Advanced velocity modeling and imaging of rock mass in hydraulic tunnels with high efficiency and accuracy
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摘要: 准确提取隧洞掌子面前方岩体的纵波速度信息并对异常地质体构造进行准确成像是提高地震波法超前地质预报精度的关键。时间域全波形反演（full waveform inversion, FWI）技术可以实现对掌子面前方岩体纵波速度的高精度建模；将FWI结合互相关目标函数，可以解决隧洞中不同炮孔能量不均对反演结果的影响，并提高反演算法的抗噪性；结合隧洞地震数据混合震源编码策略，可以显著提高FWI计算效率；利用FWI提供的准确速度模型，进行基于双向照明补偿互相关成像条件的逆时偏移（reverse time migration, RTM），可以实现对异常地质构造的精准成像。通过多种隧洞常见地质体模型的数值试验验证了高效高精度隧洞岩体超前速度建模及成像方法的有效性，并为实际隧洞工程应用提供了理论支撑。Abstract: The accurate calculation of the P-wave velocity information of the rock mass in front of the tunnel face and the accurate imaging of the adverse geological structures are the key to improve the accuracy of the advanced geological prediction of tunnels with seismic waves. The full-waveform inversion (FWI) in time domain can realize high-precision modeling for longitudinal wave velocity of rock mass in front of the hydraulic tunnel. Considering the cross correlation misfit function, the influences of energy unevenness among different boreholes on the inversion results can be solved, and the noise resistance of inversion is improved. A multi-source coding strategy for seismic data of hydraulic tunnels is proposed to improve the efficiency of FWI calculation. The reverse time migration (RTM) based on the bidirectional illumination compensated cross-correlation imaging conditions is performed to accurately image the adverse geological structures based on the velocity model provided by FWI. The numerical tests of a variety of geological models for tunnels verify the effectiveness of the advanced velocity modeling and imaging of rock mass in hydraulic tunnels with high efficiency and accuracy, and it may provide a theoretical basis for practical tunnel engineering.
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图 1 4种隧洞常见地质体构造模型
Figure 1. Four kinds of frequent geological models in tunnel engineering
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图 4 震源能量不均时两种目标函数FWI结果对比
Figure 4. FWI results of global-correlation misfit function and least squares misfit function under different energies of seismic source
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图 5 观测数据含噪时两种目标函数FWI结果对比
Figure 5. FWI results of global-correlation misfit function and least squares misfit function under observed data contaminated by strong random noise
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表 1 单炮数据与混合震源数据FWI对比
Table 1 Comparison of FWI between single-source and multi- source data
类型 震源/
个单次正演耗时/s 迭代次数/次 反演
总耗时/s误差系数/% 单炮
数据20 5.6 160 36800 1.38 混合震源数据 1 5.6 160 1840 1.46 
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