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. For technical support, please contact us at We apologize for any inconvenience.
By Topic

A massively parallel implementation of the common azimuth pre-stack depth migration

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 $31
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)
Calandra, H. ; Total Exploration and Production Division, Avenue Larribau, 6400 Pau, France ; Bothorel, F. ; Vezolle, P.

When accompanied by the appropriate algorithmic approach, seismic imaging is an application that can take advantage of massively parallel computer systems. Three-dimensional (3D) pre-stack time migration (PSTM) and pre-stack depth migration (PSDM) are key components of seismic imaging and require very large computing resources. In this paper, we show that execution of these algorithms can be dramatically accelerated by massive parallelism. Many oil exploration and service companies purchase supercomputing clusters for performing 3D PSTM and PSDM seismic imaging. The common azimuth migration (CAM) algorithm, ported to many architectures, is particularly well suited to offshore marine applications. This paper describes the porting of the CAM algorithm to the IBM Blue Gene/L™ supercomputer, which requires introducing a second level of parallelism, building a parallel 3D-FFT (fast Fourier transform) routine, optimizing a tri-diagonal solver for SIMD (single-instruction, multiple-data) floating-point units, and addressing various I/O concerns. We present results obtained by using up to 16,368 processors for actual data provided from a marine seismic acquisition. Finally, we provide recommendations for porting other pre-stack algorithms to a massively parallel environment.

Note: The Institute of Electrical and Electronics Engineers, Incorporated is distributing this Article with permission of the International Business Machines Corporation (IBM) who is the exclusive owner. The recipient of this Article may not assign, sublicense, lease, rent or otherwise transfer, reproduce, prepare derivative works, publicly display or perform, or distribute the Article.  

Published in:

IBM Journal of Research and Development  (Volume:52 ,  Issue: 1.2 )