Scheduled System Maintenance on May 29th, 2015:
IEEE Xplore will be upgraded between 11:00 AM and 10:00 PM EDT. During this time there may be intermittent impact on performance. We apologize for any inconvenience.
By Topic

Lossless seismic data compression using adaptive linear prediction

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Mandyam, G. ; Dept. of Electr. & Comput. Eng., New Mexico Univ., Albuquerque, NM, USA ; Magotra, Neeraj ; McCoy, W.

This paper presents a comparison of adaptive linear predictors as applied to the area of lossless compression of seismic waveform data. Three methods are explored: the normalize least-mean square (NLMS) algorithm, the gradient adaptive lattice (GAL) algorithm, and the recursive least squares lattice (RLSL) algorithm. When compared to standard linear prediction techniques, all three of these methods require little overhead, are more computationally efficient, and can be implemented using floating point techniques. With respect to a standard seismic database, the RLSL filter outperforms the other two methods in nearly all cases tested

Published in:

Geoscience and Remote Sensing Symposium, 1996. IGARSS '96. 'Remote Sensing for a Sustainable Future.', International  (Volume:2 )

Date of Conference:

27-31 May 1996