摘要为准确解译微震事件，防止因背景噪声滤波导致的时间定位偏移，在保留背景噪声条件下提高微震信号时间定位拾取精度。通过计算时窗信号能量快速定位信号起止时刻，以及分析信号时频特征捕捉瞬时频率突变点，实现信号时间定位精确拾取，提出基于时窗信号能量（time window energy，TWE）和时频特征（time frequency characteristics，TFC）联合（TWE-TFC）的时间定位拾取方法。采用模拟信号、模型试验信号和现场监测信号验证方法性能，并与长短时窗（STA/LTA）法、赤池信息准则（AIC）法及2个独立方法TWE法和TFC法进行对比。研究结果表明：（1）矿震信号到达和离开检波器前后，在信号能量和瞬时频率2个参数上表现出明显的突变特征，是实现时间定位拾取的关键；（2）在单一时域或频域内，不能完全展现矿震信号起始和终止时刻的特征，需联合时频域特征参数提升时间定位精度；（3）TWE-TFC法较STA/LTA和AIC法对信号起始点的拾取精度提高了67.76%以上，并补充了对信号终止时刻的拾取能力；（4）TWE-TFC法较单独的TWE法和TFC法，时间定位拾取平均误差减少了68.11%以上。
Abstract：To accurately interpret microseismic events and alleviate time localization deviations caused by background noise filtering, this study improves the time localization picking accuracy of microseismic signals is enhanced while preserving the background noise. The time window energy is calculated to quickly locate the arrival and end times of the signal, and the time-frequency characteristics of the signal are analyzed to identify instantaneous frequency mutation points, thus achieving precise time localization of the signal. We propose a time localization picking method (TWE-TFC) based on the joint integration of time window energy (TWE) and time-frequency characteristics (TFC). The performance of this method is validated using simulated signals, model test signals, and field monitoring signals, and it is compared with the STA/LTA method, the AIC method, and the two independent methods—the TWE and TFC methods. The research findings indicate that: (1) Distinct mutation characteristics in signal energy and instantaneous frequency are exhibited when mine seismic signals arrive at and depart from the geophone, which are crucial for achieving accurate time localization picking; (2) Characteristics of the arrival and end times of the mine seismic signals cannot be fully represented in a single time or frequency domain, which requires integrating time-frequency domain characteristic parameters to improve time localization accuracy; (3) Compared to the STA/LTA method and the AIC method, the proposed TWE-TFC method improves the picking accuracy of the signal arrival time by more than 67.76% and improves the capability to pick signal end time; (4) In comparison to the standalone TWE and TFC methods, the average time localization picking error of the TWE-TFC method is reduced by more than 68.11%.
周琳力1，2，3*，韦米香1，韩 军2，贾宝新1，4，卜 继1，崔博源1. 基于时窗信号能量和时频特征的微震信号时间定位拾取方法[J]. 岩石力学与工程学报, 2026, 45(3): 875-891.
ZHOU Linli1, 2, 3*, WEI Mixiang1, HAN Jun2, JIA Baoxin1, 4, BU Ji1, CUI Boyuan1. Time localization picking method for microseismic signals based on time window energy and time frequency characteristics. , 2026, 45(3): 875-891.
[1] 李 楠，王恩元，GE Maochen. 微震监测技术及其在煤矿的应用现状与展望[J]. 煤炭学报，2017，42(增1)：83–96.(LI Nan，WANG Enyuan，GE Maochen. Microseismic monitoring technique and its applications at coal mines：present status and future prospects[J]. Journal of China Coal Society，2017，42(Supp.1)：83–96.(in Chinese))
[2] LIU J，SI Y，WEI D，et al. Developments and prospects of microseismic monitoring technology in underground metal mines in China[J]. Journal of Central South University，2021，28(10)：30743098.
