Here is the data that I would be working with:
No Thickness Depth Vp Vs
(m) (m) (m/s) (m/s)
1, 2.00, 2.00, 480.00, 180.00
2, 8.00, 10.00, 2320.00, 700.00
3, 8.00, 18.00, 2980.00, 1150.00
4, 52.00, 70.00, 2980.00, 1720.00
5, -----, -----, 3120.00, 1870.00
For convenience, here is a gist of summarizing the problem as well as
potential data and scripts to read the data:
https://gist.github.com/arkottke/3897f35ec5049a13240a
Albert
On Thu, Mar 14, 2013 at 2:36 PM, Ronnie Ghose <ronnie.gh...@gmail.com>wrote:
> I think anything is ok ....?
>
>
> On Thu, Mar 14, 2013 at 5:33 PM, Albert Kottke <albert.kot...@gmail.com>wrote:
>
>> Sure. It might take me a little time to put together. I could do a
>> collection of CSV files, or just do a gzipped json file of a Python
>> dict.
>>
>> Albert
>>
>> On Thu, Mar 14, 2013 at 2:29 PM, Ronnie Ghose <ronnie.gh...@gmail.com>
>> wrote:
>> > Could you release a part of your data? / Similar data?
>> >
>> >
>> > On Thu, Mar 14, 2013 at 5:26 PM, Albert Kottke <albert.kot...@gmail.com
>> >
>> > wrote:
>> >>
>> >> I sent this email earlier, but that attachment exceeded the attachment
>> >> limit so I am linking the attachment.
>> >>
>> >> http://i.imgur.com/dHida3t.png
>> >>
>> >> Attached is a figure showing a collection of velocity profiles across
>> >> a region. The goal would be to group each of theses curves into
>> >> groups with similar characteristics
>> >>
>> >> My idea is to group the entire profile for two reasons:
>> >> 1) The goal is to randomly generate similar profiles.
>> >> 2) The layering sequence within a profile is dependent on the
>> >> geologic conditions. So the relationship between adjacent layers would
>> >> be different for different groups, and would require more variables to
>> >> describe.
>> >> If you clustered using thickness and velocity, then you would
>> >> also have to consider depth as well.
>> >>
>> >> On Thu, Mar 14, 2013 at 1:09 PM, Albert Kottke <
>> albert.kot...@gmail.com>
>> >> wrote:
>> >> > Attached is a figure showing a collection of velocity profiles across
>> >> > a region. The goal would be to group each of theses curves into
>> >> > groups with similar characteristics
>> >> >
>> >> > My idea is to group the entire profile for two reasons:
>> >> > 1) The goal is to randomly generate similar profiles.
>> >> > 2) The layering sequence within a profile is dependent on the
>> >> > geologic conditions. So the relationship between adjacent layers
>> would
>> >> > be different for different groups, and would require more variables
>> to
>> >> > describe.
>> >> > I think if you clustered using thickness and velocity, then you would
>> >> > also have to consider depth as well.
>> >> >
>> >> > Albert
>> >> >
>> >> >
>> >> > On Thu, Mar 14, 2013 at 1:01 PM, Ronnie Ghose <
>> ronnie.gh...@gmail.com>
>> >> > wrote:
>> >> >> What would be wrong with clustering via the thickness and velocity
>> of
>> >> >> each
>> >> >> layer as features?
>> >> >>
>> >> >> On Mar 14, 2013 3:31 PM, "Albert Kottke" <albert.kot...@gmail.com>
>> >> >> wrote:
>> >> >>>
>> >> >>> I am a novice at machine learning, so pardon my ignorance.
>> >> >>>
>> >> >>> I have ~3000 velocity profiles, which consist of multiple layers
>> >> >>> defined
>> >> >>> by a thickness and velocity. My goal is cluster these profiles
>> into
>> >> >>> ~10
>> >> >>> groups with similar characteristics. All of the examples that I
>> have
>> >> >>> seen
>> >> >>> on the scikits-learn website involve simpler datasets, so I am not
>> >> >>> sure that
>> >> >>> solving this is possible with machine learning.
>> >> >>>
>> >> >>> Is this a problem that could be solved with machine learning? Is
>> there
>> >> >>> an
>> >> >>> example or a good reference to look at? Any general
>> recommendations?
>> >> >>>
>> >> >>>
>> >> >>> Thanks for your help,
>> >> >>>
>> >> >>> Albert
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>>
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