Open AccessComparative analysis of matching pursuit algorithms for Kirchhoff migration on compressed data
Author(s) -
Carlos Fajardo,
Fabián Cabrera Sánchez,
Ana B. Ramirez
Publication year - 2021
Publication title -
c.t. and f ciencia, tecnología, futuro/ctandf ciencia, tecnología y tuturo
Language(s) - English
Resource type - Journals
eISSN - 2382-4581
pISSN - 0122-5383
DOI - 10.29047/01225383.142
Subject(s) - bottleneck , terabyte , seismic migration , computer science , matching pursuit , algorithm , compressed sensing , parallel computing , process (computing) , matching (statistics) , data compression , speedup , operator (biology) , theoretical computer science , computer engineering , mathematics , geology , seismology , biochemistry , statistics , chemistry , repressor , transcription factor , gene , embedded system , operating system
Currently, the amount of recorded data in a seismic survey is in the order of hundreds of Terabytes. The processing of such amount of data implies significant computational challenges. One of them is the I/O bottleneck between the main memory and the node memory. This bottleneck results from the fact that the disk memory access speed is thousands-fold slower than the processing speed of the co-processors (eg. GPUs). We propose a special Kirchhoff migration that develops the migration process over compressed data. The seismic data is compressed by using three well-known Matching Pursuit algorithms. Our approach seeks to reduce the number of memory accesses to the disk required by the Kirchhoff operator and to add more mathematical operations to the traditional Kirchhoff migration. Thus, we change slow operations (memory access) for fast operations (math operations). Experimental results show that the proposed method preserves, to a large extent, the seismic attributes of the image for a compression ratio up to 20:1.