Denoising single-ship towed MCSEM data with adaptive frequency-division matching filter
-
摘要:
海洋可控源电磁法(MCSEM, Marine Controlled-Source Electromagnetic Method)是一种工业化的主动源海洋电磁勘探方法, 近年来在海底油气资源探测中收效显著, 通过测量海底地下介质的电阻率变化, 能够有效降低地震勘探方法的多解性.MCSEM的工作方式主要有单船拖曳与双船拖曳两类.单船拖曳信号具有强时变特征, 近、中、远收发距信号强度变化极大.由于海水环境较为复杂, 数据受到洋流、背景场、仪器等多种因素干扰, 使得小收发距数据信噪比较高, 中、远收发距数据信噪比较低, 因此MCSEM数据随机干扰压制方法的研究难点在于适应数据的强时变特征.本文利用发射机数据与接收机数据的时间(空间)和频率对应关系, 提出基于正则化条件和衰减函数约束的自适应分频匹配滤波方法.该方法首先通过计算原始接收机数据基频的振幅随偏移距变化(MVO, Magnitude Versus Offset)曲线获取衰减函数, 然后利用该衰减函数将发射机所有频点数据进行振幅衰减, 减低MCSEM发射机与接收机数据对应振幅的强时变差异, 最后利用基于整形正则化条件的自适应匹配滤波方法压制接收机数据中的强振幅随机噪声.模型数据与实际数据的处理结果表明, 本文提出的方法能够有效压制MCSEM数据中不同收发距的随机噪声干扰, 保护具有强时变特征的接收机数据, 提升整体数据质量.
Abstract:The Marine Controlled-Source Electromagnetic Method (MCSEM) is an industrialized marine Electromagnetic (EM) exploration method that has achieved remarkable results in the exploration of offshore oil and gas resources in recent years, by measuring the resistivity variation of the seabed media, the multi-solution of seismic exploration methods can be effectively reduced. MCSEM mainly works in single ship towing and double ship towing manner. The single ship towing EM signal has a strong time-varying characteristic, and the amplitude of EM signal varies greatly with transmitter-receiver offsets. Due to the complicated environment in the ocean environment, the data are interfered by various factors such as ocean current, background field and instrumental noise, which makes the SNRs of the data for small transmitter-receiver offsets are very high, while the SNRs of the data for medium to large transmitter-receiver offsets are very low. Therefore, the difficulty of the research of suppressing random noise of MCSEM data lies in adaptation to the strong time-varying characteristics of data. In this paper, the time (space) and frequency correlation between transmitting and receiving data is used to deal with the strong time-varying characteristics of MCSEM signal by using adaptive frequency-divided matched filter based on regularization condition and attenuation function constraints. The attenuation function of receiving data is obtained by calculating the fundamental frequency Magnitude Versus Offset (MVO) curve of the original receiving data, and the attenuation function is used to attenuate the amplitude of all transmitting data at different frequencies to reduce the strong time-varying difference between the amplitude of the MCSEM transmitting and receiving data. Finally, the adaptive matched filtering method with regularization conditions is used to suppress the strong random noise of amplitude in the receiving data. The processing results of synthetic and field survey data show that the method presented in this paper can effectively suppress the random noise at different transmitter-receiver offsets in MCSEM data, and protect the receiving data with strong time-varying characteristics, which will improve the data quality.
-
-
图 1
MCSEM自适应分频匹配滤波消噪处理流程
Figure 1.
MCSEM denoising workflow based on adaptive frequency-division matching filter
图 9
远收发距接收机数据及概率密度分布图
Figure 9.
Long-offset receiving data and probability density distribution
-
Amun dsen L, Løseth L, Mittet R, et al. 2006. Decomposition ofelectromagnetic fields into upgoing and downgoing components. Geophysics, 71(5): G211-G223. doi: 10.1190/1.2245468
Behrens J P. 2005. The detection of electrical anisotropy in 35 Ma Pacific lithosphere: results from a marine controlled-source electromagnetic survey and implications for hydration of theupper mantle[Ph. D. thesis]. San Diego: University of California.
