Random noise attenuation is a fundamental task in seismic data processing aimed at improving the signal-to-noise ratio of seismic data, thereby improving the efficiency and accuracy of subsequent seismic data processing and interpretation. To this end, model-based and data-driven seismic data denoising methods have been widely applied, including f–x deconvolution, K-singular value decomposition, feed-forward denoising convolutional neural network and U-Net (an improved fully convolutional neural network structure), which have received widespread attention for their effectiveness in attenuating random noise. However, they often struggle with low-signal-to-noise ratio data and complex noise environments, leading to poor performance and leakage of effective signals. To address these issues, we propose a novel approach for random noise attenuation. This approach employs a multi-model stacking structure, where the parameters governing this structure are optimized using a particle swarm optimizer. In the model-based denoising method, we choose the f–x deconvolution method, whereas in the data-driven denoising method, we choose K-singular value decomposition for shallow learning and U-Net for deep learning as components of the multi-model stacking structure. The optimal parameters for the multi-model stacking structure are obtained using a particle swarm optimizer, guided by the proposed novel hybrid fitness function incorporating weighted signal-to-noise ratio, structural similarity and correlation parameters. Finally, the effectiveness of the proposed method is verified with three synthetic and two real seismic datasets. The results demonstrate that the proposed method is effective in attenuating random noise and outperforms the benchmark methods in denoising both synthetic and real seismic data.

中文翻译：

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基于粒子群优化的多模型叠加结构地震数据随机噪声衰减

随机噪声衰减是地震资料处理的一项基础任务，旨在提高地震资料的信噪比，从而提高后续地震资料处理和解释的效率和精度。为此，基于模型和数据驱动的地震数据去噪方法得到了广泛应用，包括f - x反卷积、K-奇异值分解、前馈去噪卷积神经网络和U-Net（一种改进的全卷积神经网络）结构），因其在衰减随机噪声方面的有效性而受到广泛关注。然而，它们经常与低信噪比数据和复杂的噪声环境作斗争，导致性能不佳和有效信号的泄漏。为了解决这些问题，我们提出了一种随机噪声衰减的新方法。该方法采用多模型堆叠结构，其中控制该结构的参数使用粒子群优化器进行优化。在基于模型的去噪方法中，我们选择f - x反卷积方法，而在数据驱动的去噪方法中，我们选择浅层学习的K奇异值分解和深度学习的U-Net作为多模型的组成部分堆叠结构。多模型堆叠结构的最佳参数是使用粒子群优化器获得的，由所提出的结合加权信噪比、结构相似性和相关参数的新型混合适应度函数引导。最后，利用三个合成地震数据集和两个真实地震数据集验证了该方法的有效性。结果表明，所提出的方法可以有效地衰减随机噪声，并且在合成和真实地震数据的去噪方面优于基准方法。