基于VMD-LSTM对大地电磁信号进行噪声检测和预测重构  

VMD-LSTM-based noise detection and predictive reconstruction for magnetotelluric signals

作　　者：李博 李长伟[1,2] 罗润林 吕玉增[1,2] 王占 LI Bo;LI Chang-Wei;LUO Run-Lin;LU Yu-Zeng;WANG Zhan(College of Earth Sciences,Guilin University of Technology,Guilin 541000,China;Guangxi Key Laboratory of Exploration for Hidden Metallic Ore Deposits,Guilin 541000,China)

机构地区：[1]桂林理工大学地球科学学院,广西桂林541000 [2]广西隐伏金属矿产勘查重点实验室,广西桂林541000

出　　处：《物探与化探》2025年第1期100-117,共18页Geophysical and Geochemical Exploration

基　　金：广西自然科学基金项目(2020GXNSFAA297079);国家自然科学基金项目(42274182)。

摘　　要：在大地电磁法中,强干扰噪声限制了该方法还原真实地下结构的精度,会对后期资料解释造成不良影响。本文基于大地电磁时间序列的特点,对不同类型噪声的特征进行分析,提出了一种基于VMD(变分模态分解)与LSTM(长短时记循环神经网络)预测重构的信号去噪技术。首先通过VMD信号分解算法对原始大地电磁数据进行去基线漂移处理,对处理好的时间序列继续通过VMD分解为多个不同的模态IMFs,选用含噪声轮廓信息的RSE分量中无干扰数据训练LSTM时间序列检测模型,对RSE分量进行识别并标记含噪时间段,计算噪声的步长,将噪声信息传递给原始信号并截断删除。最后通过对IMFs训练LSTM多维预测模型,对空缺的位置预测不同模态下的信号,将所有模态输出结果叠加可得大地电磁预测信号,重构信号后针对VMD方法识别度不高的尖脉冲噪声进行二次信噪分离即完成去噪。通过该技术可精确识别大地电磁信号中的强干扰噪声,只针对噪声发生时间段进行处理,有效保护了信号中无干扰数据,且预测数据误差可控制在大地电磁信号数据处理的误差允许范围内,去噪效果显著。In thereconstruction of actual subsurface structures,strong noise limits the accuracy of the magnetotelluric(MT)method,causing adverse effects on later data interpretation.Given this and the characteristics of the MT time series,this study analyzed different types of noise in the MT time series,proposing a signal denoising technique based on variational mode decomposition(VMD)and long short-term memory(LSTM)predictive reconstruction.First,baseline drift correctionwas performed for the original MT datausing the VMD signal decomposition algorithm.Then,the time series was further decomposed into multiple different intrinsic mode functions(IMFs)through VMD.The LSTM time series detection model was trained using interference-free data in the RSE component,which was then identified.Afterward,the time intervals containing noise weremarked,the increasement of noise was calculated,and the noise information wastransmitted to the original signal for truncation and removal.Finally,an LSTM multi-dimensional prediction model was trained for the IMFs,followed by the prediction of missing values under various modes.The predicted results under all modes were combined to obtain the final predicted MT signals.After signal reconstruction,a secondary signal-noise separationwas performed for spike-pulse noise that was not effectively identified through VMD.TheVMD-LSTM-based signal denoisingtechnique can accurately identify strong noise in MT signals by merely processing the time series intervals containing noise,thuseffectively preserving interference-free data.Moreover,its prediction errors can berestricted within the allowable error range of the data processing for MT signals.Therefore,this technique enjoys significant denoising effects.

关 键 词：大地电磁 变分模态分解VMD 长短时循环神经网络LSTM 深度学习 信号去噪 

分 类 号：P631[天文地球—地质矿产勘探]