论文详情
地震噪声的压制是地震勘探中地震数据处理的重要研究内容之一。准确地压制地震噪声和提取地震信号中的有效信息是地震勘探和地震监测的一项关键步骤。传统的地震噪声压制方法存在一些不足之处, 如灵活性不足、难以处理复杂噪声、有效信息损失以及依赖人工提取特征等局限性。为克服传统方法的不足, 采用时频域变换并结合深度学习方法进行地震噪声压制, 并验证其应用效果。通过构建5个神经网络模型(FCN、Unet、CBDNet、SwinUnet以及TransUnet)对经过时频变换的地震信号进行噪声压制。为了定量评估实验方法的去噪性能, 引入了峰值信噪比(PSNR)、结构相似性指数(SSIM)和均方根误差(RMSE)3个指标, 比较不同方法的噪声压制性能。数值实验结果表明, 基于时频变换的卷积神经网络(CNN)方法对常见的地震噪声类型(包括随机噪声、海洋涌浪噪声、陆地面波噪声)具有较好的噪声压制效果, 能够提高地震数据的信噪比。而Transformer模块的引入可进一步提高对上述3种常见地震数据噪声类型的压制效果, 进一步提升CNN模型的去噪性能。尽管该方法在数值实验中取得了较好的应用效果, 但仍有进一步优化的空间可供探索, 比如改进网络结构以适应更复杂的地震信号, 并探索与其他先进技术结合, 以提升地震噪声压制性能。
Noise suppression is an important research topic in seismology and seismic signal processing. Accurately suppressing seismic noises and extracting effective signals is a key step in seismological research and seismic monitoring. Traditional denoising methods have some shortcomings, such as insufficient flexibility, difficulty in dealing with complex noises, information loss, and dependence on manual feature extraction. In order to overcome these shortcomings, this paper probes in a method of time-frequency domain transform combined with deep learning and its application to noise reduction. Five neural network models (including FCN, Unet, CBDNet, SwinUnet and TransUnet) are constructed for noise suppression after the time-frequency transformation of seismic data. This paper introduces three indicators: peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and root mean square error (RMSE) for quantitative evaluation of denoising performance. Numerical experiments show that the convolutional neural network(CNN) method based on time-frequency transform can effectively suppress common noise types (including random noises, swell noises and surface waves) and improve the signal-to-noise ratio of seismic data. The introduction of the Transformer module can further reduce above-mentioned noises and enhance the denoising performance of the CNN model. Further research will focus on an improved network structure for more complex seismic signals and the combination with other advanced techniques to improve denoising performance.