隧道爆破振动信号时频谱增强优化分析

针对隧道爆破振动信号时频解析度不足的难题，运用基于卷积神经网络的时频图像增强算法，增强实测隧道爆破信号时频图像，捕获到爆破信号能量在时频域上的聚集范围，从而重构得到反映爆破特征的真实信号；根据真实信号对爆破网络中雷管的起爆时刻进行了精确判别，识别隧道爆破雷管灾害源特征。分析表明：基于卷积神经网络的时频图像增强算法可有效抑制信号中的交叉项，最大限度地保留信号自有项，提高爆破信号能量聚集性和时频解析度；不同批次雷管混用是隧道安全的主要致灾因素，应加强监管以实现隧道安全高效施工。

Optimization analysis of time frequency spectrum enhancement of tunnel blasting vibration signal

Aiming at the problem of insufficient time-frequency resolution of tunnel blasting vibration signal, a time-frequency image enhancement algorithm based on convolutional neural network is applied, through the time-frequency image enhancement of the measured tunnel blasting signal, the aggregation range of the blasting signal energy in the time-frequency domain is captured, and the real signal reflecting the blasting characteristics is reconstructed; according to the real signal, the initiation time of detonator in blasting network is accurately distinguished, and the characteristics of tunnel blasting detonator disaster source are identified.The analysis shows that the time-frequency image enhancement algorithm based on convolutional neural network can effectively suppress the cross-terms in the signal, retain the auto-terms of the signal to the greatest extent, and improve the energy aggregation and time-frequency resolution of the blasting signal; The mixed use of different batches of detonators is the main disaster causing factor of tunnel safety.Supervision should be strengthened to realize safe and efficient tunnel construction.