【目的】地震资料中的噪声严重影响着对地下地层信息的准确解释。基于地震资料中横向相关性较强的有效信号在Curvelet域分布在特定系数上，而随机噪声在Curvelet域通常会均匀分布于所有系数，可对信号进行更有效的分离。【方法】基于注意力机制卷积神经网络能够聚焦图像的重要特征，自适应提取关键信息的特点，提出一种基于Curvelet变换和注意力机制卷积神经网络(Curvelet-AU-Net)的地震数据噪声衰减方法。首先，将含噪声的地震数据通过Curvelet变换得到Curvelet变换系数，分析有效信号和噪声在Curvelet域的分布情况。其次，使用加入CBAM注意力机制的 U-Net 网络，以含噪地震数据的 Curvelet 变换系数制作训练集作为输入数据，用无噪地震数据的 Curvelet 变换系数作为标签，通过比较实际输出与标签的损失函数值，并逐层反向传播梯度来更新网络参数，当损失函数值达到最小时，网络训练完成。最后，将测试数据输入训练好的网络模型中，再对网络输出数据进行 Curvelet 反变换即可得到地震数据去噪结果。【结果和结论】模拟数据与实际数据处理结果表明，与传统方法和普通卷积网络相比，该方法在不同噪声水平和尺度条件下对常见噪声(如随机噪声等)的衰减效果更优，获得的地震信号信噪比和保真度更高。由于该方法融合了Curvelet变换的稀疏表示优势和深度学习模型的自适应性，将为地震数据噪声衰减提供一种新的解决途径。

[Objective] Noise in seismic data significantly affects the accurate interpretation of subsurface stratigraphic information. Given that effective signals with pronounced lateral correlations in seismic data are distributed in specific coefficients but random noise typically spreads uniformly over all coefficients in the curvelet domain, more effective separation of signals can be achieved. [Method] The convolutional neural network based on the attention mechanism can adaptively extract key information by focusing on important features of images. Hence, this study proposed a noise attenuation method for seismic data using a convolutional neural network based on the curvelet transform and attention mechanism (Curvelet-AU-Net). First, the curvelet coefficients of noise-containing seismic data were obtained through curvelet transform to analyze the distributions of effective signals and noise in the curvelet domain. Second, a U-Net network with a convolutional block attention module (CBAM) was employed, with the curvelet coefficients of noise-containing seismic data as input data for training and the curvelet coefficients of noise-free seismic data as labels. Then, the parameters of the network were updated by comparing the loss function values of actual outputs and labels and backpropagating gradients layer by layer. The network training was completed as the loss function value reached its minimum. Finally, the test data were put into the trained network model. The denoising results of seismic data were obtained by performing inverse curvelet transform on the network output data. [Results and Conclusions] The processing results of simulation and actual data show that compared to conventional methods and ordinary convolutional networks, the method proposed in this study demonstrates superior attenuation effects on common noise (e.g., random noise) under different noise levels and scales, achieving higher signal-to-noise ratios and fidelity for seismic signals. This method, integrating the sparse representation of the Curvelet transform and the adaptability of deep learning models, provides a novel approach for the noise attenuation of seismic data.