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First-arrival picking through fuzzy c-means and robust locally weighted regression

Abstract

First-arrival picking is a crucial step in seismic data processing. Because of the diverse background noises and irregular near-surface conditions, it is difficult to pick first arrivals. In addition, existing algorithms are usually sensitive to parameter settings. Therefore, this paper proposes the first-arrival picking through fuzzy c-means and robust locally weighted regression (FPFR) algorithm consisting of two subroutines. The pre-picking subroutine obtains initial first arrivals through fuzzy c-means clustering and adaptive cluster-selection techniques. The smoothing subroutine handles background noises and near-ground conditions through adaptive parameter regression technique. The experiment is conducted on six field seismic datasets and one synthetic dataset. Results show that FPFR is more accurate than three state-of-the-art methods.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (41674141).

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Correspondence to Fan Min.

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On behalf of all authors, the corresponding author states that there is no conflict of interest.

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Communicated by Dr. Junlun Li (ASSOCIATE EDITOR) / Prof. Michał Malinowski (CO-EDITOR-IN-CHIEF).

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Gao, L., Liu, D., Luo, G.F. et al. First-arrival picking through fuzzy c-means and robust locally weighted regression. Acta Geophys. (2021). https://doi.org/10.1007/s11600-021-00636-z

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Keywords

  • First-arrival picking
  • Fuzzy c-means
  • Robust locally weighted regression
  • Optimization model