CSEM pseudo-random signal processing method based on feature extraction and clustering identification
ZHANG Xian1,2, LI Diquan1,2, LI Jin3, HU Yanfang1,2
1. Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Ministry of Education, Changsha, Hunan 410083, China; 2. School of Geosciences and Info-Physics, Central South University, Changsha, Hunan 410083, China; 3. College of Information Science and Engineering, Hunan Normal University, Changsha, Hunan 410081, China
Abstract:The controlled-source electromagnetic (CSEM) data is susceptible to noise,which leads to unsatisfactory exploration effects. Human factors will exert a huge influence on traditional CSEM data processing which usually employs frequency point screening,abnormal elimination and other methods and the filtering method cannot retain pseudo-random effective signals. According to the recorded CSEM data in the time domain,we analyze the time-domain statistical characteristics of useful signals and noises in the CSEM data,and quantitatively identify and qualitatively analyze useful CSEM signals to address the above problems. As a resulta CSEM pseudo-random signal processing method based on feature extraction and clustering identification is proposed in this paper. Firstly,the sample library including two kinds of typical noises and pseudo-random signals is established,and features of the time and frequency domains of the sample library signals are analyzed. Then the time-domain statistical features are extracted,and the fuzzy C-means clustering algorithm is adopted to identify and eliminate the noise for retaining useful signals and reconstructing original CSEM data. Finally,the frequency spectrum of effective frequency points is extracted by digital coherence technology. Through the processing of simulated data and measured data,results show that the proposed method can identify and eliminate typical noises accurately and effectively,thereby significantly improving the quality of CSEM data. After being processed by the proposed method,the component Ex normalization electric field curve and wide field electromagnetic (WFEM) resistivity curve are smoother and more continuous, thus effectively increasing the signal-to-noise ratio of CSEM signals.
张贤, 李帝铨, 李晋, 胡艳芳. 基于特征提取与聚类识别的人工源电磁伪随机信号处理方法[J]. 石油地球物理勘探, 2022, 57(4): 973-981,1008.
ZHANG Xian, LI Diquan, LI Jin, HU Yanfang. CSEM pseudo-random signal processing method based on feature extraction and clustering identification. Oil Geophysical Prospecting, 2022, 57(4): 973-981,1008.
陈辉,邓居智,谭捍东,等. 大地电磁三维交错网格有限差分数值模拟中的散度校正方法研究[J]. 地球物理学报,2011,54(6):1649-1659.CHEN Hui,DENG Juzhi,TAN Handong,et al. Study on divergence correction method in three-dimensional magnetotelluric modeling with staggered-grid finite difference method[J]. Chinese Journal of Geophysics,2011,54(6):1649-1659.
[2]
CHAVE A D. Estimation of the magnetotelluric response function:the path from robust estimation to a stable maximum likelihood estimator[J]. Surveys in Geophysics,2017,38(5):837-867.
[3]
何继善. 广域电磁测深法研究[J]. 中南大学学报(自然科学版),2010,41(3):1065-1072.HE Jishan. Wide field electromagnetic sounding methods[J]. Journal of Central South University(Science and Technology),2010,41(3):1065-1072.
汤井田,李广,肖晓,等. 基于压缩感知重构算法的大地电磁强干扰分离[J]. 地球物理学报,2017,60(9):3642-3654.TANG Jingtian,LI Guang,XIAO Xiao,et al. Strong noise separation for magnetotelluric data based on a signal reconstruction algorithm of compressive sen-sing[J]. Chinese Journal of Geophysics,2017,60(9):3642-3654.
[6]
何继善. 广域电磁法和伪随机信号电法[M]. 北京:高等教育出版社,2010.HE Jishan. Wide Area Electromagnetic Method and Pseudorandom Signal Electrical Method[M]. Higher Education Press,Beijing,2010.
