在复杂的勘探环境下，原始采集的地震数据包含大量随机噪声会严重影响地震资料的质量，为后续地质解释带来困难。针对该问题，提出了一种结合可变形卷积和注意力机制的地震信号去噪模型，即DnDCNN（denoising deformable convolutional neural network）网络。首先，在DnCNN网络中引入融合可变形卷积的注意力机制，使网络更加关注有效信号区域，减少细节信息的丢失；其次，将网络中堆叠的标准卷积替换为可变形卷积和标准卷积串联模式，提高不变性特征提取能力；最后，将批量归一化和残差学习策略融合，实现网络快速收敛和信噪分离。通过对模拟和实际地震数据进行验证，结果表明该网络模型在不同噪声水平下可以有效压制随机噪声、保留更多细节信息，对微弱信号去噪表现出更优秀的信噪比。

In complex exploration environments, the raw seismic data collected contains a large amount of random noise, which seriously affects the quality of seismic data and brings difficulties for subsequent geological interpretation. In response to this issue, a seismic signal denoising model combining deformable convolution and attention mechanism has been proposed to solve the above problems, namely DnDCNN (denoising deformable convolutional neural network). Firstly, the attention mechanism that integrates deformable convolutions was introduced into DnCNN, making the network more focused on the effective signal area and reducing the loss of detailed information. Secondly, the concatenated standard convolution was replaced with deformable convolution and standard convolution concatenated mode, which improves the network's ability to extract invariant features. Finally, batch normalization and residual learning strategies were integrated to achieve fast network convergence and signal-to-noise separation. The validation of simulated and actual earthquake data has shown that the network model can effectively suppress random noise, preserve more detailed information, and exhibit better signal-to-noise ratio in denoising weak signals at different noise levels.