利用AR模型参数和BP神经网络辨识微震信号

Use of AR Model Parameters and BP Neural Network Identification Microseismic Signal

利用AR模型参数和BP神经网络,针对矿山微震信号具有频带较宽、谱成分丰富的特性,提出了对不同频率范围的信号和噪声进行滤波处理的方法。利用该方法可将噪声与信号分离以及将不同频段信号分解,从而达到滤波的目的。实验结果表明,利用AR模型参数和BP神经网络能够有效去除微震异常信号的噪声,可应用于微震信号的预处理和微震预测。

According to the characteristics of broad frequency and abundant spectral components of mine microseismic signal, we use AR model parameters and BP neural network to propose a method of filtering treatment for the signal and noise with different frequency ranges. We can use this method to separate noise and signal, and decompose different frequency band signals, so we can achieve the goal of filtering. The experimental results suggest that we can effectively remove the noise of microseismic abnormal signal by using AR model parameters and BP neural network,and this method can be used in the microseismic prediction and the pretreatment of microseismic signal.