Noniterative <i>f -x-y</i> Streaming Prediction Filtering for Random Noise Attenuation on Seismic Data

Liu, Yang; Zheng, Zhisheng

doi:10.1109/tgrs.2021.3099431

Cited by 9 publications

(1 citation statement)

References 23 publications

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“…In addition, field seismic data are usually contaminated by noise (both random and coherent), which also causes FWI to deviate from the correct direction of convergence. Conventional denoising methods (e.g., f-x deconvolution, EMD, SVD, and wavelet transform) are usually based on theoretical model assumptions and rely on a priori information, which has difficulty handling complex noises and low computational efficiency (Han and Van, 2015;Liu and Zheng, 2022). Data-driven-based denoising methods can establish a strong non-linear mapping between noise-contained and pure data, which is currently a hot research topic in seismic denoising.…”

Section: Introductionmentioning

confidence: 99%

Full waveform inversion based on dynamic data matching of convolutional wavefields

et al. 2023

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Cycle skipping problem caused by the absent of low frequencies and inaccurate initial model makes full waveform inversion (FWI) deviate from the true model. A novel method is proposed to mitigate cycle skipping phenomenon by dynamic data matching which improves the matching of synthetic and observed events to regulate the updating of initial model in a correct direction. 1-dimentional (1-D) Gaussian convolutional kernels with different lengths are used to extract features of each time sample in each trace which represents the integrated properties of wavefield at different time ranges centered on each time sample. According to the minimum Euclidean distance of the features, the optimally matched pairs of time samples in the observed and synthetic trace can be found. A constraint evaluates the reliability of dynamic matching by attenuating the amplitude of synthetic data according to the values of traveltime differences between each pairs of optimally matched time samples is proposed to improve the accuracy of data matching. In addition, Gaussian kernels have the capability to extract features of time samples contaminated by strong noises accurately to improve the robustness of the propose method further. The selection scheme of optimal parameters is discussed and concluded to ensure the convergence of the proposed method. Numerical tests on Marmousi model verify the feasibility of the propose method. The proposed method provides a new approach to tackle the convergence problem of FWI when using the field seismic data.

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Section: Introductionmentioning

confidence: 99%

Full waveform inversion based on dynamic data matching of convolutional wavefields

et al. 2023

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3-D Structure-Oriented Adaptive Gaussian Pyramid for Seismic Multiscale Fracture Detection

Chen

et al. 2023

IEEE Geosci. Remote Sensing Lett.

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Simultaneous Seismic Data Interpolation and Denoising Based on Nonsubsampled Contourlet Transform Integrating With Two-Step Iterative Log Thresholding Algorithm

Liu

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Noniterative f -x-y Streaming Prediction Filtering for Random Noise Attenuation on Seismic Data

Cited by 9 publications

References 23 publications

Full waveform inversion based on dynamic data matching of convolutional wavefields

Full waveform inversion based on dynamic data matching of convolutional wavefields

3-D Structure-Oriented Adaptive Gaussian Pyramid for Seismic Multiscale Fracture Detection

Simultaneous Seismic Data Interpolation and Denoising Based on Nonsubsampled Contourlet Transform Integrating With Two-Step Iterative Log Thresholding Algorithm

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