基于EMD和形态分形维数的微震波形识别

发布时间：2018-05-01 04:33

本文选题：微震 + EMD　；参考：《中南大学学报(自然科学版)》2017年01期

【摘要】：针对现有矿山微震监测系统信号自动识别难的问题,提出基于经验模态分解(EMD)和形态分形维数的识别方法。首先,采用EMD将原始信号分解为若干个本征模态分量(IMF),选择前5个分量进行重构得到新的信号。其次,求出处理后信号的形态学分形维数,利用微震波形和爆破波形分形维数的差异进行信号识别。对50组微震波形和50组爆破波形进行试验研究。对比未经EMD处理的形态学分形维数以及经EMD处理的盒维数识别结果。研究结果表明:50组微震波形和50组爆破波形在形态学分形维数为1.4时具有较高的识别率;微震波形维数主要在1.4以下,爆破波形维数则基本高于1.4;EMD结合形态学分形维数的识别效果最好,为微震监测波形识别提供了新途径。
[Abstract]:Aiming at the difficulty of automatic signal recognition in the existing mine microseismic monitoring system, an identification method based on empirical mode decomposition (EMD) and fractal dimension of morphology is proposed. Firstly, the original signal is decomposed into several intrinsic mode components by EMD, and the first five components are selected to reconstruct the new signal. Secondly, the fractal dimension of the processed signal is obtained, and the difference of fractal dimension between the microseismic waveform and the blasting waveform is used to recognize the signal. Experimental study on 50 sets of microseismic waveforms and 50 groups of blasting waveforms was carried out. The fractal dimension of morphology without EMD and the recognition result of box dimension treated by EMD were compared. The results show that the recognition rate of microseismic waveforms and blasting waveforms of 50 groups are higher when the fractal dimension of morphology is 1.4, and the dimension of microseismic waveforms is mainly below 1.4. The blasting waveform dimension is higher than 1.4 EMD combined with morphological fractal dimension, which provides a new way for microseismic monitoring waveform recognition.
【作者单位】：中南大学资源与安全工程学院;
【基金】：国家自然科学基金资助项目(51374244)~~
【分类号】：TD76

【相似文献】