Multiple Attenuation
 
  This section contains our multiple attenuation tools.  They are listed in
their functional category with some appearing in more than one [qdchop].
 
  Interpretive Techniques are where you as the user wish to discriminate
against one or more events in your dataset.  Tools listed there allow you
to define and remove very select events. 
 
  Model Driven Techniques are where the user defines the data in terms of a
model [time,velocity,dip ... etc.] which is used to provide prarmeters to
the operative routines.  This is distinct from looking at the data and
deriving these same parameters.  With this selection of routines you
determine the nature of your data based on your model of the data. 
 
  Statistical Techniques make use of the volume of periodic information in
the dataset to derive parameters to enhance or discriminate against those
events. Here the user is not envoking any particular model of the data but
instead allowing the data to determine the effectiveness of the process. 
 
  Transform Techniques are a combination of Statistical and Model Driven
techniques in that they take advantage of the statistics of the data as well
as make use of a model of the data inherent in the transform.  These
techniques operate in other than (t,x) space. 
 
  For a more detailed explanation of these processes consider enrolling in
our Noise and Multiple Suppression class [P324] taught 3 times a year at
the Exploration Training Center in Houston. 
