References

Allen, J. B., 1977, Short term spectral analysis, synthetic and modification by discrete fourier transform: IEEE Transactions on Acoustic, Speech, Signal Processing, 25, 235–238.

Bai, J., et al., 2022, Object detection in large-scale remote-sensing images based on time-frequency analysis and feature optimization: IEEE Transactions on Geoscience and Remote Sensing, 60, 1–16; doi: 10.1109/TGRS.2021.3119344.

Boashash, B., and P. Black, 1987, An efficient real-time implementation of the wigner-ville distribution: IEEE Transactions on Acoustics, Speech, and Signal Processing, 35, 1611–1618; doi: 10.1109/TASSP.1987.1165070.

Chakraborty, A., and D. Okaya, 1995, Frequency-time decomposition of seismic data using wavelet-based method: Geophysics, 60, 1906–1916.

Chen, S. S., D. L. Donoho, and M. A. Saunders, 2001, Atomic decomposition by basis pursuit: SIAM review, 43, 129–159.

Chen, Y., 2021, Nonstationary local time-frequency transform: Geophysics, 86, V245–V254.

Chen, Y., S. Jiao, J. Ma, H. Chen, Y. Zhou, and S. Gan, 2015, Ground-roll noise attenuation using a simple and effective approach based on local band-limited orthogonalization: IEEE Geoscience and Remote Sensing Letters, 12, 2316–2320.

Daubechies, I., J. Lu, and H.-T. Wu, 2011, Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool: Applied and computational harmonic analysis, 30, 243–261.

Elboth, T., I. V. Presterud, and D. Hermansen, 2010, Time-frequency seismic data de-noising: Geophysical prospecting, 58, 441–453.

Flandrin, P., G. Rilling, and P. Goncalves, 2004, Empirical mode decomposition as a filter bank: IEEE signal processing letters, 11, 112–114.

Fomel, S., 2002, Applications of plane‐wave destruction filters: GEOPHYSICS, 67, 1946–1960; doi: 10.1190/1.1527095.

——–, 2007a, Local seismic attributes: Geophysics, 72, A29–A33.

——–, 2007b, Shaping regularization in geophysical-estimation problems: Geophysics, 72, R29–R36.

——–, 2009, Adaptive multiple subtraction using regularized nonstationary regression: Geophysics, 74, V25–V33.

Fomel, S., and J. Claerbout, 2016, Streaming prediction-error filters: SEG International Exposition and Annual Meeting, SEG, SEG–2016.

——–, 2024, Streaming prediction-error filters: Geophysics, 89, 1–35.

Fomel, S., and L. Jin, 2009, Time-lapse image registration using the local similarity attribute: Geophysics, 74, A7–A11.

Fomel, S., and M. van der Baan, 2010, Local similarity with the envelope as a seismic phase detector: SEG International Exposition and Annual Meeting, SEG, SEG–2010.

Gao, W., and M. D. Sacchi, 2018, Multicomponent seismic data registration by nonlinear optimization: Geophysics, 83, V1–V10.

Geng, Z., S. Fomel, Y. Liu, Q. Wang, Z. Zheng, and Y. Chen, 2024, Streaming seismic attributes: Geophysics, 89, A7–A10.

Gholami, A., 2012, Sparse time–frequency decomposition and some applications: IEEE Transactions on Geoscience and Remote Sensing, 51, 3598–3604.

Huang, N. E., Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung, and H. H. Liu, 1998, The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis: Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences, 454, 903–995.

Kaur, H., S. Fomel, and N. Pham, 2020, Seismic ground-roll noise attenuation using deep learning: Geophysical Prospecting, 68, 2064–2077.

Liu, G., X. Chen, and K. Wu, 2011a, Random noise attenuation using nonstationary autoregression in fx domain: 73rd EAGE Conference and Exhibition incorporating SPE EUROPEC 2011, European Association of Geoscientists & Engineers, cp–238.

Liu, G., S. Fomel, and X. Chen, 2011b, Time-frequency analysis of seismic data using local attributes: Geophysics, 76, P23–P34.

Liu, N., J. Gao, X. Jiang, Z. Zhang, and Q. Wang, 2016, Seismic time–frequency analysis via stft-based concentration of frequency and time: IEEE Geoscience and Remote Sensing Letters, 14, 127–131.

Liu, N., J. Gao, B. Zhang, Q. Wang, and X. Jiang, 2019, Self-adaptive generalized s-transform and its application in seismic time–frequency analysis: IEEE Transactions on Geoscience and Remote Sensing, 57, 7849–7859.

Liu, N., Y. Lei, R. Liu, Y. Yang, T. Wei, and J. Gao, 2023, Sparse time–frequency analysis of seismic data: Sparse representation to unrolled optimization: IEEE Transactions on Geoscience and Remote Sensing, 61, 1–10; doi: 10.1109/TGRS.2023.3300578.

