【作者】 王更峰；

【导师】 赵奎；

【作者基本信息】 江西理工大学 ， 岩土工程， 2007， 硕士

【摘要】 目前岩石声发射测量地应力确定Kaiser点时普遍采用的特征参数法,根据振铃计数率、能量计数率、振铃累计数等参数随时间变化关系确定参数明显增加点为Kaiser点,当这些参数在Kaiser点变化显著时,较容易判断,当这些参数在Kaiser点变化不十分显著时,则往往难以准确判断Kaiser点,测量精度和可靠性也缺乏系统深入地研究,尤其是采用参数分析法研究岩石声发射不能利用声发射的全部信息,不能进行声发射信号波形特征研究,存在的这些问题限制了声发射在测量岩石地应力方面的进一步发展,这也是岩石声发射技术仍是理论研究落后于工程实际的少数学科的重要原因之一。本文在单轴加载岩石破坏全过程声发射试验基础上,采用参数分析法确定了Kaiser点,对Kaiser点信号进行了频谱分析,确定了Kaiser点信号的频率范围,提出了基于小波分析的岩石声发射信号的信噪分离方法。结果表明,小波分析是处理岩石声发射Kaiser点信号的一种有效的方法。其次,应用小波包分析的方法研究砂岩Kaiser点及其相邻点信号在不同频带上能量分布的变化规律,得到了Kaiser点特征频带能量百分比大于相邻点的重要结论,该特征可作为识别岩石声发射Kaiser点的新判据。最后,采用G-P算法计算了声发射过程关联分形维数,结果表明,声发射过程不仅具有分形特征,而且Kaiser点声发射信号关联分维数小于其相邻点,得到的结论可作为通过波形分析识别Kaiser点的特征。更多还原

【Abstract】 The characteristic parameter analysis method to determine Kaiser point of rock acoustic emission(AE) in measuring initial stress is widely adopted, it depends on recording some parameters such as ring down count rate, energy count rate, ring down accumulate number and so on, then confirm the point which the parameter obviously increases as Kaiser point according to relationship between parameter and time.When these parameters change prominently in Kaiser point, it’s easier to judge, but when these parameters do not change very prominently in Kaiser point, it’s often difficult to judge accurately. Moreover the measure precision and dependability lack the systematicly and deeply studying too, especially the parameter analysis method which was used to comfirm the Kaiser point, it can’t utilize all the information of rock AE and can’t carry on the research of waveform characteristic of AE signal. The development of AE in measuring initial stress is limited due to above problems, it made the mechanism research of rock AE difficult to be developed for a long time, it is also one of the most important reasons why the rock AE technology theoretical research lags behind the project reality.Based on the AE tests of sandstone specimens under uniaxial compression, Kaiser point is determined by parameter method according to the principal of Kaiser effect and AE signal frequency distribution regularity is obtained by means of spectrum analysis, and the signal noise reduction method for AE at Kaiser point is presented basing on wavelet analysis.The results show that wavelet analysis is an effective way in noise reduction and signal processing of AE signal.Then, wavelet packet analysis method is used to research energy distribution of AE signals. Regularity rules of energy distributions for different frequency bands are obtained. Results show that the energy percentage of dominant frequency band at Kaiser point is significantly higher than other points.This characteristic can be used as a new criterion to determine Kaiser point.At last, relevant fractal dimensions of AE process are obtained using G-P algorithm. Results show that the AE process is of obvious fractal characteristics, and the minimum value of relevant fractal dimension is at Kaiser point. The conclusion can be used to identify the characteristics at Kaiser point by waveform analysis.更多还原