摘要
大地电磁(MT)法是一种广泛应用于地球物理勘探的核心技术,它常用于地质调查、资源勘探和地球动力学研究.然而,MT数据容易受到复杂噪声的干扰,这些噪声包括非线性和非平稳噪声,这显著降低了数据的质量和解释的准确性.传统的去噪方法,如稀疏表示和小波变换,虽然能够在一定程度上改善数据质量,但在应对多样化的噪声类型时,存在调参复杂、鲁棒性不足等局限性.为了解决这些问题,本文提出了一种基于逆自编码器和通道注意力机制的创新MT数据去噪方法.逆自编码器通过升维和降维过程,增强了捕捉复杂信号特征的能力,实现了高效的信噪识别与信号拟合;通道注意力机制通过动态调整特征通道权重,进一步提升了去噪精度.在此基础上,设计了一个端到端深度学习框架,用于处理复杂噪声环境中的MT数据.实验结果表明,该方法在多种噪声条件下均表现出优越的去噪性能.在相关系数(CORC)、归一化均方根误差(NRMSE)和信噪比(SNR)等指标上显著优于传统方法;此外,在视电阻率-相位曲线和电磁场极化方向分析中,本文方法展示了更高的鲁棒性和一致性.这表明,本文方法能够有效提高MT数据的质量和可解释性,为地球物理勘探提供了可靠的技术支持.
The magnetotelluric(MT)method is a core technology widely used in geophysical exploration and commonly used in geological surveys,resource exploration and geodynamic research.However,MT data are susceptible to complex noise,including nonlinear and non-stationary noise,which significantly reduces data quality and interpretation accuracy.Although traditional denoising methods(such as sparse representation and wavelet transform)can improve the quality of some data,they have limitations such as complex parameter adjustment and insufficient robustness when dealing with diverse noise types.In order to solve the above problems,this paper proposes an innovative MT data denoising method based on inverse autoencoder and channel attention mechanism.The inverse autoencoder enhances the ability to capture complex signal features through the process of dimensionality increase and dimensionality reduction,achieving efficient signal-to-noise identification and signal fitting;the channel attention mechanism further improves denoising accuracy by dynamically adjusting the weight of feature channels.On this basis,an end-to-end deep learning framework is designed to process MT data in complex noisy environments.Experimental results show that this method exhibits superior denoising performance under a variety of noise conditions.It is significantly better than traditional methods in indicators such as correlation coefficient(CORC),normalized root mean square error(NRMSE)and signal-to-noise ratio(SNR);in addition,in the analysis of apparent resistivity-phase curve and electromagnetic field polarization direction,the method in this paper shows higher robustness and consistency.This shows that the method in this paper can effectively improve the quality and interpretability of MT data and provide reliable technical support for geophysical exploration.
作者
余年
计铭杰
张超
Yu Nian;Ji Mingjie;Zhang Chao(School of Electrical Engineering,Chongqing University,Chongqing 400044,China;School of Resources and Safety Engineering,Chongqing University,Chongqing 400044,China)
出处
《地球与行星物理论评(中英文)》
2025年第6期665-673,共9页
Reviews of Geophysics and Planetary Physics
基金
国家自然科学基金优秀青年基金资助项目(42222404)
国家重点研发项目课题(2023YFC2907104)。
关键词
大地电磁信号去噪
深度学习
逆自编码器
通道注意力机制
magnetotelluric signal denoising
deep learning
inverse autoencoder
channel attention mechanism
