Ground roll attenuation using a time-frequency dependent polarization filter based on the S transform

Tan, Yuyang; He, Chu; Wang, Yandong; Zhao, Ziran

doi:10.1007/s11770-013-0383-3

Cited by 18 publications

(3 citation statements)

References 28 publications

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“…Park et al [48] and Reading et al [27] proposed the frequency-domain polarization filtering method to differentiate the waves that are superposed in time but with different frequency components. However, the body waves and surface waves would be superposed in both time and frequency; therefore, polarization analysis methods based on a time-frequency transform were developed, including the continuous wavelet transform-based method [28,49], the discrete wavelet transform-based method [50], and the S transform-based methods [51,52]. To solve the problem of the inaccurate estimation of the polarization parameter caused by the phase distortion of the conventional wavelet transform coefficient, Galiana-Merino et al [53] proposed to use the stationary wavelet packet transform to estimate the instantaneous polarization parameters.…”

Section: The Polarization Filtering Methodsmentioning

confidence: 99%

Discussions on the Processing of the Multi-Component Seismic Vector Field

Wang

Sun

et al. 2019

Applied Sciences

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Multi-component seismic data contain a great deal of vector field information that reflects the situation of the underground medium. However, the processing methods used for multi-component seismic data are still being developed, and effectively retaining and using this information is the difficulty and the focus of the task. Currently, the main-stream processing techniques of multi-component seismic data treat the individual components independently as a scalar field; in this way, they do not excavate the vector features of the wavefield, thus restricting the potential utilities of the effective information. Research into processing methods that are suitable for use with the vector field, which can better retain and use the orientations and the relative amplitude relationship between multi-component seismic data, is urgently needed and represent an important direction for the current development of multi-component seismic data processing techniques. In this paper, we introduce and summarize several existing vector pre-processing techniques, including polarization filtering, de-noising using vector order statistics, group sparse representation, and vector separation of compressional waves and shear waves, to help scholars develop more effective vector field processing methods and to promote the development of vector processing techniques for multi-component seismic data.

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Section: The Polarization Filtering Methodsmentioning

confidence: 99%

Discussions on the Processing of the Multi-Component Seismic Vector Field

Wang

Sun

et al. 2019

Applied Sciences

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“…In recent years, the high-precision time-frequency analysis method based on mathematical transformation has F I G U R E 3 Flowchart of ST-CNN achieved excellent results in ground roll suppression (Liu & Fomel, 2013;Tao et al, 2020). Tan et al (2013) used a time-frequency-dependent polarization filter based on the S transform to suppress the ground roll. Chen et al (2016) proposed the improved complete ensemble empirical mode decomposition, which effectively solved the problems of strong filtering residuals and spurious artefacts based on the Empirical Mode Decomposition time-frequency analysis method.…”

Section: Introductionmentioning

confidence: 99%

“…Tan et al. (2013) used a time‐frequency‐dependent polarization filter based on the S transform to suppress the ground roll. Chen et al.…”

Section: Introductionmentioning

confidence: 99%

Ground roll intelligent suppression based on spatial domain synchrosqueezing wavelet transform convolutional neural network

Xing

Lin

Wang

et al. 2022

Geophysical Prospecting

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Ground roll is usually considered as a common linear noise in land seismic data. The existence of the ground roll often masks the effective reflection information of underground media, resulting in the deterioration of seismic data quality. Therefore, ground roll suppression is one of the main tasks in seismic data processing. A large number of previous studies have proved that the time‐frequency signal processing method based on mathematical transformation has shown excellent performance in ground roll attenuation and still has development potential. Meanwhile, a convolutional neural network, as one of the popular deep learning technologies, has also been widely used in the field of seismic signal processing. In this paper, we combine the convolutional neural network with the time‐frequency signal processing method based on mathematical transformation, that is, spatial domain synchrosqueezing wavelet transform, and propose a complete ground roll suppression workflow of shot gathers in spatial wavenumber domain, realizing high‐precision and automatic ground roll removal. Field data examples show that compared with bandpass filtering, FK filtering, time domain synchrosqueezing wavelet transform, spatial domain synchrosqueezing wavelet transform and the convolutional neural network, the spatial domain synchrosqueezing wavelet transform convolutional neural network has achieved satisfactory results in effectively attenuating ground roll and retaining valid information.

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Phase-shift- and phase-filtering-based surface-wave suppression method

Wang

2016

Appl. Geophys.

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Ground roll attenuation using a time-frequency dependent polarization filter based on the S transform

Cited by 18 publications

References 28 publications

Discussions on the Processing of the Multi-Component Seismic Vector Field

Discussions on the Processing of the Multi-Component Seismic Vector Field

Ground roll intelligent suppression based on spatial domain synchrosqueezing wavelet transform convolutional neural network

Phase-shift- and phase-filtering-based surface-wave suppression method

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