Constable S, Weiss C J. 2006. Mapping thin resistors and hydrocarbons with marine EM methods: Insights from 1D modeling. Geophysics, 71(2), G43-G51. doi: 10.1190/1.2187748
Fomel S. 2007. Shaping regularization in geophysical-estimation problems. Geophysics, 72(2): R29-R36. doi: 10.1190/1.2433716
Fomel S. 2009. Adaptive multiple subtraction using regularized nonstationary regression. Geophysics, 74(1): V25-V33. doi: 10.1190/1.3043447
Key K. 2009. 1D inversion of multicomponent, multifrequency marine CSEM data: Methodology and synthetic studies for resolving thin resistive layers. Geophysics, 74(2), F9-F20. doi: 10.1190/1.3058434
Li S Y, Jiang S Q, Wang Y Y, et al. 2016. A wavelet correction method for the seawater turbulence noise in marine controlled-source electromagnetic data. Geophysical Prospecting for Petroleum (in Chinese), 55(5): 657-663. doi: 10.3969/j.issn.1000-1441.2016.05.004
Li S Y, Zhao Y C, Sun W B, et al. 2019. New wavelet-based denoising method for marine controlled source electromagnetic data. Chinese Journal of Scientific Instrument (in Chinese), 40(2): 67-74.
Li Y G, Duan S M. 2014. Data preprocessing of marine controlled-source electromagnetic data. Periodical of Ocean University of China (in Chinese), 44(10): 106-112.
Li Z L. 2017. Study on marine controlled-source electromagnetic data de-noising based on adaptive filtering method[Ph. D. thesis] (in Chinese). Wuhan: China University of Geosciences.
Lin X, Wei W B, Jing J E, et al. 2009. Study on improving MCSEM signal-to-noise ratio. Progress in Geophysics (in Chinese), 24(3): 1047-1050. doi: 10.3969/j.issn.1004-2903.2009.03.032
Liu N. 2015. Preprocessing and research of denoising methods for marine controlled source electromagnetic data[Ph. D. thesis] (in Chinese). Changchun: Jilin University.
Myer D, Constable S, Key K. 2011. Broad-band waveforms and robustprocessing for marine CSEM surveys. Geophysical Journal International, 184(2): 689-698. doi: 10.1111/j.1365-246X.2010.04887.x
Tan S. 2014. EMD algorithm research on marine electromagnetic signal de-noising[Master's thesis] (in Chinese). Chengdu: Chengdu University of Technology.
Tikhonov A. 1963. Solution of incorrectly formulated problems and the regularization method. Soviet Mathematics Doklady, 5: 1035-1038.
Wang S M, Di Q Y, Wang R, et al. 2018. Removing airwave effects usingelectromagnetic fields decomposition for 3D MCSEM. Chinese Journal of Geophysics (in Chinese), 61(2): 742-749, doi: 10.6038/cjg2018L0033.
Wang X, Shen J S. 2013. Research on air wave suppression method in marine controlled source electromagnetic exploration. //The Chinese Geophysics 2013 (in Chinese). Kunming: Chinese Geophysical Society.
Wang Z S, Zhao N, Wang Z G. 2021.3D MCSEM modeling usingGMG method in tri-axial anisotropy media. Progress in Geophysics (in Chinese), 36(4): 1724-1733, doi: 10.6038/pg2021EE0348.
Wright D. 2015. Quantifying the effect of the air/water interface in marine active source EM. Journal of Applied Geophysics, 118: 92-105. doi: 10.1016/j.jappgeo.2015.03.028
Yang Y, Li D Q, Tong T G, et al. 2018. Denoising controlled-source electromagnetic data using least-squares inversion. Geophysics, 83(4): E229-E244. doi: 10.1190/geo2016-0659.1
Yin C C, Zhang W Q, Su Y, et al. 2022. Three-dimensional MCSEM forward modeling based on domain decomposition. Progress in Geophysics (in Chinese), 37(1): 469-477, doi: 10.6038/pg2022FF0388.
