小波熵去噪在微差爆破延时精确识别中的应用

在小波熵去噪理论的基础上,对实际隧道爆破工程采集到的爆破振动信号进行了小波熵方法去噪。利用db8小波对去噪后的信号在尺度a=16进行了连续小波变换得到其模极大值,准确识别了隧道多段别微差爆破实际延期时间间隔,并验证了小波熵方法去噪的可靠性。结果表明,小波熵去噪方法能够有效滤除和抑制爆破振动非线性信号所夹杂的高频噪声分量,并且很好地保留了爆破振动信号的突变细节。对滤波后的信号进行模极大值变换,信号局部奇异点的辨识更准确,可以精确识别隧道微差爆破延时间隔。

Application of Wavelet Entropy De -noising Method in Precise Identification of Millisecond Blasting Delay Time

In this paper ,based on the theory of wavelet entropy de -noising ,the wavelet entropy method is established for the blasting vibration signals collected in the actual tunnel blasting engineering .The db8 wavelet is used to carry out the con-tinuous wavelet transform to get the modulus maximum of the signal after de -noising ,accurately identify the millisecond de-lay time interval in tunnel blasting ,the reliability of wavelet entropy de-noising method is verified .The results show ,the wavelet entropy de-noising method can effectively filter and suppress the high frequency noise component of the nonlinear signal of blasting vibration ,and the mutation details of blasting vibration signal can be preserved well .After the filtering the signal is carried out modular maximum transform ,the signal local singular point identification is more accurate ,which can precisely identify the delay interval in the tunnel millisecond blasting .