Liu, Y., and S. Fomel, 2013, Seismic data analysis using local time-frequency decomposition: Geophysical Prospecting, 61, 516–525.

Lu, J., Z. Yang, Y. Wang, and Y. Shi, 2015, Joint pp and ps ava seismic inversion using exact zoeppritz equations: Geophysics, 80, R239–R250.

Lu, W., and F. Li, 2013, Seismic spectral decomposition using deconvolutive short-time fourier transform spectrogram: Geophysics, 78, V43–V51.

Mallat, S. G., and Z. Zhang, 1993, Matching pursuits with time-frequency dictionaries: IEEE Transactions on signal processing, 41, 3397–3415.

Mohammadigheymasi, H., et al., 2022, Sparsity-promoting approach to polarization analysis of seismic signals in the time–frequency domain: IEEE Transactions on Geoscience and Remote Sensing, 60, 1–11; doi: 10.1109/TGRS.2022.3141580.

Qian, J., S. Huang, L. Wang, G. Bi, and X. Yang, 2022, Super-resolution isar imaging for maneuvering target based on deep-learning-assisted time–frequency analysis: IEEE Transactions on Geoscience and Remote Sensing, 60, 1–14; doi: 10.1109/TGRS.2021.3050189.

Sherman, J., and W. J. Morrison, 1950, Adjustment of an inverse matrix corresponding to a change in one element of a given matrix: The Annals of Mathematical Statistics, 21, 124–127.

Stockwell, R., 2007, Why use the s-transform?: Ams pseudo-differential operators: Partial differential equations and time-frequency analysis 52.

Stockwell, R. G., L. Mansinha, and R. P. Lowe, 1996, Localization of the complex spectrum: the S transform: IEEE Transactions on Signal Processing, 44, 998–1001.

Tan, Y., et al., 2022, Joint communication and sar waveform design method via time-frequency spectrum shaping: IEEE Transactions on Geoscience and Remote Sensing, 60, 1–13; doi: 10.1109/TGRS.2022.3230439.

Tao, Y., S. Cao, Y. Ma, and M. Ma, 2020, Second-order adaptive synchrosqueezing S transform and its application in seismic ground roll attenuation: IEEE Geoscience and Remote Sensing Letters, 17, 1308–1312.

Tary, J. B., R. H. Herrera, J. Han, and M. van der Baan, 2014, Spectral estimation—what is new? what is next?: Reviews of Geophysics, 52, 723–749.

Tikhonov, A. N., 1963, Solution of incorrectly formulated problems and the regularization method: Soviet Mathematics – Doklady.

Wang, Q.-H., Y. Liu, C. Liu, and Z.-S. Zheng, 2020, Continuous time-varying q-factor estimation method in the time-frequency domain: Applied Geophysics, 17, 844–856.

Wu, X., H. Zhang, and B. He, 2023, Deconvolutive improved s transform and its application in hydrocarbon detection: IEEE Transactions on Geoscience and Remote Sensing, 61, 1–12; doi: 10.1109/TGRS.2023.3268405.

Yang, L., S. Fomel, S. Wang, X. Chen, and Y. Chen, 2024, Deep learning with soft attention mechanism for small-scale ground roll attenuation: GEOPHYSICS, 89, WA179–WA193; doi: 10.1190/geo2023-0150.1.

Yang, Y., J. Gao, Z. Wang, and N. Liu, 2021, Data-driven time-frequency method and its application in detection of free gas beneath a gas hydrate deposit: IEEE Transactions on Geoscience and Remote Sensing, 60, 1–13.

Yang, Y., Y. Lei, N. Liu, Z. Wang, J. Gao, and J. Ding, 2022, Sparsetfnet: A physically informed autoencoder for sparse time–frequency analysis of seismic data: IEEE Transactions on Geoscience and Remote Sensing, 60, 1–12; doi: 10.1109/TGRS.2022.3213851.

Yao, Y., and L. Liu, 2022, Automatic p-wave arrival picking based on inaction method: IEEE Transactions on Geoscience and Remote Sensing, 60, 1–11; doi: 10.1109/TGRS.2022.3230411.

Yarham, C., U. Boeniger, and F. Herrmann, 2006, Curvelet-based ground roll removal: SEG International Exposition and Annual Meeting, SEG, SEG–2006.

Yilmaz, O., 1987, Seismic data processing: Investigation in geophysics, 2, 526.

Zhang, G., et al., 2020, Adaptive time-resampled high-resolution synchrosqueezing transform and its application in seismic data: IEEE Transactions on Geoscience and Remote Sensing, 58, 6691–6698; doi: 10.1109/TGRS.2020.2978509.

Zheng, Z., Y. Liu, and C. Liu, 2022, Nonstationary pattern-based signal–noise separation using adaptive prediction-error filter: Journal of Geophysics and Engineering, 19, 14–27.




2025-09-10