Yu C X. 2010. Study on marine controlled-source electromagnetic signal processing[Master's thesis] (in Chinese). Beijing: China University of Geosciences (Beijing).
Zhang P F. 2020. The study of marine controlled source electromagnetic method de-noising based on feature learning. [Ph. D. thesis] (in Chinese). Beijing: China University of Geosciences (Beijing).
Zhou L, Zhu Z M, Tan S. 2013. Application of wavelet shrinkage threshold denoising in ocean electromagnetic. Information & Communications (in Chinese), (10): 1-2.
Zhou W Q, Zhou P, Dai Y S, et al. 2019. Research on evaluation method of MT noise denoising effect in marine controlled-source electromagnetic exploration. Progress in Geophysics (in Chinese), 34(3): 1268-1280, doi: 10.6038/pg2019CC0156.
李肃义, 蒋善庆, 王跃洋等. 2016. 海洋可控源电磁数据中海水扰动噪声的小波校正方法研究. 石油物探, 55(5): 657-663. doi: 10.3969/j.issn.1000-1441.2016.05.004
李肃义, 赵彦超, 孙卫斌等. 2019. 海洋可控源电磁数据的新型小波基消噪方法. 仪器仪表学报, 40(2): 67-74. https://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201902007.htm
李予国, 段双敏. 2014. 海洋可控源电磁数据预处理方法研究. 中国海洋大学学报, 44(10): 106-112. https://www.cnki.com.cn/Article/CJFDTOTAL-QDHY201410015.htm
李泽林. 2017. 基于自适应滤波的海洋可控源数据噪声处理方法研究[博士论文]. 武汉: 中国地质大学.
林昕, 魏文博, 景建恩等. 2009. 提高海洋可控源电磁法信噪比的方法研究. 地球物理学进展, 24(3): 1047-1050. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWJ200903033.htm
刘宁. 2015. 海洋可控源电磁数据典型预处理及几种去噪方法研究[博士论文]. 长春: 吉林大学.
谭帅. 2014. 关于海洋电磁信号消噪的EMD算法研究[硕士论文]. 成都: 成都理工大学.
王书明, 底青云, 王若等. 2018. 三维MCSEM利用电磁场分解消除空气波效应. 地球物理学报, 61(2): 742-749, doi: 10.6038/cjg2018L0033.
汪轩, 沈金松. 2013. 海洋可控源电磁勘探中空气波压制方法研究. //中国地球物理2013——第六专题论文集. 昆明: 中国地球物理学会.
王振水, 赵宁, 王志刚. 2021. 基于几何多重网格算法的海洋可控源电磁法三轴各向异性介质的三维正演研究. 地球物理学进展, 36(4): 1724-1733, doi: 10.6038/pg2021EE0348.
殷长春, 张文强, 苏扬等. 2022. 基于区域分解法的海洋可控源电磁三维正演模拟研究. 地球物理学进展, 37(1): 469-477, doi: 10.6038/pg2022FF0388.
于彩霞. 2010. 海洋可控源电磁法数据处理研究[硕士论文]. 北京: 中国地质大学(北京).
张鹏飞. 2020. 基于特征学习的海洋可控源电磁法数据降噪研究与应用[博士论文]. 北京: 中国地质大学(北京).
周潞, 祝忠明, 谭帅. 2013. 小波收缩阈值消噪在海洋电磁中的应用. 信息通信, (10): 1-2. https://www.cnki.com.cn/Article/CJFDTOTAL-HBYD201310001.htm
周文强, 周鹏, 戴永寿等. 2019. 海洋可控源电磁勘探中MT噪声降噪效果评估方法研究. 地球物理学进展, 34(3): 1268-1280, doi:10.6038/pg2019CC0156.
-
下载:
图(10)
计量
- 文章访问数:
- PDF下载数:
- 施引文献: 0