摘要
岩体微小破裂或变形产生的微震信号能量微弱,且受到大量环境噪声的干扰,难以准确识别有效信号,从而对岩体破裂源的空间定位带来巨大挑战.为了消除叠加在破裂信号中的噪声,提高微震弱信号的信噪比,本文提出了完全自适应噪声集合经验模态分解(Complet EEMD with Adaptive Noise,CEEMDAN)与奇异谱分析(Singular Spectrum Analysis,SSA)相结合的降噪方法.该方法首先使用完全自适应噪声集合经验模态分解获得信号的IMF分量,利用能量熵对信号分量优选以便于去除其中的低频噪声;然后针对重构信号开展奇异谱分析,将信号分解为不同奇异值对应的分量;最终利用奇异值确定重构分量,重构信号实现二次滤波.通过仿真实验,对比分析了微震弱信号的降噪效果,本文方法信号信噪比提高了10.19 dB,均方根误差降低了74.24%,高频段降噪效果优于EMD和EMD-小波阈值方法.利用本方法对重庆涪陵页岩气SF-6井水力压裂产生的微震信号进行处理,有效去除了信号的背景噪声,微震弱信号特征得以保留.该研究对分析岩质边坡破裂诱发的微弱微震事件定位以及滑坡动力灾害监测具有重要意义.
Microseismic signals generated by minor fracturing or deformation in rock masses are often weak and significantly affected by environmental noise,making it challenging to accurately identify effective signals and locate the fracturing source spatially.To eliminate noise superimposed on the fracturing signals and improve the Signal-to-Noise Ratio(SNR)of weak microseismic signals,this paper proposes a denoising method that combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and Singular Spectrum Analysis(SSA).First,CEEMDAN is used to obtain the Intrinsic Mode Functions(IMFs)of the signal,and energy entropy is employed to optimize the signal components,removing low-frequency noise.Then,SSA is applied to the reconstructed signal to decompose it into components corresponding to different singular values.Using singular values,the reconstructed components are determined,and the final reconstructed signal achieves secondary filtering.The study is of significant importance for analyzing the location of weak microseismic events induced by fracturing in rock slopes and monitoring the dynamics of landslide hazards.Based on the theoretical and experimental results,the following conclusions can be drawn:(1)The traditional EMD method shows poor frequency separation effect when decomposing weak signals.Due to the strong coupling between microseismic weak signals and random noise,modal aliasing occurs in the components.(2)The simulation results of noisy sinusoidal function waveforms indicate that the SNR of the simulated waveform before denoising was 11.34 dB.After applying this method,the SNR improved to 21.53 dB,the root mean square error was reduced by 74.24%,and the signal energy was maintained at 98%.This method demonstrates a significant denoising effect.(3)Denoising of microseismic signals generated by hydraulic fracturing in the SF-6 well of the Fuling shale gas field in Chongqing shows that the high-frequency band denoising effect is superior to that of EMD and EMD-wavelet threshold methods.(4)Denoising experiments on three microseismic signals effectively removed the background noise,preserving the characteristics of the microseismic weak signals.
作者
赵清
陈乔
谢庆明
于兴旺
苏鹏程
韦方强
刘岸松
孙耀柏
ZHAO Qing;CHEN Qiao;XIE QingMing;YU XingWang;SU PengCheng;WEI FangQiang;LIU AnSong;SUN YaoBai(College of Architecture and Urban Planning of Chongqing Jiaotong University,Chongqing 400074,China;Chongqing Institute of Green and Intelligent Technology,Chinese Academy of Sciences,Chongqing 400714,China;Chongqing Polytechnic University of Electronic Technology,Chongqing 401331,China;University of Chinese Academy of Sciences,Beijing 100049,China;Institute of Mountain Hazards and Environment,Chinese Academy of Sciences,Chengdu 610041,China;Chongqing River Affairs Center,Chongqing 401147,China)
出处
《地球物理学进展》
北大核心
2025年第5期2064-2075,共12页
Progress in Geophysics
基金
生态环境调查与生态修复三峡水库消落水位治理技术示范工程(5000002021BF40001)
重庆市科学技术局自然科学基金面上项目(CSTB2022NSCQ-MSX1433)
重庆市自然科学基金项目(CSTC2021JCYJ-MSXMX0187)
重庆市教育委员会科学技术研究计划重点项目(KJZD-K202203103)
川渝联合实施重点研发项目(CSTB2022TIAD-CUX0016,2024YFHZ0098)
重庆市技术创新与应用发展专项重点项目(CSTB2022TIAD-KPX0098)
中国科学院西部青年学者理科A类(E2296201)联合资助。
关键词
微震
弱信号
降噪
完全自适应噪声集合经验模态分解
能量熵
奇异谱分析
Microseismic
Weak signal
Noise reduction
Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)
Energy entropy
Singular spectrum analysis
