Ground roll suppression based on the synchrosqueezed wavelet transform and local adaptive subtraction filtering
Abstract
Ground roll suppression remains a challenge in land seismic data processing, particularly when strong surface wave energy exhibits significant overlap with effective reflection events in the low frequency band. Conventional approaches, such as frequency-wavenumber (F-K) filtering, Radon transform-based methods, and other spatial filtering techniques, often fail to achieve satisfactory results under such conditions due to limited resolution and poor amplitude preservation. To address this problem, a time-frequency domain ground roll suppression method that integrates the synchrosqueezed wavelet transform (SSWT) with local adaptive subtraction filtering is proposed. First, seismic traces are transformed from the time-space domain into the time-frequency domain using SSWT, which provides a highly concentrated time-frequency representation. Based on the distinct distribution characteristics of ground roll noise and effective reflection signals in the time-frequency domain, a ground roll model is extracted. Subsequently, a local adaptive subtraction filter is applied to adjust the amplitude and phase of the extracted ground roll model, and the corrected model is subtracted from the original seismic data to obtain the final suppressed result. Applications to both synthetic and field seismic data demonstrate that the proposed method effectively attenuates ground roll energy while maximally preserving the amplitude and waveform characteristics of reflection signals. Compared with conventional time-frequency-based ground roll suppression approaches, the proposed method exhibits superior amplitude fidelity and improved noise attenuation performance.
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BibTeX
@misc{li2026ground,
title = {{Ground roll suppression based on the synchrosqueezed wavelet transform and local adaptive subtraction filtering}},
author = {Zifei Li and Zhengyu Tan and Shaohuan Zu},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.08035},
eprint = {2610.08035},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.08035},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Ground roll suppression based on the synchrosqueezed wavelet transform and local adaptive subtraction filtering
AU - Zifei Li
AU - Zhengyu Tan
AU - Shaohuan Zu
PY - 2026
DA - 2026/10/06
PB - arXiv
DO - 10.48550/arxiv.2610.08035
UR - https://arxiv.org/abs/2610.08035
AB - Ground roll suppression remains a challenge in land seismic data processing, particularly when strong surface wave energy exhibits significant overlap with effective reflection events in the low frequency band. Conventional approaches, such as frequency-wavenumber (F-K) filtering, Radon transform-based methods, and other spatial filtering techniques, often fail to achieve satisfactory results under such conditions due to limited resolution and poor amplitude preservation. To address this problem, a time-frequency domain ground roll suppression method that integrates the synchrosqueezed wavelet transform (SSWT) with local adaptive subtraction filtering is proposed. First, seismic traces are transformed from the time-space domain into the time-frequency domain using SSWT, which provides a highly concentrated time-frequency representation. Based on the distinct distribution characteristics of ground roll noise and effective reflection signals in the time-frequency domain, a ground roll model is extracted. Subsequently, a local adaptive subtraction filter is applied to adjust the amplitude and phase of the extracted ground roll model, and the corrected model is subtracted from the original seismic data to obtain the final suppressed result. Applications to both synthetic and field seismic data demonstrate that the proposed method effectively attenuates ground roll energy while maximally preserving the amplitude and waveform characteristics of reflection signals. Compared with conventional time-frequency-based ground roll suppression approaches, the proposed method exhibits superior amplitude fidelity and improved noise attenuation performance.
ER -