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 <[email protected]> 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" <[email protected]> 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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<<attachment: velocity_profile.png>>

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