基于同步压缩曲波-拉东约束的共炮点道集中相干混叠信号迭代分离
Iterative separation of coherent blended signals in common shot gathers using synchrosqueezed curvelet-Radon constraints
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中文总结 AI 辅助
提出一种基于同步压缩变换和拉东变换联合约束的迭代分离方法，在共炮点道集域中利用信号传播方向差异，无需时间抖动即可实现混叠数据的高保真分离，支持并行计算，适用于大规模数据处理。
中文摘要 AI 辅助
通过同时源地震勘探采集的混叠数据通常需要采集后的去混叠处理，该处理通常依赖于由激发时间延迟（时间抖动）引入的相干性差异。为降低去混叠过程对这些时间抖动的依赖性，提出了一种基于同步压缩变换和拉东变换的新型联合约束，直接在共炮点道集（CSG）域中操作。具体而言，通过利用CSG内混叠信号传播方向的差异，首先采用同步压缩变换进行初始迭代分离，直接从连续记录中提取各个独立震源。一旦大部分有效信号被分离，随后在进一步迭代中应用拉东变换以抑制残余混叠干扰，从而防止有效信号的振幅损伤。与传统的非CSG去混叠方法相比，该方法绕过了对时间延迟相干性差异的依赖，从而能够在野外采集期间对单个震源进行实时质量监控。此外，与其他现有的基于CSG的分离技术相比，所提出的联合约束迭代框架在处理复杂数据时表现出更优越的性能。在合成和野外混叠数据集上的应用表明，可以独立于时间抖动约束成功实现高保真数据分离。最后，由于所提出的方法在不同CSG切片上完全独立运行，因此非常适用于并行计算，有助于在短时间内高效处理大规模数据集。
英文摘要
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.
发表机构
- State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu University of Technology（油气藏地质及开发工程国家重点实验室，成都理工大学）
- College of Geophysics, Chengdu University of Technology（地球物理学院，成都理工大学）
- Key Laboratory of Earth Exploration and Information Technology of Ministry of Education, Chengdu University of Technology（教育部地球探测与信息技术重点实验室，成都理工大学）
机构由 AI 辅助整理，请以论文原文为准。
