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Regularized least-squares migration of simultaneous-source seismic data with adaptive singular spectrum analysis.

Chuang Li1, Jian-Ping Huang1, Zhen-Chun Li1

  • 1School of Geosciences, China University of Petroleum, Qingdao, 266580 Shandong China.

Petroleum Science
|February 28, 2017
PubMed
Summary

Simultaneous-source seismic acquisition offers efficiency but causes artifacts. A new regularized least-squares reverse time migration (RLSRTM) method effectively removes these artifacts, improving seismic imaging quality.

Keywords:
Adaptive singular spectrum analysisBlended dataLeast-squares migrationRegularization

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Area of Science:

  • Geophysics
  • Seismic Imaging
  • Signal Processing

Background:

  • Simultaneous-source seismic acquisition is cost-effective but introduces migration artifacts due to source interference.
  • Existing methods struggle to accurately image data acquired simultaneously from multiple sources.

Purpose of the Study:

  • To develop a novel method for artifact reduction in simultaneous-source seismic data.
  • To improve the imaging quality and efficiency of seismic data processing.

Main Methods:

  • Proposed a regularized least-squares reverse time migration (RLSRTM) method.
  • Incorporated singular spectrum analysis (SSA) for sparseness constraints on the inverted model.
  • Utilized difference spectrum theory of singular values for adaptive artifact elimination.

Main Results:

  • RLSRTM successfully eliminated migration artifacts in numerical tests.
  • Demonstrated superior imaging quality, efficiency, and convergence compared to standard LSRTM.
  • Validated performance on flat layer and Marmousi models with simultaneous-source, incomplete, and noisy data.

Conclusions:

  • RLSRTM is an effective technique for processing simultaneous-source seismic data.
  • The method enhances seismic imaging quality and processing efficiency.
  • RLSRTM shows robustness in handling various data acquisition challenges.