Volume 7 Number 7 (Jul. 2012)
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JSW 2012 Vol.7(7): 1625-1632 ISSN: 1796-217X
doi: 10.4304/jsw.7.7.1625-1632

Hunan University of science and technology / Physical Education Institute, Xiangtan, China

Wenqin Li and Daqing Wang
College of Mathematics & Computer Science, Yangtze Normal University, Fuling, Chongqing, China

Abstract—With the fast development of Visualization Technology, massive data need to be converted into intuitive graphics in more and more areas, such as in oil and geological exploration fields. In order to achieve highly efficient visualization of 2D seismic data, the following important methods are employed to improve the speed of rendering. The first method is to process massive data in point coordinate (x, y) forms. The second method is to draw local area of graphics by figuring out what points need to be painted for that graphics of massive data that sometimes can’t be shown completely on a displayed screen, which results in a high rate of drawing and less usage of memory. The third method is to pick out a sparsing algorithm by KNeighboring in order to see the whole picture of massive 2D data or to zoom out the whole picture to guarantee the quality of pictures. And sometimes processors need to view detailed graphics of a region of 2D seimic data, then it proposed bilinear interpolation algorithm. Overall, visualization system of massive 2D seismic data presented in this paper is based on the methods proposed above and uses Qt as a development language. Finally, high efficiency is achieved by drawing local area data and bitmap-cache mechanism when massive 2D data need to be displayed on more screens; and also high speed is obtained to render by operating 2D graphics such as by zooming out through KNeighboring sparsing algorithm.

Index Terms—Massive 2D seismic data, local area data, sparsing algorithm, bilinear interpolation algorithm, Qt.

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Cite: Wenqin Li and Daqing Wang, "Visualization System of Massive 2D Seismic Data," Journal of Software vol. 7, no. 7, pp. 1625-1632, 2012.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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