[3] 柳云龙，田 有，冯 晅，等. 微震技术与应用研究综述[J].地球物理学进展，2013，28(4)：1 801–1 808.(LIU Yunlong，TIAN You，FENG Xuan，et al. Review of microseism technology and its application[J]. Progress in Geophysics，2013，28(4)：1 801–1 808.(in Chinese))
[4] 王 强，肖亚勋，姚志宾，等. 特长深埋隧道无线微震监测技术[J].实验室研究与探索，2021，40(2)：80–84.(WANG Qiang，XIAO Yaxun，YAO Zhibin，et al. Research on wireless microseismic monitoring technology of super long and deep buried tunnel[J]. Research and Exploration in Laboratory，2021，40(2)：80–84.(in Chinese))
[5] LI H，WU J，WANG W. Automatic picking method of p-wave initial time of microseismic in coal mine[J]. Engineering Letters，2025，33(2)：312–319.
[6] 卢新明，阚淑婷. 煤矿动力灾害本源预警方法关键技术与展望[J].煤炭学报，2020，45(增1)：128–139.(LU Xinming，KAN Shuting. Key technology and prospect of the original source early warning method for coal mine dynamic disaster[J]. Journal of China Coal Society，2020，45(Supp.1)：128–139.(in Chinese))
[7] ZHANG C，JIN G，LIU C，et al. Prediction of rockbursts in a typical island working face of a coal mine through microseismic monitoring technology[J]. Tunnelling and Underground Space Technology incorporating Trenchless Technology Research，2021，113：103972.
[8] JIA H，YAN B，YANG Z，et al. Identification of goaf instability under blasting disturbance using microseismic monitoring technology[J]. Geomechanics and Geophysics for Geo-Energy and Geo-Resources，2023，9(1)：142.
[9] 樊 鑫，程建远，栗 升，等. 煤矿微震监测系统在回采工作面顶板水害防治中的应用[J]. 煤田地质与勘探，2024，52(6)：115–127. (FAN Xin，CHENG Jianyuan，LI Sheng，et al. Application of microseismic monitoring system for coal mines to the prevention and control of water disasters on working face roofs[J]. Coal Geology and Exploration，2024，52(6)：115–127.(in Chinese))
[10] 李天斌，许韦豪，马春驰，等. 基于深度学习的隧道微震监测及岩爆预警技术与系统研究[J]. 岩石力学与工程学报，2024，43(5)：1 041–1 063.(LI Tianbin，XU Weihao，MA Chunchi，et al. Research of technology and system of tunnel microseismic monitoring and rockburst early warning based on deep learning[J]. Chinese Journal of Rock Mechanics and Engineering，2024，43(5)：1 041–1 063.(in Chinese))
[11] 许延春，黄 磊. 基于微震监测的工作面底板突水全时空预警方法[J]. 煤炭科学技术，2023，51(1)：369–382.(XU Yanchun，HUANG Lei. Full-time and space early-warning method for floor water inrush in working face based on microseismic monitoring[J]. Coal Science and Technology，2023，51(1)：369–382.(in Chinese))
[12] 陈 畅，王 赟，郭高源，等. 几种旋转地震仪在深部地下巷道的观测对比[J]. 地球物理学报，2022，65(12)：4 569–4 582.(CHEN Chang，WANG Yun，GUO Gaoyuan，et al. Deep underground observation comparison of rotational seismometers[J]. Chinese Journal of Geophysics，2022，65(12)：4 569–4 582.(in Chinese))
[13] 曹安业，杨 旭，王常彬，等. 基于深度迁移学习的矿山微震到时精确拾取与自动定位策略[J]. 煤炭学报，2023，48(12)：4 393–4 405. (CAO Anye，YANG Xu，WANG Changbin，et al. High-precision phase picking and automatic source locating method for seismicity in mines based on deep transfer learning[J]. Journal of China Coal Society，2023，48(12)：4 393–4 405.(in Chinese))
[14] ZHAN K，WANG X，WEN X，et al. Bayesian microseismic localization method based on the maximum entropy hamiltonian monte carlo markov chain approach[J]. Rock Mechanics and Rock Engineering，2025，58(3)：3 353–3 369.
