基于深度学习与联合降噪的可控源音频大地电磁抗干扰数据处理方法研究  

Anti-interference processing for CSAMT based on deep learning and joint de-noising

作　　者：刘卫强 林品荣[2] 陈儒军[3] 张昆[1] 陈昌昕 刘旭 LIU WeiQiang;LIN PinRong;CHEN RuJun;ZHANG Kun;CHEN ChangXin;LIU Xu(SinoProbe Laboratory,Chinese Academy of Geological Sciences,Beijing 100037,China;Key Laboratory of Geophysical Electromagnetic Probing Technologies of Ministry of Natural Resources,Institute of Geophysical and Geochemical Exploration of Chinese Academy of Geological Science,Langfang 065000,China;Institute of Geosciences and Info-Physics,Central South University,Changsha 410083,China;School of Geophysics and Information Technology,China University of Geosciences,Beijing 100083,China)

机构地区：[1]自然资源部深地科学与探测技术实验室,中国地质科学院,北京100037 [2]自然资源部地球物理电磁法探测技术重点实验室,中国地质科学院地球物理地球化学勘查研究所,廊坊065000 [3]中南大学地球科学与信息物理学院,长沙410083 [4]中国地质大学(北京)地球物理与信息技术学院,北京100083

出　　处：《地球物理学进展》2024年第4期1457-1473,共17页Progress in Geophysics

基　　金：自然资源部深地科学与探测技术实验室开放课题基金(SinoProbe Lab 202207);自然资源部地球物理电磁法探测技术重点实验室开放课题基金(KLGEPT202203)联合资助;国家自然科学基金(42304091)。

摘　　要：可控源音频大地电磁法(CSAMT)是在天然源大地电磁法(MT)基础上发展起来的一种近地表地球物理探测方法.随着社会经济的发展,CSAMT数据质量受到噪声干扰的严重影响.在实际勘探中,电磁场的时间序列通常与大尺度趋势项漂移、短时突发强干扰和尖峰脉冲干扰相叠加,导致计算的电阻率谱失真.本文提出一种基于深度学习与联合降噪的可控源音频大地电磁抗干扰数据处理方法.首先,提出了一种层状大地可控源音频大地电磁时间序列正演算法,用于生成不含噪声干扰的标准电磁信号.然后,训练了长短时记忆神经网络(LSTM)来识别噪声类型.最后,采用改进经验模态分解、相关分析数据挑选和稳健统计来对CSAMT时间序列进行联合降噪.仿真数据测试结果表明,LSTM对多源混合噪声干扰的识别准确率可以达到95%以上,联合降噪算法可以将数据误差从20%左右降低到3%以下.最后,将该方法应用于内蒙古某矿区实测数据集,有效提高了低频段视电阻率和视相位的精度.Controlled-Source Audio Magnetotelluric(CSAMT)is a near-surface geophysical method that developed on the basis of Magnetotelluric method(MT).With the development of social economy,the data quality of CSAMT has also been seriously disturbed by noise interference.In practical exploration,the time series of electromagnetic field is usually superimposed with large-scale trend drift,short-term sudden strong interference and peak impulsive outliers,resulting in the distortion of the calculated resistivity spectrum.In this paper,an anti-interference processing method based on deep learning and joint de-noising is proposed to preprocess CSAMT time series.Firstly,a forward algorithm of electromagnetic time series of layered earth controllable source is proposed,which is used to generate standard electromagnetic signals without noise interference.Then,a Long and Short Term Memory Neural Network(LSTM)classifier is trained to recognize the noise.Finally,the improved Empirical Mode Decomposition(EMD)algorithm,correlation based data selection algorithm and robust statistical algorithm are jointly used to de-noise the CSAMT time series.The test results by simulated data show that the recognition accuracy of LSTM for noise interference can reach more than 95%,and the three noise reduction algorithms can reduce the data error from about 20%to less than 3%.Finally,the proposed method is applied to the actual data set of a metal mining area in Inner Mongolia.the accuracy of low-frequency resistivity and phase was effectively improved.

关 键 词：可控源音频大地电磁 信噪识别 信噪分离 长短时记忆神经网络 联合降噪算法 

分 类 号：P631[天文地球—地质矿产勘探]