检索规则说明:AND代表“并且”;OR代表“或者”;NOT代表“不包含”;(注意必须大写,运算符两边需空一格)
检 索 范 例 :范例一: (K=图书馆学 OR K=情报学) AND A=范并思 范例二:J=计算机应用与软件 AND (U=C++ OR U=Basic) NOT M=Visual
作 者:朱权洁[1] 姜福兴[1] 于正兴[1] 尹永明[1] 吕垒[2]
机构地区:[1]北京科技大学土木与环境工程学院,北京100083 [2]中钢集团武汉安全环保研究院,湖北武汉430081
出 处:《岩石力学与工程学报》2012年第4期723-730,共8页Chinese Journal of Rock Mechanics and Engineering
基 金:国家重点基础研究发展计划(973)项目(2010CB226803);国家自然科学基金资助项目(51174016)
摘 要:以矿山现场微震数据为基础,选取矿山爆破震动信号与岩石破裂微震信号对比研究。首先,运用Matlab的小波包分析模块对微震信号进行5层多尺度分解,分别求取各节点处重构信号的小波包频带能量,对爆破震动信号与岩石破裂信号的频带能量分布特征进行研究;其次,通过建立新的频带空间,对比二者的能量分布特征。结果表明,矿山现场微震信号的频带能量分布特征为:岩体破裂信号的能量多集中于S5,0~S5,7低频频带(0~125 Hz),爆破震动信号的能量则在S5,24~S5,31频带(375~500 Hz)表现得较为集中。该分析方法为矿山识别爆破震动事件与岩石破裂事件提供了一种思路,利用二者能量分布差异大、特征对比明显的特点,通过对比新频带空间内的能量分布特征,可以实现对两类微震波形的初步辨识。Based on in-situ microseismic data, the features of blasting vibration and rock fracture microseismic signals were studied. Firstly, using the wavelet packet analysis method, the microseismic signals were decomposed into 5 multi-scale, 32 frequency bands to calculate the signals energy under different bands. Secondly, via building 4 new frequency bands to compare the rock fracture signals and blasting vibration signals, the energy distribution was different, and the contrasting characteristics was more obvious. The results indicate that the frequency band energy distribution character of microseismic signals in mine field are as follows: the energy of rock fracture signals was concentrated in S5,0- S5,7 bands(0- 125 Hz); blasting vibration signal was more obviously concentrated in S5,24 - S5, 31 bands(375 - 500 Hz). This analysis procedure provides a new method for recognizing blasting vibration and rock fracture signals, and the calculated distribution feature of signals energy can be used to recognize microseismic waveforms as feature index.
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在链接到云南高校图书馆文献保障联盟下载...
云南高校图书馆联盟文献共享服务平台 版权所有©
您的IP:216.73.216.28