摘要： 地震数据中的面波作为典型的相干噪声，因其能量强、传播方向复杂且波形特征与有效信号高度相似，成为地震数据去噪中的主要难点。现有深度学习方法依赖网络深度堆叠或单模态特征表征，虽能压制面波，但存在多尺度特征融合不足和长程依赖建模局限，易导致有效信号模糊或低频成分丢失。为此，本文提出一种多尺度注意力-空洞卷积融合网络（MA-DCNet），由方向自适应特征增强模块（DAFEM）、多尺度特征融合模块（MSFFM）、通道局部增强注意力模块（CLAM）和全局上下文自注意力模块（GCSAM）组成。DAFEM利用多轴自注意力机制自适应增强关键方向信息，MSFFM通过风车卷积构建多尺度感受野，CLAM结合通道注意力与深度可分离卷积增强同相轴连续性，GCSAM基于全局上下文注意力建立全道集依赖关系以区分面波与有效信号。实验表明，相比四种先进方法，MA-DCNet在显著压制面波的同时更好地保持同相轴连续性。

Abstract: Surface waves in seismic data are typical coherent noise. They pose major denoising challenges due to strong energy, complex propagation directions, and waveform similarity to valid signals. Existing deep learning methods often rely on deep network stacking or single-modal feature representation. While these approaches improve surface wave suppression, they suffer from insufficient multi-scale feature fusion and limited long-range dependency modeling, leading to structural blurring or low-frequency loss in valid signals. In this paper, we propose a Multi-scale Attention and Dilated Convolution Fusion Network (MA-DCNet) for surface wave suppression. MA-DCNet has four main modules, including direction-adaptive feature enhancement module (DAFEM), multi-scale feature fusion module (MSFFM), channel-localized attention module (CLAM) and global context self-attention module (GCSAM). DAFEM employs multi-axis self-attention to adaptively enhance direction-critical information. MSFFM constructing multi-scale receptive fields through windmill convolution. CLAM integrating channel attention with depthwise separable convolution to preserve event continuity. GCSAM establishing full-trace-gather dependency relationships using global contextual attention to discriminate surface waves from valid signals. Experimental results demonstrate that MA-DCNet outperforms four state-of-the-art deep learning methods, achieving superior surface wave suppression while better preserving seismic signal integrity.