[15] 姚彦吉，柳林涛，盛敏汉，等. 利用标准时频变换方法在强噪声环境下无偏拾取地震P波、S波到时[J]. 地球物理学报，2022，65(1)：227–243.(YAO Yanji，LIU Lintao，SHENG Minhan，et al. Unbiased picking onset time of P and S phases by Normal Time-Frequency Transform method under a strong noise environment[J]. Chinese Journal of Geophysics，2022，65(1)：227–243.(in Chinese))
[16] 易思梦，唐东林，赵云亮，等. 基于人工智能的地震初至拾取方法研究进展[J]. 石油地球物理勘探，2024，59(4)：899–914.(YI Simeng，TANG Donglin，ZHAO Yunliang，et al. A review of artificial intelligence-based seismic first break picking methods[J]. Oil Geophysical Prospecting，2024，59(4)：899–914.(in Chinese))
[17] 谢学斌，叶永飞，高 山，等. 一种基于波形包络趋势变化的AE信号到时拾取方法[J]. 矿冶工程，2021，41(3)：6–10.(XIE Xuebin，YE Yongfei，GAO Shan，et al. An arrival-time pickup method for AE signal based on trend change in waveform envelope[J]. Mining and Metallurgical Engineering，2021，41(3)：6–10.(in Chinese))
[18] 唐 佩，李青锋，朱川曲，等. 基于小波变换的微震阈值去噪方法[J]. 湖南科技大学学报：自然科学版，2021，36(4)：1–7.(TANG Pei，LI Qingfeng，ZHU Chuanqu，et al. Microseism threshold denoising method based on wavelet transform[J]. Journal of Hunan University of Science and Technology：Natural Science，2021，36(4)：1–7.(in Chinese))
[19] 赵扬锋，王进铭，潘一山，等. 基于质量寻优与归一化STA/LTA方法的微震P波到时拾取技术研究[J]. 岩石力学与工程学报，2022，41(8)：1 610–1 625.(ZHAO Yangfeng，WANG Jinming，PAN Yishan，et al. Study on determining arrival times of microseismic P-wave based on quality optimization and normalized STA/LTA method[J]. Chinese Journal of Rock Mechanics and Engineering，2022，41(8)：1 610–1 625.(in Chinese))
[20] 贾宝新，李 峰，周琳力，等. 基于时频分析的微震P波和S波到时联合拾取方法[J]. 岩土力学，2021，42(5)：1 253–1 265.(JIA Baoxin，LI Feng，ZHOU Linli，et al. Joint arrival-time picking method of microseismic P-wave and S-wave based on time-frequency analysis[J]. Rock and Soil Mechanics，2021，42(5)：1 253–1 265.(in Chinese))
[21] SHANG X，LI X，MORALES-ESTEBAN A，et al. An improved P-phase arrival picking method S/L-K-A with an application to the Yongshaba mine in China[J]. Pure and Applied Geophysics，2018，175(6)：2 121–2 139.
[22] 孟 娟，吴燕雄，李亚南. 基于改进长短时窗比值及优化变分模态分解的微震初至拾取算法[J]. 地震学报，2022，44(3)：388–400. (MENG Juan，WU Yanxiong，LI Yanan. First arrival time picking algorithm of micro-seismic based on improved STA/LTA and adaptive VMD[J]. Acta Seismologica Sinica，2022，44(3)：388–400.(in Chinese))
[23] ZHOU Z，CHENG R，CHEN L，et al. An improved joint method for onset picking of acoustic emission signals with noise[J]. Journal of Central South University，2019，26(10)：2 878–2 890.
[24] CHENG A，XU E，YAO P，et al. An automatic arrival time picking algorithm of P-wave based on adaptive characteristic function[J]. Computers and Geosciences，2024，185：105544.
