压裂微震数据初至拾取的时频谱熵法

doi:10.13278/j.cnki.jjuese.20190093

摘要/Abstract

摘要： 在油气田开发过程中，微震监测是获得水力压裂引起裂缝分布的一种较为有效的方法。微震的定位成像与裂缝解释需要利用有效微震信号位置，而微震信号具有低信噪比的特点，传统信号拾取方法无法有效实现较低信噪比条件下初至时刻的准确拾取。本文提出一种基于时频谱熵的初至拾取新方法，该方法首先通过S变换获取含噪信号的时频谱；然后对谱内各个采样点沿频率方向进行分帧操作，并计算每帧频段内的近似负熵值，以最小近似负熵值作为该谱点的负熵值；最后沿时间方向比较各谱点的负熵值，最小值对应的时刻即为初至时刻。本文利用不同信噪比的合成地震数据对该方法进行效果验证，并与长短时窗能量比（STA/LTA）法进行拾取结果对比，结果表明：信噪比在-5 dB时，两种方法拾取效果都很好；信噪比在-10 dB时，时频谱熵法拾取效果更好。时频谱熵法更适合低信噪比情况下的信号初至拾取。

关键词: 微震信号, 时频谱, 频域分帧, 负熵, 初至拾取

Abstract: In the process of oil and gas field development, microseismic monitoring is an effective method to obtain the fracture distribution of hydraulic fracturing. Microseismic location imaging and crack interpretation need to use the location of effective microseismic signals; however,microseismic signals have the characteristics of low signal-to-noise ratio, and the traditional signal picking methods cannot effectively achieve the accurate picking at the first arrival time under the condition of low signal-to-noise ratio. In this paper, a new method based on time-spectral entropy for picking up the initial arrival point is proposed. This method first obtains the time-frequency spectrum of the signal containing noise through S transformation;then divides each sampling point in the spectrum into frames along the frequency direction, and calculates the approximate negative entropy value in each frame frequency band, with the smallest approximate negative entropy value as the negative entropy value of the point;finally, the approximate negative entropy values of all sampling points are compared in the time direction, and the time corresponding to the minimum value is the initial arrival position. In this study, a group of synthetic seismic data are used to verify the effect of the new method, and the results are compared with those obtained by the STA/LTA method. It is concluded that the two methods are both effective when the signal-to-noise ratio is -5 dB, but the time-spectral entropy method is better when the signal-to-noise ratio is -10 dB. Thus, the time-spectral entropy method is more suitable for the first arrival signal picking under low signal-to-noise ratio.

Key words: microseismic signals, time-frequency spectrum, frequency domain frame, negative entropy, arrival-time picking

中图分类号:

P631.4

田雅男, 王环宇, 王鑫, 黄佳俊, 章强. 压裂微震数据初至拾取的时频谱熵法[J]. 吉林大学学报(地球科学版), 2020, 50(4): 1219-1227.

Tian Yanan, Wang Huanyu, Wang Xin, Huang Jiajun, Zhang Qiang. Time-Spectral Entropy Method for Picking Up Fracturing Microseismic Data[J]. Journal of Jilin University(Earth Science Edition), 2020, 50(4): 1219-1227.

