Data adaptive ground-roll attenuation via sparsity promotion

Wang, Wei; Gao, Jinghuai; Chen, Wenchao; Xu, Jin

doi:10.1016/j.jappgeo.2012.04.004

Cited by 29 publications

(5 citation statements)

References 35 publications

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“…We choose D g and D r as the sparse representation dictionaries of surface waves and reflections, respectively, i.e., y g = D g z g , y r = D r z r . The separation of surface waves and reflections can be formulated as [25]:…”

Section: New Method: Sparse Representations Of Wavefields Based On Mcamentioning

confidence: 99%

“…What causes this phenomenon, "mode kissing", is the non-negligible effect of the reflections within this range of frequencies and velocities. The picked dispersion curves based on the amplitude and the continuity of dispersive energy are shown in Figure 5c, where the second higher mode of frequencies of [25][26][27] Hz is misidentified as the third higher mode. However, the surface-wave dispersive energy on Z-component seismic data is not severely influenced by the reflections from the deep reflectors according to Hu et al [19].…”

Section: Distortion Of Surface-wave Dispersive Energy Caused By Reflementioning

confidence: 99%

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Surface-Wave Extraction Based on Morphological Diversity of Seismic Events

Qiu

Wang

et al. 2018

Applied Sciences

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It is essential to extract high-fidelity surface waves in surface-wave surveys. Because reflections usually interfere with surface waves on X components in multicomponent seismic exploration, it is difficult to extract dispersion curves of surface waves. To make matters worse, the frequencies and velocities of higher-mode surface waves are close to those of PS-waves. A method for surface-wave extraction is proposed based on the morphological differences between surface waves and reflections. Frequency-domain high-resolution linear Radon transform (LRT) and time-domain high-resolution hyperbolic Radon transform (HRT) are used to represent surface waves and reflections, respectively. Then, a sparse representation problem based on morphological component analysis (MCA) is built and optimally solved to obtain high-fidelity surface waves. An advantage of our method is its ability to extract surface waves when their frequencies and velocities are close to those of reflections. Furthermore, the results of synthetic and field examples confirm that the proposed method can attenuate the distortion of surface-wave dispersive energy caused by reflections, which contributes to extraction of accurate dispersion curves.

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Section: New Method: Sparse Representations Of Wavefields Based On Mcamentioning

confidence: 99%

Section: Distortion Of Surface-wave Dispersive Energy Caused By Reflementioning

confidence: 99%

Surface-Wave Extraction Based on Morphological Diversity of Seismic Events

Qiu

Wang

et al. 2018

Applied Sciences

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“…Many conventional methods have been developed to attenuate these noises, the conventional methods can be divided into two groups. The first one can be summarized to filter method which is based on suppression of undesired parts of recorded data in the spectral domain, including high-pass and band pass filtering, f-k filtering [7][8][9] and the adaptive ground-roll attenuation method [10,11].…”

Section: Issn: 0067-2904mentioning

confidence: 99%

Coherent Noise Attenuation Using AGORA Filter on 2D Seismic Data in East Diwaniya, South Eastern â€“ Iraq

Ali

Ibraheem

Thabit³

2019

eijs

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Coherent noise such as ground roll and guided wave is present in the seismic line DE21 (East Diwaniya south eastern Iraq) and it obscures seismic signal and degrades from the quality of data. To attenuate the coherent noise from the shot gather and the Stack of the seismic line, AGORA filter was applied in order to obtain the good signal as the hyperbola shape in shot gather and the reflectors will be clearly to interpret it later. It has given good results and the coherent noise was attenuated in high ratio on all the line. The spectrum analysis is confirmed the effectiveness of AGORA filter to attenuate the coherent noise.

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“…In the process of seismic data processing, based on the characteristics of low frequency and low apparent velocity of ground roll, researchers put forward many methods to suppress ground roll, such as bandpass filtering, matching filtering (Saatcilar & Canitez, 1988), FK filtering (Treitel et al., 1967), adaptive threshold filtering (Prasad & Mahalanabis, 1980), Radon domain filtering (Hu et al., 2016), Karhunen‐Loeve transform domain filtering and so on (Liu, 1999; Tiapkina et al., 2012). Although these methods have a certain suppression effect, but the processing accuracy is limited, which often causes a certain degree of damage to the valid signal (Wang et al., 2012).…”

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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Data adaptive ground-roll attenuation via sparsity promotion

Cited by 29 publications

References 35 publications

Surface-Wave Extraction Based on Morphological Diversity of Seismic Events

Surface-Wave Extraction Based on Morphological Diversity of Seismic Events

Coherent Noise Attenuation Using AGORA Filter on 2D Seismic Data in East Diwaniya, South Eastern â€“ Iraq

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

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