TITLE:
Seismic Multiple Attenuation Method Based on Spectral Subtraction and Convolutional Dual Attention Mechanism
AUTHORS:
Mingming Fang, Zhonghua Ma
KEYWORDS:
Seismic Multiple Attenuation, Deep Learning, U-Net, Convolutional Block Attention Module, Spectral Subtraction
JOURNAL NAME:
Journal of Applied Mathematics and Physics,
Vol.13 No.12,
December
18,
2025
ABSTRACT: Multiple reflections in seismic exploration data severely degrade subsurface imaging accuracy and interpretation reliability. Traditional multiple attenuation methods exhibit limited performance under complex geological conditions, while existing deep learning approaches struggle to simultaneously suppress high-energy multiples and preserve the structural continuity and amplitude fidelity of primary reflections. This paper proposes an enhanced U-Net architecture termed Seismic-CBAM-UNet, employing a two-stage cascaded strategy of “frequency-domain preprocessing + attention-enhanced network”. The method first applies spectral subtraction in the frequency domain to preprocess raw seismic data, specifically targeting multiples concentrated in the low-frequency band. Subsequently, Convolutional Block Attention Modules are embedded within the network architecture, integrating both channel and spatial attention mechanisms to enable adaptive focusing on geologically significant reflection events. Experimental results demonstrate that our method outperforms Wiener filtering and wavelet thresholding across multiple evaluation metrics: achieving 1.62 dB SNR improvement, 61.3% multiple attenuation rate, 92.10% primary preservation rate, and 0.798 structural similarity index. The proposed approach effectively suppresses multiple interference while precisely maintaining geological structure continuity and amplitude characteristics, providing reliable technical support for high-precision imaging in complex seismic environments.