A multi tone modeling for seismic data compression

Bo Liu; M. Mohandes; H. Nuha; M. Deriche; Naveed Iqbal; Faramarz Fekri; James H. McClellan

doi:10.1190/segam2019-3208024.1

A multi tone modeling for seismic data compression

Bo Liu
, M. Mohandes
, H. Nuha
, M. Deriche
, Naveed Iqbal
, Faramarz Fekri
, James H. McClellan

King Fahd University of Petroleum and Minerals
Georgia Institute of Technology

Research output: Contribution to conference › Paper › peer-review

3 Scopus citations

Abstract

This work develops a multi tone modeling for seismic data compression. First, seismic traces are described as multitone decaying sinusoidal waves superposition. Each sinusoidal wave is regarded as a model component and is represented by a set of distinct parameters. Secondly, a novel parameter estimation algorithm for this model is proposed accordingly. In this algorithm, the parameters are estimated for each component sequentially. A suitable number of model components is determined by the level of the residuals energy. The proposed model-based compression scheme outperforms the Linear Predictive Coding (LPC) both analytically and experimentally in terms of compression ratio, reconstruction quality and convergence rate.

Original language	English
Pages	263-267
Number of pages	5
DOIs	https://doi.org/10.1190/segam2019-3208024.1
State	Published - 2020
Externally published	Yes
Event	Society of Exploration Geophysicists International Exposition and Annual Meeting 2019, SEG 2019 - San Antonio, United States Duration: 15 Sep 2019 → 20 Sep 2019

Conference

Conference	Society of Exploration Geophysicists International Exposition and Annual Meeting 2019, SEG 2019
Country/Territory	United States
City	San Antonio
Period	15/09/19 → 20/09/19

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10.1190/segam2019-3208024.1

Cite this

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title = "A multi tone modeling for seismic data compression",

abstract = "This work develops a multi tone modeling for seismic data compression. First, seismic traces are described as multitone decaying sinusoidal waves superposition. Each sinusoidal wave is regarded as a model component and is represented by a set of distinct parameters. Secondly, a novel parameter estimation algorithm for this model is proposed accordingly. In this algorithm, the parameters are estimated for each component sequentially. A suitable number of model components is determined by the level of the residuals energy. The proposed model-based compression scheme outperforms the Linear Predictive Coding (LPC) both analytically and experimentally in terms of compression ratio, reconstruction quality and convergence rate.",

author = "Bo Liu and M. Mohandes and H. Nuha and M. Deriche and Naveed Iqbal and Faramarz Fekri and McClellan, \{James H.\}",

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AU - Liu, Bo

AU - Mohandes, M.

AU - Nuha, H.

AU - Deriche, M.

AU - Iqbal, Naveed

AU - Fekri, Faramarz

AU - McClellan, James H.

PY - 2020

Y1 - 2020

N2 - This work develops a multi tone modeling for seismic data compression. First, seismic traces are described as multitone decaying sinusoidal waves superposition. Each sinusoidal wave is regarded as a model component and is represented by a set of distinct parameters. Secondly, a novel parameter estimation algorithm for this model is proposed accordingly. In this algorithm, the parameters are estimated for each component sequentially. A suitable number of model components is determined by the level of the residuals energy. The proposed model-based compression scheme outperforms the Linear Predictive Coding (LPC) both analytically and experimentally in terms of compression ratio, reconstruction quality and convergence rate.

AB - This work develops a multi tone modeling for seismic data compression. First, seismic traces are described as multitone decaying sinusoidal waves superposition. Each sinusoidal wave is regarded as a model component and is represented by a set of distinct parameters. Secondly, a novel parameter estimation algorithm for this model is proposed accordingly. In this algorithm, the parameters are estimated for each component sequentially. A suitable number of model components is determined by the level of the residuals energy. The proposed model-based compression scheme outperforms the Linear Predictive Coding (LPC) both analytically and experimentally in terms of compression ratio, reconstruction quality and convergence rate.

UR - https://www.scopus.com/pages/publications/85079482266

U2 - 10.1190/segam2019-3208024.1

DO - 10.1190/segam2019-3208024.1

M3 - Paper

AN - SCOPUS:85079482266

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EP - 267

T2 - Society of Exploration Geophysicists International Exposition and Annual Meeting 2019, SEG 2019

Y2 - 15 September 2019 through 20 September 2019

ER -

A multi tone modeling for seismic data compression

Abstract

Conference

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