Time-frequency analysis of seismic data using local attributes |

**Guochang Liu ^{}, Sergey Fomel^{}, Xiaohong Chen^{}**

^{}State Key Laboratory of Petroleum Resources and Prospecting

China University of Petroleum

Beijing, China
^{}Bureau of Economic Geology,

John A. and Katherine G. Jackson School of Geosciences

The University of Texas at Austin

University Station, Box X

Austin, TX, USA, 78713-8924

Time-frequency analysis is an important technology in seismic data processing and interpretation. To localize frequency content in time, we have developed a novel method for computing a time-frequency map for nonstationary signals using an iterative inversion framework. We calculated time-varying Fourier coefficients by solving a least-squares problem that uses regularized nonstationary regression. We defined the time-frequency map as the norm of time-varying coefficients. Time-varying average frequency of the seismic data can also be estimated from the time-frequency map calculated by our method. We tested the method on benchmark synthetic signals and compared it with the well-known Stransform. Two field data examples showed applications of the proposed method for delineation of sand channels and for detection of low-frequency anomalies.

- Introduction
- Time-frequency analysis using local attributes
- ESTIMATION OF TIME-VARYING AVERAGE FREQUENCY
- Examples
- Synthetic data
- 3D Gulf of Mexico data for channel detection
- 2D data example for low-frequency anomaly detection

- Conclusion
- Acknowledgments
- SHAPING REGULARIZATION FOR INVERSE PROBLEMS
- Bibliography
- About this document ...

Time-frequency analysis of seismic data using local attributes |

2013-03-02