[7]
何继善,佟铁钢,柳建新. an序列伪随机多频信号数学分析及实现[J]. 中南大学学报(自然科学版),2009,40(6):1666-1671.HE Jishan,TONG Tiegang,LIU Jianxin. Mathematical analysis and realization of an sequence pseudo-random multi-frequencies signal[J]. Journal of Central South University(Science and Technology),2009,40(6):1666-1671.
[8]
何继善. 三元素集合中的自封闭加法与2n系列伪随机信号编码[J]. 中南大学学报(自然科学版),2010,41(2):632-637.HE Jishan. Closed addition in a three-element set and 2n sequence pseudo-random signal coding[J]. Journal of Central South University(Science and Technology),2010,41(2):632-637.
[9]
李帝铨,谢维,程党性. E-Ex广域电磁法三维数值模拟[J]. 中国有色金属学报,2013,23(9):2459-2470.LI Diquan,XIE Wei,CHENG Dangxing. Three-dimensional modeling for E-Ex wide field electromagnetic methods[J]. The Chinese Journal of Nonferrous Metals,2013,23(9):2459-2470.
[10]
汤井田,何继善. 水平电偶源频率测深中全区视电阻率定义的新方法[J]. 地球物理学报,1994,37(4):543-552.TANG Jingtian,HE Jishan. A new method to define the full-zone resistivity in horizontal electric dipole frequency soundings on a layered earth[J]. Chinese Journal of Geophysics,1994,37(4):543-552.
[11]
王顺国,熊彬,戴世坤. 据一维正反演分析广域电磁法E-Ex方式的分辨能力[J]. 中南大学学报(自然科学版),2013,44(9):3766-3775.WANG Shunguo,XIONG Bin,DAI Shikun. Analysis of resolution ability to E-Ex arrangement wide field electromagnetic method using 1-D modeling and inversion[J]. Journal of Central South University(Science and Technology),2013,44(9):3766-3775.
[12]
何继善,李帝铨,戴世坤. 广域电磁法在湘西北页岩气探测中的应用[J]. 石油地球物理勘探,2014,49(5):1006-1012.HE Jishan,LI Diquan,DAI Shikun. Shale gas detection with wide field electromagnetic method in North-western Hunan[J]. Oil Geophysical Prospecting,2014,49(5):1006-1012.
[13]
YANG X L,LI B,PENG C S,et al. Application of a wide-field electromagnetic method to shale gas exploration in South China[J]. Applied Geophysics,2017,14(3):441-448.
[14]
张乔勋,李帝铨,田茂军. 广域电磁法在赣南某盆地油气勘探中的应用[J]. 石油地球物理勘探,2017,52(5):1085-1092.ZHANG Qiaoxun,LI Diquan,TIAN Maojun. Application of wide field electromagnetic method to the hydrocarbon exploration in a basin of South Jiangxi[J]. Oil Geophysical Prospecting,2017,52(5):1085-1092.
[15]
刘春明,佟铁钢,何继善. 多种电磁法在某金矿的野外勘探应用[J]. 中国有色金属学报,2013,23(9):2422-2429.LIU Chunming,TONG Tiegang,HE Jishan. Exploration of various electromagnetic method in some gold mine[J]. The Chinese Journal of Nonferrous Metals,2013,23(9):2422-2429.
[16]
张岩,李新月,王斌,等. 基于联合深度学习的地震数据随机噪声压制[J]. 石油地球物理勘探,2021,56(1):9-25,56.ZHANG Yan,LI Xinyue,WANG Bin,et al. Random noise suppression of seismic data based on joint deep learning[J]. Oil Geophysical Prospecting,2021,56(1):9-25,56.
[17]
张必明,蒋奇云,莫丹,等. 电磁勘探数据粗大误差处理的一种新方法[J]. 地球物理学报,2015,58(6):2087-2102.ZHANG Biming,JIANG Qiyun,MO Dan,et al. A novel method for handling gross errors in electromagnetic prospecting data[J]. Chinese Journal of Geophysics,2015,58(6):2087-2102.
