[OpendTect_Users] fault detection by attributes

[Friso Brouwer]

Dear Taizhong,

That is a non-trivial issue and it may be impossible achieve a complete
removal of stratigraphic features. I do not think the filtering of the
steering cube will make much of a difference, I advise to use the background
steering cube a defined in our standard
workflow<http://www.dgbes.com/images/stories/PDF/effectivedipsteeringworkflowusingbgsteering_primerodata.pdf>.


What could help, or at least mitigate your problem are the following
solutions, in order of complexity (try them in order) :

   1. Increase the time window of the similarity attribute, this will tune
   out vertical small features, which are often the stratigraphic features, not
   the faults.
   2. Do post-attribute filtering on the similarity volume, using the volume
   statistics.
   3. Use an completely other attribute for fault detection, curvature is a
   good candidate, here an
article<http://www.dgbes.com/images/stories/PDF/fault_attributes_niger_delta_aapg.pdf>
   .
   4. If the fault system and stratigraphic features have different
   directions in the horizontal plane, one can separate them using directional
   decomposition of the attribute. This is not trivial, and if this is an
   option for you, let me know, so I can explain you the details.
   5. Use a second attribute that detects the stratigraphic features, but
   not faults (e.g. a small window energy). Then use a cross-plotting approach
   to identify the domain in the crossplot where the faults are "clean" and use
   the mathematics attribute (conditional statements) to pass only attribute
   combinations within this domain.
   6. Extending on point 5 one can choose an user-driven multi-attribute
   neural network approach. This relies on there being a multiple attributes
   that each can partially separate faults and stratigraphic features. Based on
   user picked examples the neural network will then "learn" how to combine the
   different attributes to arrive at a more complete separation of faults and
   stratigraphic features. Here is an
article<http://www.dgbes.com/images/stories/PDF/eage_2000_meldahl_obj.pdf>and
product
   information <http://www.dgbes.com/index.php/neural-networks.html>.

All in all it remains a difficult problem as faults and stratigraphic
features often have there main characteristic/attributes, such as
discontinuity (similarity) and curvature, in common. Still with the above
methods you should be able to at least achieve an improvement. I gladly help
you with the further details if you choose to experiment with one of the
methods.

Kind regards,

Friso

-- 
Friso Brouwer
Support Americas
dGB Earth Sciences

+1 281 240 3939 (o)
support-americas at dgbes.com

On Thu, May 26, 2011 at 9:30 PM, Duan, Taizhong (MRO) <tduan at marathonoil.com
> wrote:

>  Hi all,
>
>
>
> What is the way to filter out all stratigraphic discontinuities in
> similarity calculation? The dip cube steering is supposed to do so? (bkg: I
> tried with dip steering cubes filtered with i-j stepout from 3-9, but it
> seems make not much difference, i.e., there is still significant
> stratigraphic discontinuities left on the volume. Of course I want to keep
> all non-vertical faults/fractures).
>
>
>
>  Anyone gets a better workflow?
>
> Thanks.
>
>
>
> Taizhong Duan
>
>
>
> Marathon Oil
>
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