Deghosting is an important technique in the marine seismic industry, as it plays a crucial role in mitigating the effects of ghost reflections from the sea surface, which can significantly impact the accuracy and resolution of subsurface imaging. In recent years, various acquisition-based techniques have been developed to tackle the challenge of removing receiver–ghost reflections, which is the focus of our paper. These state-of-the-art approaches, such as dual-sensor or multicomponent towed streamer acquisitions, have demonstrated exceptional accuracy by combining pressure and particle motion data. However, such methods face limitations when dealing with low frequencies due to heavy noise contamination in the particle motion data. Consequently, ghost-free data reconstruction at low frequencies typically relies on processing-based approaches, which exclusively utilize recorded pressure data. This study presents a novel deghosting method for low-frequency applications based on parabolic dictionary learning, which relies solely on recorded pressure data. The proposed method has the advantage of being applicable directly in a compressed domain, eliminating the need for data decompression prior to the deghosting process when compression is applied before the processing steps. This not only reduces costs related to data storage and transfer but also provides a cost-effective alternative to conventional deghosting by operating directly on the compressed data format, which is smaller in size. The effectiveness of the proposed method was evaluated using both synthetic and field datasets. The results obtained from a synthetic data example indicate that the proposed method achieves similar results to an industry-standard frequency–wavenumber method, while achieving a compression rate of over 7. Furthermore, the method was tested using a field dataset consisting of a full sail-line of marine seismic acquisition. The comparison of 2D pre-stack migrated images between the proposed method and the industry-standard frequency–wavenumber method revealed insignificant differences, while achieving a compression ratio higher than 5 when our method was used.

中文翻译：

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使用抛物线字典学习在压缩域中进行低频地震消幻影

消幻影是海洋地震行业中的一项重要技术，因为它在减轻海面幻影反射的影响方面发挥着至关重要的作用，这会显着影响地下成像的精度和分辨率。近年来，已经开发了各种基于采集的技术来解决消除接收器重影反射的挑战，这是我们论文的重点。这些最先进的方法，例如双传感器或多分量拖曳拖缆采集，通过结合压力和粒子运动数据，展示了卓越的准确性。然而，由于粒子运动数据中存在严重的噪声污染，此类方法在处理低频时面临局限性。因此，低频下的无重影数据重建通常依赖于基于处理的方法，这些方法专门利用记录的压力数据。这项研究提出了一种基于抛物线字典学习的低频应用的新颖的去幻影方法，该方法仅依赖于记录的压力数据。所提出的方法的优点是可以直接应用于压缩域，当在处理步骤之前应用压缩时，无需在去幻影过程之前进行数据解压缩。这不仅降低了与数据存储和传输相关的成本，而且还通过直接对尺寸较小的压缩数据格式进行操作，为传统的去重影提供了一种经济高效的替代方案。使用合成数据集和现场数据集评估了所提出方法的有效性。从合成数据示例获得的结果表明，所提出的方法取得了与行业标准频率波数方法相似的结果，同时实现了超过 7 的压缩率。此外，该方法使用由全帆组成的现场数据集进行了测试-海洋地震采集线。所提出的方法和行业标准频率波数方法之间的二维叠前偏移图像的比较显示出不显着的差异，同时使用我们的方法时实现了高于5的压缩比。