Research on microseismic event imaging based on waveform clustering analysis
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摘要: 微地震数据的质量极大地影响震源定位结果的准确性，尤其在地面微地震数据中P波、S波初至不明显或难以识别的低信噪比信号，基于初至拾取的定位方法往往具有较大误差。由于诱发地震的震源机制不同，不同检波器接收到的信号特征也不尽相同，这些因素都会对微地震事件准确定位造成一定的影响。本文提出一种基于聚类分析的微地震数据优选方法，以提高微地震事件振幅叠加定位的成像质量。首先，通过聚类分析计算各数据道之间的欧式距离，通过聚类图去除距离较大和较小的数据道，即去除信号特征不明显或噪声干扰严重数据道; 其次，对各道信号进行互相关计算，提取信号特征相关性强数据道，去除P波特征不明显的数据道; 最后，对筛选数据道进行振幅叠加定位。与原始数据道的定位结果对比表明，微地震聚类分析定位效果更加聚焦，最大震源点位置更加清晰，定位结果得到明显改善。Abstract: The quality of microseismic data greatly affects the accuracy of the source location results, especially in surface microseismic data where the first arrivals of P waves and S waves are not obvious or the low signal-to-noise ratio signals are difficult to identify.Secondly, due to the different focal mechanisms that induce microseismic, the signal characteristics received by different geophones are not the same.All of these factors have a certain impact on the accurate location of microseismic events.This paper proposes a method for selecting microseismic data based on cluster analysis to improve the imaging quality of microseismic event amplitude superposition locating.First, the Euclidean distance between the trace channels is calculated by cluster analysis, and the trace channels with larger and smaller distances are removed through the clustering graph, that is, trace channels with insignificant signal characteristics or severe noise interference are removed.Second, the cross-correlation calculation is performed on the signals of each channel, the trace channels with strong signal characteristic correlation being extracted, and those with unobvious P wave characteristics removed.Finally, the screened trace channels are superimposed and located with amplitude superposition.The comparison of the locating results with the original trace channels shows that the locating effect of the microseismic cluster analysis is more focused, the location of the focal point is clearer, and the locating results are significantly improved.
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Key words:
- microseismic /
- cluster analysis /
- waveform classification /
- data denoising /
- events location
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图 1 4种类型子波与对应加噪后合成波形
Figure 1. Four types of wavelets and the corresponding synthesized waveform with noise
图 4 M井组地面微地震观测阵列与井轨迹地面投影
Figure 4. Geophone array of well M group and ground projection of well trajectory
图 8 事件A聚类后保留数据与删除数据波形
Figure 8. Retained traces and deleted traces after event A clustering
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[1] McClellan J H, Eisner L, Liu E T, et al. Array processing in microseismic monitoring: detection, enhancement, and localization of induced seismicity[J]. IEEE Signal Processing Magazine, 2018, 35(2): 99-111. doi: 10.1109/MSP.2017.2776798 [2] 达姝瑾, 李学贵, 董宏丽, 等. 微地震震源定位方法综述[J]. 吉林大学学报: 地球科学版, 2020, 50(4): 1228-1239.Da Shujin, Li Xuegui, Dong Hongli, et al. Summary of microseismic location methods[J]. Journal of Jilin University: Earth Science Edition, 2020, 50(4): 1228-1239. [3] 常旭, 李政, 王鹏, 等. 基于频率衰减补偿的微地震定位方法[J]. 地球物理学报, 2018, 61(1): 250-257.Chang Xu, Li Zheng, Wang Peng, et al. Micro-seismic location based on frequency attenuation compensation[J]. Chinese Journal of Geophysics, 2018, 61(1): 250-257. [4] 刘劲松, 王赟, 姚振兴. 微地震信号到时自动拾取方法[J]. 地球物理学报, 2013, 56(5): 1660-1666. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWX201305024.htmLiu Jinsong, Wang Yun, Yao Zhenxing. On micro-seismic first arrival identification: a case study[J]. Chinese Journal of Geophysics, 2013, 56(5): 1660-1666. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWX201305024.htm [5] 黄国娇, 巴晶, 钱卫. 层状TI介质中微地震定位和各向异性速度结构同时反演[J]. 地球物理学报, 2020, 63(7): 2846-2857. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWX202007032.htmHuang Guojiao, Ba Jing, Qian Wei. Simultaneous inversion for anisotropic velocity structure and microseismic location in layered TI media[J]. Chinese Journal of Geophysics, 2020, 63(7): 2846-2857. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWX202007032.htm [6] 曾志毅, 张建中. 利用微地震记录互相关成像的震源定位方法[J]. 