受小波类时频分析方法的瞬时频率精度的影响，相近的隧道可控震源信号在时频域分析中存在一定的混叠干扰。通过分析时频域中扫频信号与混叠干扰的特征差异，提出了一种基于自适应时变权重的时频域有效信号识别提取方法。该方法利用时变增益系数和连续性判断系数，结合时窗函数，实现了对有效信号的精准识别和提取。结果表明：该方法能够有效区分扫频信号与干扰成分，显著提升了可控震源信号相关提取后的子波分辨率，并增强了对弱反射信号的识别能力；通过三维弹性波数值模拟和地下实验数据验证，证明了该方法的有效性和实用性。可见，该方法不仅克服了传统时频分析方法在瞬时频率计算和干扰抑制上的不足，还进一步提升了弱反射信号的识别精度，提高了地质预报的精度和可靠性。

The issue of aliasing interference in tunnel vibroseis signals is caused by the limited instantaneous frequency accuracy of wavelet-based time-frequency analysis methods. To address this issue, a novel adaptive time-varying weight-based method for effective signal recognition and extraction in the time-frequency domain was proposed. The characteristic differences between sweep signals and aliasing interference in the time-frequency domain were analyzed. Time-varying gain coefficients and continuity judgment coefficients were utilized, combined with a time-window function, to achieve precise recognition and extraction of effective signals. The results show that this method effectively distinguishes sweep signals from interference components. The wavelet extraction resolution of vibroseis signals is significantly improved, and the recognition capability of weak reflection signals is enhanced. The effectiveness and practicality of the method were demonstrated through validation using 3D elastic wave numerical simulations and underground experimental data. It is concluded that this method not only overcomes the limitations of traditional time-frequency analysis methods in instantaneous frequency calculation and interference suppression but also further improves the accuracy of weak reflection signal recognition, thereby enhancing the precision and reliability of geological ahead-prospecting.