[25] 刘晓佳，李 剑，刘代劲，等. 基于时窗熵的冲击波到时提取方法研究[J]. 计算机测量与控制，2023，31(3)：281–286.(LIU Xiaojia，LI Jian，LIU Daijin，et al. Research on shock wave arrival time extraction method based on time window entropy[J]. Computer Measurement and Control，2023，31(3)：281–286.(in Chinese))
[26] 肖晓春，丁 振，丁 鑫，等. 基于P波到时拾取的分析法与遗传算法联合定位方法与应用[J]. 岩土力学，2024，45(7)：2 195–2 207. (XIAO Xiaochun，DING Zhen，DING Xin，et al. Joint positioning method of P-wave arrival time picking based analysis-genetic algorithm and its application[J]. Rock and Soil Mechanics，2024，45(7)：2 195–2 207.(in Chinese))
[27] 姜福兴，尹永明，朱权洁，等. 单事件多通道微震波形的特征提取与联合识别研究[J]. 煤炭学报，2014，39(2)：229–237.(JIANG Fuxing，YIN Yongming，ZHU Quanjie，et al. Feature extraction and classification of mining microseismic waveforms via multi-channels analysis[J]. Journal of China Coal Society，2014，39(2)：229–237. (in Chinese))
[28] 张艳博，周 浩，梁 鹏，等. 基于到时精确拾取与智能优化算法结合的岩石声发射定位方法研究[J]. 岩土力学，2025，46(5)：1 643–1 656.(ZHANG Yanbo，ZHOU Hao，LIANG Peng，et al. Acoustic emission location method of rock based on time precise picking and intelligent optimization algorithm[J]. Rock and Soil Mechanics，2025，46(5)：1 643–1 656. (in Chinese))
[29] 王婷婷，王威华，陈建玲，等. 基于改进VMD的岩石破裂声发射信号滤波算法[J]. 噪声与振动控制，2023，43(2)：104–111.(WANG Tingting，WANG Weihua，CHEN Jianling，et al. The filtering algorithm for rock fracture signal based on improved VMD[J]. Noise and Vibration Control，2023，43(2)：104–111.(in Chinese))
[30] 彭平安，王李管，裴安磊. 微震信号无参数自动去噪PD算法实现及应用[J]. 岩石力学与工程学报，2019，38(增1)：3 061–3 069. (PENG Ping?an，WANG Liguan，PEI Anlei. Non-parametric automatic microseismic data denoising via PD method and its application[J]. Chinese Journal of Rock Mechanics and Engineering，2019，38(Supp.1)：3 061–3 069.(in Chinese))
[31] 赵震华，张杏莉，卢新明. 微震信号初至拾取的AIC算法及其分析[J]. 山东科技大学学报：自然科学版，2022，41(1)：44–53.(ZHAO Zhenhua，ZHANG Xingli，LU Xinming. AIC algorithm and its analysis for initial picking of micro-seismic signals[J]. Journal of Shandong University of Science and Technology：Natural Science，2022，41(1)：44–53.(in Chinese))
[32] 刘 晗，张建中. 微震信号自动检测的STA/LTA算法及其改进分析[J]. 地球物理学进展，2014，29(4)：1 708–1 714.(LIU Han，ZHANG Jianzhong. STA/LTA algorithm analysis and improvement of microseismic signal automatic detection[J]. Progress in Geophysics，2014，29(4)：1 708–1 714.(in Chinese))
[33] 严蔚敏，吴伟民. 数据结构(C语言版)[M]. 北京：清华大学出版社，2007：13–15.(YAN Weimin，WU Weimin. Data structure(C language edition)[M]. Beijing：Tsinghua University Press，2007：13–15.(in Chinese))
[34] 邱 磊，李彩华. STA/LTA方法拾取天然地震波初至及其改进分析[J]. 地球物理学进展，2023，38(4)：1 497–1 506.(QIU Lei，LI Caihua. STA/LTA method for picking up the first arrival of natura seismic waves and its improvement analysis[J]. Progress in Geophysics，2023，38(4)：1 497–1 506.(in Chinese))
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