基于A- CGAN的深反射地震数据随机噪声压制方法研究

基于深度学习的地震数据噪声压制方法是当前地震数据去噪处理的重要方向。深度学习方法突破了传统滤波处理的局限,在对常规地震数据的噪声压制中表现出效率高、信噪分离效果好的特点。但针对深部弱有效反射数据,当前的深度学习方法特征提取能力有限,难以取得较好的去噪效果。笔者等结合深反射地震数据特点,针对当前深度学习噪声压制方法在特征提取及对数据集依赖上的局限,提出了基于注意力循环生成对抗网络(Attention Cycle- Consistent Generative Adversarial Networks,A- CGAN)的深反射地震数据随机噪声压制方法。借助循环一致生成对抗网络(Cycle- Consistent Generative Adversarial Networks,Cycle- GAN)的域映射思想,降低对数据集的要求。为了构建适用于深反射地震数据的去噪网络,从3个方面对Cycle- GAN进行改进：在Cycle- GAN的生成器（去噪器）中加入残差结构和注意力机制,用于加深网络深度和提高其特征提取能力;在Cycle- GAN的鉴别器中使用块判决,提高鉴别精度和准确度;在损失函数部分加入感知一致性损失函数,提升网络模型恢复纹理细节信息的能力。通过合成地震数据和实际深反射地震数据测试,验证了优化算法的有效性,体现了良好的应用价值。

Study on random noise suppression method of deep reflection seismic data based on A- CGAN

Seismic data noise suppression method based on deep learning is an important field of seismic data denoising processing. Deep learning method breaks through the limitation of traditional filter processing, and shows high efficiency and good signal- to- noise separation effect in noise suppression of conventional seismic data. However, for deep weak reflection data, the current deep learning methods have limited feature extraction ability, and it is difficult to achieve good denoising effect. Combined with the characteristics of deep reflection seismic data, and aiming at the limitations of current deep learning noise suppression methods in feature extraction and data set dependence, we propose a stochastic noise suppression method for deep reflection seismic data based on Attention Cycle- Consistent Generative Adversarial Networks (A- CGAN). Using the domain mapping idea of Cycle- Consistent Generative Adversarial Networks (Cycle- GAN), the requirements on data sets are reduced. In order to build a denoising network suitable for deep reflection seismic data, improvements are made to Cycle- GAN from three aspects: adding residual structure and attention mechanism to Cycle- GAN generator (denoising device) to deepen the depth of the network and improve its feature extraction capability; the block decision is used in the discriminator of Cycle- GAN to improve the precision and accuracy of the discriminator; adding the perception consistency loss function to the loss function improves the ability of the network model to recover texture details. The numerical model data and the actual deep reflection seismic data are tested to verify the effectiveness of the optimization algorithm, which shows good application value.