基于经验低秩表示的自适应多次波减去方法

多次波压制一直是海洋地震数据处理的研究重点，SRME（surface-related multiple elimination）方法通过多次波预测、自适应减去两个步骤实现多次波压制，具有压制精度高、模型依赖度低等优点，是工业界广泛采用的方法。但是常规减去方法在一次波、多次波交叉位置易出现一次波损伤、多次波残留等现象，进而增加后续地震处理、解释的难度。本文在前人研究的基础上，提出了基于经验低秩表示的自适应多次波减去方法。首先，采用经验低秩表示方法，将地震数据自适应地分解为信噪比高、倾角单一、同相轴相位圆滑的低秩倾角分量，简化地震数据倾角成分的复杂程度，以优化低秩表示过程中局部窗口的参数选择问题；然后，在不同低秩倾角分量中分别采用基于能量最小原则的匹配减去方法，避免匹配过程出现同相轴交叉，并重构各分量，在保证计算效率的前提下提升多次波压制效果。针对理论数据和实际数据进行了有效性测试，本文方法在多次波压制结果和保幅性两方面优于传统方法，并采用抗噪性测试证明了该方法的鲁棒性。

Adaptive Subtraction of Multiples Based on Empirical Low-Rank Representation

Multiple attenuation is a research focus in marine seismic data processing. Due to the advantages of high accuracy and low model dependence, the surface-related multiple elimination method achieves multiple attenuation through multiple prediction and adaptive subtraction, so it is widely used in the industry. However, the conventional adaptive subtraction method is not applicable at the primary and multiple intersections, and the phenomenon of primary damage and multiple residuals will increase the difficulty of subsequent seismic processing and interpretation. Based on previous research, in this paper, an adaptive multiple subtraction method based on empirical low-rank representation is proposed. We use the empirical mode decomposition method to improve the conventional low-rank representation method. By adaptively decomposing the seismic signal into low-rank subsets with high signal-to-noise ratio, simple inclination, and smooth event phase, the parameter selection of the local window during the low-rank representation processing is optimized, that is, to reduce the complexity of the dip component of the seismic signal; then the conventional adaptive subtraction method in different low-rank subsets is adopted to avoid the event intersections in the adaptive subtraction, and each subset is reconstructed to improve the multiple attenuation effect under the premise of ensuring the calculation efficiency. In order to verify the effectiveness of the proposed method, we apply it to the synthetic and field examples, the results are better than those of the conventional methods, and the noise sensitivity example proves the robustness of the proposed method.