Communication and Prevention Geological Technology Department, Huaihe Energy Group Coal Industry Branch
School of Earth and Environment, Anhui University of Science and Technology
Xieqiao Coal Mine, Huaihe Energy Group Coal Industry Branch
Fuzhou Huahong Intelligent Technology Co., Ltd.

随掘地震超前探测技术可实现探掘平行，为巷道快速智能掘进场景下实时、精准地质保障提供了可能。随掘震源产生的是复杂、变频、连续信号，信号特征认知直接影响数据处理与成像精度，而目前针对岩巷全断面掘进机（TBM）随掘地震信号特征的认知仍不清晰，且暂时还没有针对性开展过信号处理与成像研究工作。针对上述问题，以谢桥煤矿瓦斯治理巷TBM随掘地震超前探测试验为例，分析了刀盘先导信号与岩壁接收信号的时间域、频率域及时频域特征：岩巷TBM随掘地震信号中不同振幅能量成分比例呈现金字塔形，但分布随机，不对称程度较高；机械运转信号能量较大，刀盘先导信号强度是岩壁接收信号的200倍左右；频率域变频特征明显；机械运转信号基础频率较低，刀盘先导信号频率成分主要集中在10～80 Hz与150～200 Hz，主频为36.99 Hz，岩壁接收信号频率成分主要集中在50～200 Hz，主频为137.97 Hz；刀盘先导信号较岩壁接收信号时频域能量团分布更为规则，多次震源激发现象明显，能量团之间的差异性特征表明了多次震源激发时振幅能量与持续时间的随机性。利用脉冲化算法与绕射叠加偏移成像方法对岩巷TBM随掘地震信号进行数据处理与成像试验，结果表明：① 脉冲化等效单炮记录与利用常规震源得到的超前探测单炮记录特征一致性较强，同相轴清晰且连续性较好，可满足现场探测分析需要。② 对探测范围内岩体情况的超前预报结果与实际揭露情况一致，说明岩巷TBM随掘地震超前探测可提供有效地质保障。

The advanced seismic-while-excavating detection technology can achieve parallel exploration and excavation, providing the possibility of real-time and accurate geological support in the scenario of rapid and intelligent excavation of roadways. The signals generated by the excavation seismic source are complex, variable frequency, and continuous. The recognition of signal features directly affects the accuracy of data processing and imaging. However, currently, the recognition of seismic-while-excavating signal features for rock tunnel boring machine (TBM) is still unclear, and there is currently no targeted research on signal processing and imaging. In order to solve the above problems, taking the TBM advanced seismic-while-excavating detection test of the gas control roadway in Xieqiao Coal Mine as an example, the time domain, frequency domain, and frequency domain features of the cutterhead pilot signal and the rock wall received signal are analyzed. The proportion of different amplitude energy components in the rock roadway TBM seismic-while-excavating signal show a pyramid shape. But the distribution is random and the degree of asymmetry is high. The energy of the mechanical operation signal is relatively high, and the strength of the cutterhead pilot signal is about 200 times that of the signal received by the rock wall. The frequency domain frequency conversion features are obvious. The basic frequency of the mechanical operation signal is relatively low, and the frequency components of the cutterhead pilot signal are mainly concentrated in the range of 10-80 Hz and 150-200 Hz, with a main frequency of 36.99 Hz. The frequency components of the rock wall received signal are mainly concentrated in the range of 50-200 Hz, with a main frequency of 137.97 Hz. The frequency domain energy distribution of the cutterhead pilot signal is more regular than that of the rock wall received signal, and the phenomenon of multiple source excitation is obvious. The difference features between energy clusters indicate the randomness of amplitude energy and duration during multiple source excitations. The data processing and imaging experiments of TBM seismic-while-excavating signals in rock roadways are carried out using the pulse algorithm and diffraction stacking migration imaging method. The results show the following points. ① The pulse equivalent single shot record has strong consistency with the advanced detection single shot record obtained from conventional seismic-while-excavating sources, with clear and continuous in-phase axes, which can meet the needs of on-site detection analysis.② The advanced prediction results of the rock mass situation within the detection range are consistent with the actual exposure, indicating that TBM advanced seismic-while-excavating detection in rock roadways can provide effective geological support.

安徽省高校自然科学研究项目（2023AH051186） ；安徽理工大学引进人才基金项目（2023yjrc21）。

党保全，郭立全，张延喜，等. 煤矿岩巷TBM掘进随掘地震信号特征及其应用[J]. 工矿自动化，2024，50（6）：46-53, 60.

DANG Baoquan, GUO Liquan, ZHANG Yanxi, et al. Features and application of seismic-while-excavating signals during TBM excavation in coal mine rock roadways[J]. Journal of Mine Automation，2024，50（6）：46-53, 60.