参考文献

[1] 焦健, 南雄, 陈德山. 煤矿井下微震监测技术现状与发展[J]. 山东工业技术, 2018(20):85. Jiao Jian, Nan Xiong, Chen Deshan. Status and Development of Microseismic Monitoring Technology in Coal Mine[J]. Shandong Industrial Technology, 2018(20):85.
[2] 段建华. 微地震事件不同初至拾取方法的对比分析[J].煤田地质与勘探,2013,41(3):82-86. Duan Jianhua. Comparative Analysis of Different Picking Methods of Microseismic Events from the Beginning to the End[J]. Coal Geology & Exploration, 2013,41(3):82-86.
[3] Zhang Z Y, Weller A, Kruschwitz S. Pore Radius Distribution and Fractal Dimension Derived from Spectral Induced Polarization[J]. Near Surface Geophysics, 2017, 15(6):625-632.
[4] 程浩,袁月,王恩德,等.基于小波变换的自适应阈值微震信号去噪研究[J].东北大学学报(自然科学版),2018,39(9):1332-1336. Cheng Hao, Yuan Yue, Wang Ende, et al. Study on Adaptive Threshold Microseismic Signal Denoising Based on Wavelet Transform[J]. Journal of Northeastern University (Natural Science Edtion), 2018, 39(9):1332-1336.
[5] 张雪冰,刘财,刘洋,等.基于局部均值分解的地震信号时频分解方法[J].吉林大学学报(地球科学版), 2017, 47(5):1562-1571. Zhang Xuebing, Liu Cai, Liu Yang, et al. Time-Frequency Decomposition Method of Seismic Signals Based on Local Mean Value Decomposition[J]. Journal of Jilin University (Earth Science Edition),2017,47(5):1562-1571.
[6] Li Y, Ni Z, Tian Y. Arrival-Time Picking Method Based on Approximate Negentropy for Microseismic Data[J]. Journal of Applied Geophysics, 2018,152:100-109.
[7] 杨千里. 广义S变换功率谱在地震监测中的应用研究[C]//国家安全地球物理丛书:十四:资源环境与地球物理.北京:中国地球物理学会,2018:301-306. Yang Qianli. Application of Generalized S Transform Power Spectrum in Seismic Monitoring[C]//National Security Geophysics Series:ⅩⅣ:Resources, Environment and Geophysics. Beijing:Chinese Geophysical Society, 2018:301-306.
[8] 马力,何健,李超胜,等.基于广义S变换的地震资料处理[J].科学技术与工程,2017,17(25):171-175. Ma Li, He Jian,Li Chaosheng, et al. Seismic Data Processing Based on Generalized S Transform[J]. Science, Technology and Engineering, 2017, 17(25):171-175.
[9] 陈学华,贺振华,文晓涛,等.基于广义S变换的裂缝分频边缘检测方法[J].吉林大学学报(地球科学版),2011,41(5):1605-1609. Chen Xuehua, He Zhenhua, Wen Xiaotao,et al. Frequency Division Edge Detection Method Based on Generalized S Transform[J]. Journal of Jilin University (Earth Science Edition), 2011, 41(5):1605-1609.
[10] 邓攻,梁锋,李晓婷,等.S变换谱分解技术在深反射地震弱信号提取中的应用[J].地球物理学报,2015,58(12):4594-4604. Deng Gong, Liang Feng, Li Xiaoting, et al. Application of S Transform Spectrum Decomposition Technique in Weak Signal Extraction from Deep Reflection Seismic[J]. Chinese Journal of Geophysics,2015,58(12):4594-4604.
[11] 陈莹莹,简磊.基于最大熵谱估计和时频特性的语音端点检测[J].计算机应用与软件,2017,34(11):91-96. Chen Yingying, Jian Lei. Speech Endpoint Detection Based on Maximum Entropy Spectrum Estimation and Time-Frequency Characteristics[J]. Computer Applications and Software,2017,34(11):91-96.
[12] Saulig N, Milanovic Ž, Ioana C. A Local Entropy-Based Algorithm for Information Content Extraction from Time-Frequency Distributions of Noisy Signals[J]. Digital Signal Processing, 2017(70):155-165.
[13] 倪卓. 基于近似负熵算法实现微地震数据初至拾取研究[D]. 长春:吉林大学, 2018. Ni Zhuo. Research on Initial Arrival Pickup of Microseismic Data Based on Approximate Negative Entropy Algorithm[D]. Changchun:Jilin University, 2018.
[14] 徐耀华, 郭英, 王刚.基于负熵的语音端点检测算法[J].信号处理, 2009, 25(2):307-312. Xu Yaohua, Guo Ying, Wang Gang. Speech Endpoint Detection Algorithm Based on Negative Entropy[J]. Signal Processing, 2009, 25(2):307-312.

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