000 2005 02 16 00 00 00.0000 1 BHZ
> LBNH =A0US 2005 01 17 00 00 00.0000 2005 02 16 00 00 00.0000 1 BHZ
> CIA =A0 CI 2005 01 17 00 00 00.0000 2005 02 16 00 00 00.0000 1 BHZ
> __________________________________________________________
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