[18]
MO D,JIANG Q Y,LI D Q,et al. Controlled-source electromagnetic data processing based on gray system theory and robust estimation[J]. Applied Geophy-sics,2017,14(4):570-580.
[19]
陈超健,蒋奇云,莫丹,等. 基于灰色判别准则和有理函数滤波的伪随机电磁数据去噪[J]. 地球物理学报,2019,62(10):3854-3865.CHEN Chaojian,JIANG Qiyun,MO Dan,et al. De-noising pseudo-random electromagnetic data using gray judgment criterion and rational function filtering[J]. Chinese Journal of Geophysics,2019,62(10):3854-3865.
[20]
杨洋,何继善,李帝铨. 在频率域基于小波变换和Hilbert解析包络的CSEM噪声评价[J]. 地球物理学报,2018,61(1):344-357.YANG Yang,HE Jishan,LI Diquan. A noise evaluation method for CSEM in the frequency domain based on wavelet transform and analytic envelope[J]. Chinese Journal of Geophysics,2018,61(1):344-357.
[21]
YANG Y,LI D Q,TONG T G,et al. Denoising controlled-source electromagnetic data using least-squares inversion[J]. Geophysics,2018,83(4):E229-E244.
[22]
LI G,HE Z S,TANG J T,et al. Dictionary learning and shift-invariant sparse coding denoising for controlled-source electromagnetic data combined with complementary ensemble empirical mode decomposition[J]. Geophysics,2021,86(3):E185-E198.
[23]
李晋,汤井田,燕欢,等. 基于递归分析和聚类的大地电磁信噪辨识及分离[J]. 地球物理学报,2017,60(5):1918-1936.LI Jin,TANG Jingtian,YAN Huan,et al. Identification and separation of magnetotelluric signal and noise based on recurrence analysis and clustering[J]. Chinese Journal of Geophysics,2017,60(5):1918-1936.
[24]
LI J,ZHANG X,GONG J Z,et al. Signal-noise identification of magnetotelluric signals using fractal-entropy and clustering algorithm for targeted denoising[J]. Fractals,2018,26(2):1840011.
[25]
朱占龙,刘永军. 融合混沌优化和改进模糊聚类的图像分割算法[J]. 电子学报,2020,48(5):975-984.ZHU Zhanlong,LIU Yongjun. A novel algorithm by incorporating chaos optimization and improved fuzzy C-means for image segmentation[J]. Acta Electronica Sinica,2020,48(5):975-984.
[26]
胡英,陈辉,贺振华,等. 基于地震纹理属性和模糊聚类划分地震相[J]. 石油地球物理勘探,2013,48(1):114-120.HU Ying,CHEN Hui,HE Zhenhua,et al. Seismic facies classification based on seismic texture attributes and fuzzy clustering[J]. Oil Geophysical Prospecting,2013,48(1):114-120.
[27]
蒋奇云. 广域电磁测深仪关键技术研究[D]. 湖南长沙:中南大学,2010,67-78.JIANG Qiyun. Study on the Key Technology of Wide Field Electromagnetic Sounding Instrument[D]. Central South University,Changsha,Hunan,2010,67-78.
[28]
杨博,张祥国,刘展,等. 基于聚类和多元地质统计学的电—震联合建模约束反演技术及应用[J]. 石油地球物理勘探,2021,56(3):670-677.YANG Bo,ZHANG Xiangguo,LIU Zhan,et al. Technique and application of joint magnetotelluric and seismic modeling and constrained inversion based on clustering and multivariate geostatistics[J]. Oil Geophysical Prospecting,2021,56(3):670-677.
[29]
温广瑞,陈征,张志芬. 基于模糊C均值聚类和转子轴心轨迹特征的转子状态诊断[J]. 振动与冲击,2019,38(15):27-35.WEN Guangrui,CHEN Zheng,ZHANG Zhifen. Rotor state diagnosis based on fuzzy C-mean value clustering and its axial center orbit features[J]. Journal of Vibration and Shock,2019,38(15):27-35.