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Signal Detection and Enhancement for Seismic Crosscorrelation Using the Wavelet-Domain Kalman Filter

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Abstract

Crosscorrelation is a classical signal-processing technique that plays an important role in exploration and earthquake geophysics. Seismic velocity estimation utilizes the crosscorrelation between observed and predicted seismic records in traveltime tomography. The crosscorrelation between two stations represents the Green’s functions retrieved from ambient noises in passive seismic interferometry. It can be used to estimate the subsurface velocity and amplitude information. The calculation of crosscorrelation usually assumes that the input data are stationary; however, the real seismic data are often non-stationary, due to the presence of multiple wave-modes and background noises. The seismic crosscorrelations often have low signal-to-noise ratio and frequently fail to provide correct information for subsequent processing. To address this problem, we develop a comprehensive technique to reduce contamination and improve the quality of crosscorrelation in the wavelet domain. The new procedure includes the forward wavelet transformation of raw records, the crosscorrelation between wavelet coefficients, single-channel image object detection, multi-channel Kalman-filter object tracking, and inverse wavelet transformation to produce the new crosscorrelation gathers. We effectively remove the unwanted components associated with contaminated wave-modes as the proposed detection and tracking algorithm can accurately extract the target wave-mode. We validate the method for three datasets: a marine streamer survey, a borehole survey, and a broadband dataset from seismology stations. We demonstrate that the proposed method can significantly improve the signal-to-noise ratio of the seismic crosscorrelations, considerably enhancing the quality of the data for subsequent advanced crosscorrelation-based seismic processing.

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Acknowledgements

We specifically express our appreciation to Cristina Young for her critical work on improving this paper. We would like to thank the Associate Editor, Prof. Jeffery Yu Gu, and one anonymous reviewer for their comments and suggestions, which significantly improved the quality of this paper. This study is jointly supported by the National Key R&D Program of China (2017YFC1500303), the Science Foundation of China University of Petroleum, Beijing (2462019YJRC007 and 2462020YXZZ047), and the Strategic Cooperation Technology Projects of CNPC and CUPB (ZX20190220). The associated data and codes used by this paper are available at the link with https://github.com/zhaoyangprof/KalmanFilterCrosscorrelation.git.

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Authors and Affiliations

  1. State Key Laboratory of Petroleum Resource and Prospecting, Unconventional Petroleum Research Institute, China University of Petroleum, 18 Fuxue Road, Changping District, Beijing, 102249, China

    Yang Zhao

  2. Department of Earth, Environmental and Planetary Sciences, Rice University, Houston, TX, 77005, USA

    Fenglin Niu

  3. Department of Geophysics, Stanford University, Stanford, CA, 94305, USA

    Zhishuai Zhang

  4. Broad Geophysical Technology, Houston, TX, 77043, USA

    Xiang Li

  5. Houston Research Center, Aramco Services Company, Houston, TX, 77084, USA

    Jinhong Chen

  6. University of Texas at Dallas, Dallas, TX, 75080, USA

    Jidong Yang

Authors
  1. Yang Zhao
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  2. Fenglin Niu
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  3. Zhishuai Zhang
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  4. Xiang Li
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  5. Jinhong Chen
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  6. Jidong Yang
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Contributions

YZ conceived the presented idea, developed the theory and performed the computations. He wrote the initial version of the manuscript and verified the preliminary results. FN is in charge of two Grants, which are used to support this study. He also provided the data of broadband stations in Texas. ZZ organized these examples and delivered many insightful discussions in the early stage of this study. XL provided data platforms and computational resources. JC assisted in preparing the updated data and codes since the corresponding author was unable to access the resources which were associated with his previous employer. JY provided English language editing services and fixed grammar issues.

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Correspondence to Yang Zhao.

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Zhao, Y., Niu, F., Zhang, Z. et al. Signal Detection and Enhancement for Seismic Crosscorrelation Using the Wavelet-Domain Kalman Filter. Surv Geophys 42, 43–67 (2021). https://doi.org/10.1007/s10712-020-09620-6

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  • DOI: https://doi.org/10.1007/s10712-020-09620-6

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