石油地球物理勘探, 2020, 55(2): 360-372, 388, 231. https://www.cnki.com.cn/Article/CJFDTOTAL-SYDQ202002015.htmZeng Zhiyi, Zhang Jianzhong. Source location through microseismic cross-correlation imaging[J]. Oil Geophysical Prospecting, 2020, 55(2): 360-372, 388, 231. https://www.cnki.com.cn/Article/CJFDTOTAL-SYDQ202002015.htm [7] Geller R J, Mueller C S. Four similar earthquakes in central California[J]. Geophysical Research Letters, 1980, 7(10): 821-824. http://www.onacademic.com/detail/journal_1000035778135710_07c0.html [8] Hadi A S, Kaufman L, Rousseeuw P J. Finding groups in data: an introduction to cluster analysis[J]. Technometrics, 1992, 34(1): 111. http://esyuwapu.ru/nilyjas.pdf [9] 任雪松, 于秀林. 多元统计分析[M]. 北京: 中国统计出版社, 2010. [10] 王璐琛, 常旭, 王一博. 干涉走时微地震震源定位方法[J]. 地球物理学报, 2016, 59(8): 3037-3045. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWX201608026.htmWang Luchen, Chang Xu, Wang Yibo. Locating micro-seismic events based on interferometric traveltime inversion[J]. Chinese Journal of Geophysics, 2016, 59(8): 3037-3045. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWX201608026.htm [11] 郑晶, 曹子原, 姜天琪, 等. 基于深度信念神经网络的微震波到时拾取方法[J]. 矿业科学学报, 2018, 3(6): 521-526. http://kykxxb.cumtb.edu.cn/article/id/180Zheng Jing, Cao Ziyuan, Jiang Tianqi, et al. Deep belief neural network-based arrival picking for microseismic data[J]. Journal of Mining Science and Technology, 2018, 3(6): 521-526. http://kykxxb.cumtb.edu.cn/article/id/180 [12] 孙远, 杨峰, 郑晶, 等. 基于变分模态分解和小波能量熵的微震信号降噪[J]. 矿业科学学报, 2019, 4(6): 469-479. http://kykxxb.cumtb.edu.cn/article/id/248Sun Yuan, Yang Feng, Zheng Jing, et al. Research on microseismic signal denoising based on variational mode decomposition and wavelet energy entropy[J]. Journal of Mining Science and Technology, 2019, 4(6): 469-479. http://kykxxb.cumtb.edu.cn/article/id/248 [13] 李德伟, 杨瑞召, 孟令斌, 等. 煤储层中微地震事件震级影响因素分析[J]. 煤炭学报, 2020, 45(11): 3895-3902. https://www.cnki.com.cn/Article/CJFDTOTAL-MTXB202011024.htmLi Dewei, Yang Ruizhao, Meng Lingbin, et al. Analysis of factors influencing the magnitude of microseismic events in coal reservoirs[J]. Journal of China Coal Society, 2020, 45(11): 3895-3902. https://www.cnki.com.cn/Article/CJFDTOTAL-MTXB202011024.htm [14] 颜志丰, 琚宜文, 唐书恒, 等. 沁水盆地南部煤层气储层压裂过程数值模拟研究[J]. 地球物理学报, 2013, 56(5): 1734-1744.Yan Zhifeng, Jü Yiwen, Tang Shuheng, et al. Numerical simulation study of fracturing process in coalbed methane reservoirs in southern Qinshui basin[J]. Chinese Journal of Geophysics, 2013, 56(5): 1734-1744. [15] 缪思钰, 张海江, 陈余宽, 等. 基于微地震定位和速度成像的页岩气水力压裂地面微地震监测[J]. 石油物探, 2019, 58(2): 262-271, 284. https://www.cnki.com.cn/Article/CJFDTOTAL-SYWT201902013.htmMiao Siyu, Zhang Haijiang, Chen Yükuan, et al. Surface microseismic monitoring of shale gas hydraulic fracturing based on microseismic location and tomography[J]. Geophysical Prospecting for Petroleum, 2019, 58(2): 262-271, 284. https://www.cnki.com.cn/Article/CJFDTOTAL-SYWT201902013.htm [16] 田宵, 张雄, 张华, 等. 全干涉成像的微地震定位方法研究[J]. 地球物理学报, 2020, 63(8): 3105-3115.Tian Xiao, Zhang Xiong, Zhang Hua, et al. Full-interferometry imaging method for microseismic location[J]. Chinese Journal of Geophysics, 2020, 63(8): 3105-3115. [17] 姜天琪, 裴烁瑾. 基于网格搜索-牛顿迭代法的微震震源定位算法[J]. 矿业科学学报, 2019, 4(6): 480-488. http://kykxxb.cumtb.edu.cn/article/id/249Jiang Tianqi, Pei Shuojin. Micro-seismic event location based on Newton iteration method and grid-search method[J]. Journal of Mining Science and Technology, 2019, 4(6): 480-488. http://kykxxb.cumtb.edu.cn/article/id/249 [18] 王维波, 周瑶琪, 春兰. 地面微地震监测SET震源定位特性研究[J]. 中国石油大学学报: 自然科学版, 2012, 36(5): 45-50, 55. https://www.cnki.com.cn/Article/CJFDTOTAL-SYDX201205010.htmWang Weibo, Zhou Yaoqi, Chun Lan. Characteristics of source localization by seismic emission tomgraphy for surface based on microseismic monitoring[J]. Journal of China University of Petroleum: Edition of Natural Science, 2012, 36(5): 45-50, 55. https://www.cnki.com.cn/Article/CJFDTOTAL-SYDX201205010.htm [19] 张永成, 郝海金, 李兵, 等. 煤层气水平井微地震成像裂缝监测应用研究[J]. 煤田地质与勘探, 2018, 46(4): 67-71. https://www.cnki.com.cn/Article/CJFDTOTAL-MDKT201804011.htmZhang Yongcheng, Hao Haijin, Li Bing, et al. Application of microseismic monitoring and imaging of fractures in horizontal CBM well[J]. Coal Geology & Exploration, 2018, 46(4): 67-71. https://www.cnki.com.cn/Article/CJFDTOTAL-MDKT201804011.htm [20] 单仁亮, 宋永威, 白瑶, 等. 基于小波包变换的爆破信号能量衰减特征研究[J]. 矿业科学学报, 2018, 3(2): 119-128. http://kykxxb.cumtb.edu.cn/article/id/129Shan Renliang, Song Yongwei, Bai Yao, et al. Research on the energy attenuation characteristics of blasting vibration signals based on wavelet packet transformation[J]. Journal of Mining Science and Technology, 2018, 3(2): 119-128. http://kykxxb.cumtb.edu.cn/article/id/129 -
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