Seismic model estimation using particle-swarm optimization
- Bo Liu (King Fahd University of Petroleum and Minerals (KRUPM)) | M. Mohandes (King Fahd University of Petroleum and Minerals (KRUPM)) | Hilal Nuha (King Fahd University of Petroleum and Minerals (KRUPM))
- Document ID
- Society of Exploration Geophysicists
- 2018 SEG International Exposition and Annual Meeting, 14-19 October, Anaheim, California, USA
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Exploration Geophysicists
- Modeling, Compressional, Data reconstruction, Acquisition, 2D
- 0 in the last 30 days
- 15 since 2007
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Modern seismic data surveys generate terabytes of data daily leading to a significant increase of the cost for storage and transmission. Therefore, it is desired to compress seismic data. In this work, we propose a model-based compression scheme to deal with the large data volume. First, each seismic trace is modeled as a superposition of multiple exponentially decaying sinusoidal waves (EDSWs). Each EDSW represents a model component and is defined by a set of parameters. Secondly, a parameter estimation algorithm for this model is proposed using Particle Swarm Optimization (PSO) technique. In the proposed algorithm, the parameters of each EDSW are estimated sequentially wave by wave. A suitable number of model components for each trace is determined according to the level of the residuals energy. The proposed model based compression scheme is then experimentally compared with the discrete Cosine transform (DCT) on a real seismic data. The proposed model based algorithm outperforms the DCT in term of compression ratio and reconstruction quality.
Presentation Date: Tuesday, October 16, 2018
Start Time: 1:50:00 PM
Location: Poster Station 20
Presentation Type: Poster
|File Size||357 KB||Number of Pages||5|
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