Iterative separation of coherent blended signals in common shot gathers using synchrosqueezed curvelet-Radon constraints
Abstract
Blended data acquired via simultaneous-source seismic exploration conventionally require post-acquisition deblending, which typically relies on the coherence differences introduced by firing time delays (time dithering). To reduce the dependency of the deblending process on these time dithers, a novel joint constraint based on the synchrosqueezed transform and the Radon transform is proposed, operating directly in the common-shot gather (CSG) domain. Specifically, by exploiting the differences in propagation directions of the blended signals within CSGs, the synchrosqueezed transform is first employed for an initial iterative separation to extract individual sources directly from the continuous records. Once the majority of the valid signals are separated, the Radon transform is subsequently applied in further iterations to suppress the residual blending interference, thereby preventing amplitude damage to the effective signals. Compared to conventional non-CSG deblending methods, this approach bypasses the reliance on time-delay coherence differences, thus enabling real-time quality monitoring of individual sources during field acquisition. Furthermore, compared to other existing CSG-based separation techniques, the proposed joint-constraint iterative framework demonstrates superior performance when handling complex data. Applications on both synthetic and field blended datasets demonstrate that high-fidelity data separation can be successfully achieved independently of the time-dithering constraints. Finally, because the proposed method operates completely independently across different CSG slices, it is highly amenable to parallel computing, facilitating the efficient processing of massive datasets within a short timeframe.
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BibTeX
@misc{li2026iterative,
title = {{Iterative separation of coherent blended signals in common shot gathers using synchrosqueezed curvelet-Radon constraints}},
author = {Zifei Li and Shaohuan Zu and Haojun Chen},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.39354},
eprint = {2609.39354},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.39354},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Iterative separation of coherent blended signals in common shot gathers using synchrosqueezed curvelet-Radon constraints
AU - Zifei Li
AU - Shaohuan Zu
AU - Haojun Chen
PY - 2026
DA - 2026/09/30
PB - arXiv
DO - 10.48550/arxiv.2609.39354
UR - https://arxiv.org/abs/2609.39354
AB - Blended data acquired via simultaneous-source seismic exploration conventionally require post-acquisition deblending, which typically relies on the coherence differences introduced by firing time delays (time dithering). To reduce the dependency of the deblending process on these time dithers, a novel joint constraint based on the synchrosqueezed transform and the Radon transform is proposed, operating directly in the common-shot gather (CSG) domain. Specifically, by exploiting the differences in propagation directions of the blended signals within CSGs, the synchrosqueezed transform is first employed for an initial iterative separation to extract individual sources directly from the continuous records. Once the majority of the valid signals are separated, the Radon transform is subsequently applied in further iterations to suppress the residual blending interference, thereby preventing amplitude damage to the effective signals. Compared to conventional non-CSG deblending methods, this approach bypasses the reliance on time-delay coherence differences, thus enabling real-time quality monitoring of individual sources during field acquisition. Furthermore, compared to other existing CSG-based separation techniques, the proposed joint-constraint iterative framework demonstrates superior performance when handling complex data. Applications on both synthetic and field blended datasets demonstrate that high-fidelity data separation can be successfully achieved independently of the time-dithering constraints. Finally, because the proposed method operates completely independently across different CSG slices, it is highly amenable to parallel computing, facilitating the efficient processing of massive datasets within a short timeframe.
ER -