基于多尺度编码-解码特征融合网络的叠前地震数据工业噪声压制

Seismic data acquisition in mature oil fields or industrial areas often suffers from interference caused by industrial machinery noise, such as that generated by drilling rigs, pumping units, and operating machinery. This noise significantly degrades the signal-to-noise ratio, hindering subsequent processing and interpretation. Unlike random noise, industrial noise exhibits non-stationary characteristics, strong amplitudes, and often overlaps with the desired signal band. Traditional and deep learning methods designed for random noise attenuation are often ineffective against this type of noise. To address this challenge, we propose a Multi-Scale Codec Feature Fusion Network (MCFF-Net). This network employs a multi-scale structure to learn multi-scale features of industrial noise and effective signals using different convolution structures within multi-scale modules. By fusing multi-scale features and incorporating both deep and shallow contextual information, MCFF-Net effectively separates industrial noise from contaminated seismic data. Experiments conducted on both synthetic and real seismic gathers demonstrate the effectiveness of MCFF-Net. Compared to traditional methods including DnCNN and GAN, MCFF-Net achieves superior performance across four metrics.