波谱学杂志2026，Vol.43Issue(3)：307-320,14.DOI:10.11938/cjmr20263201
基于深度学习的低场NMR弛豫时间谱反演新方法
A Deep Learning-based Method for LF-NMR Relaxation Time Spectrum Inversion
摘要
Abstract
Low-field nuclear magnetic resonance(LF-NMR)relaxation time spectrum analysis has been widely applied in petroleum engineering and geological exploration.However,conventional NMR relaxation time spectrum inversion algorithms require time-domain signals with high signal-to-noise ratios(SNRs),which are often difficult to obtain in practical scenarios such as on-site core analysis.To achieve accurate and stable inversion of low-SNR NMR signals,this paper proposes a deep learning-based inversion network named UDMCA(U-Net Denoising and Multi-scale Cross Attention Inversion Network),which incorporates a multi-scale cross-attention mechanism to extract and fuse relaxation features at multiple scales according to the decay characteristics of NMR signals,thereby improving inversion accuracy for low-SNR multi-component signals.In addition,a U-Net denoising module is introduced to further enhance noise robustness.Experimental results show that the proposed method achieves accurate and stable inversion for low-SNR signals and demonstrates good applicability in core NMR signal inversion,providing an effective solution for low-SNR LF-NMR data processing and inversion.关键词
低场核磁共振/弛豫时间谱/深度学习/一维反演Key words
low-field nuclear magnetic resonance(LF-NMR)/relaxation time spectrum/deep learning/1D inversion分类
数理科学引用本文复制引用
刘可文,姜予康,陈方,陈俊飞,卢媛,陈黎,刘朝阳..基于深度学习的低场NMR弛豫时间谱反演新方法[J].波谱学杂志,2026,43(3):307-320,14.基金项目
国家自然科学基金资助项目(22404165,22374158,22204168,22574167) （22404165,22374158,22204168,22574167）
中国科学院战略性先导科技专项(XDB0540301) （XDB0540301）
国家重大科研仪器研制项目(22327901). （22327901）
