【摘要】：微震信號蘊(yùn)藏著豐富的巖體破裂信息,對其監(jiān)測并進(jìn)行數(shù)據(jù)處理分析可以獲取巖體破裂的位置及能量釋放情況,目前已在沖擊地壓、煤與瓦斯突出等煤巖動力災(zāi)害監(jiān)測預(yù)警領(lǐng)域得到廣泛應(yīng)用。但是礦下環(huán)境復(fù)雜多變,需要經(jīng)常進(jìn)行巖石爆破作業(yè),拾振器拾取的微震信號中往往摻雜著無法識別的爆破干擾信號,影響微震監(jiān)測及定位結(jié)果。因此如何有效的提取兩者的特征參數(shù)信息來識別巖石破裂微震信號和爆破振動信號顯得尤為重要。本文基于巖石破裂微震信號和爆破振動信號的時變非平穩(wěn)特征,通過對比幾種時頻分析的方法性能—短時傅里葉變換、小波變換和希爾伯特黃變換,提出了基于集合經(jīng)驗?zāi)B(tài)分解的巖石破裂微震信號和爆破振動信號的時頻能量特征提取和識別方法。首先,通過小波閾值去噪,將待測信號中的噪聲干擾成分盡可能的剔除,真實地還原信號波;其次對去噪的待測信號進(jìn)行集合經(jīng)驗?zāi)B(tài)分解(EEMD),獲得一系列本征模態(tài)函數(shù)(IMF);最后求得每個IMF的能量占總信號能量的比例來作為待測信號的時頻能量分布。由于巖石破裂微震信號和爆破振動信號的頻率分布狀況不同,故將求得的本征模態(tài)函數(shù)能量比值的分布情況來作為其特征參數(shù),來識別巖石破裂微震信號和爆破振動信號。通過對80組典型的煤巖破裂微震信號和爆破振動信號進(jìn)行實驗,結(jié)果顯示,煤巖破裂微震和爆破振動信號IMF能量分布有較大差別,煤巖破裂微震信號主要集中在IMF2、IMF3和IMF4的20-100Hz低頻段,爆破振動信號則在IMF1的225-375Hz高頻處較為集中。為把兩者信號差異最大化,從而形成區(qū)分兩者的有效特征參數(shù),將IMF2、IMF3和IMF4頻段能量合并為新頻段,爆破振動信號在IMF1與煤巖破裂微震信號在IMF(2+3+4)頻段內(nèi)能量值所占比例均在80%以上,區(qū)別最為明顯,故將IMF1與IMF(2+3+4)能量特征比例作為區(qū)分煤巖破裂微震信號和爆破振動信號的特征指標(biāo)。該分析方法為煤礦識別微震信號事件和爆破信號事件提供了一種新的思路,利用兩者能量分布差異較大、特征對比明顯等特點,可以實現(xiàn)對兩類波形信號的有效辨識。
[Abstract]:The microseismic signal contains abundant information of rock mass rupture. Monitoring and data processing can obtain the position of rock mass rupture and energy release. At present, rock burst is already under ground pressure. Coal and gas outburst are widely used in the field of monitoring and warning of coal and rock dynamic disasters. However, the mining environment is complex and changeable, so rock blasting is often needed. The microseismic signals picked up by vibration pickers are often mixed with unidentifiable blasting interference signals, which affect the monitoring and positioning results of microearthquakes. Therefore, it is very important to extract the characteristic parameter information to identify the rock rupture microseismic signal and the blasting vibration signal. Based on the time-varying non-stationary characteristics of rock rupture microseismic signals and blasting vibration signals, this paper compares the performance of several time-frequency analysis methods-short time Fourier transform, wavelet transform and Hilbert-Huang transform. A method of extracting and identifying time-frequency energy features of rock rupture microseismic signal and blasting vibration signal based on set empirical mode decomposition is proposed. Firstly, the wavelet threshold is used to remove the noise interference from the signal to be tested, and the signal wave can be truly restored. Secondly, the set empirical mode decomposition (EEMD),) of the denoised signal is used to obtain a series of intrinsic mode functions (IMF);). Finally, the ratio of the energy of each IMF to the total signal energy is obtained as the time-frequency energy distribution of the signal to be tested. Because the frequency distribution of rock rupture microseismic signal and blasting vibration signal is different, the distribution of eigenmode function energy ratio is taken as its characteristic parameter to identify rock rupture microseismic signal and blasting vibration signal. Based on the experiments of 80 typical coal and rock rupture microseismic signals and blasting vibration signals, the results show that the IMF energy distribution of coal and rock rupture microearthquakes and blasting vibration signals is quite different. The microseismic signals of coal and rock fracture are mainly concentrated in the low frequency band of 20-100Hz of IMF2F3 and IMF4, while the vibration signals of blasting are concentrated at the high frequency of 225-375Hz of IMF1. In order to maximize the difference between the two signals and form an effective characteristic parameter to distinguish the two, the energy of IMF2F3 and IMF4 band is combined into a new frequency band. The proportion of blasting vibration signals in the IMF (234) frequency band of IMF1 and coal rock rupture microseismic signals is more than 80%, the difference is most obvious. Therefore, the ratio of energy characteristic of IMF1 and IMF (234) is taken as the characteristic index to distinguish the microseismic signal of coal and rock fracture from the vibration signal of blasting. This analysis method provides a new way of thinking for coal mine to identify microseismic signal events and blasting signal events. The two kinds of waveform signals can be effectively identified by using the characteristics of great difference of energy distribution and obvious characteristic contrast between the two kinds of signals.
【學(xué)位授予單位】：山東科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TD326

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 燕天;洪飛;鄒金龍;馬捷;;基于短時傅里葉變換與相關(guān)解調(diào)的參數(shù)估計算法[J];探測與控制學(xué)報;2016年02期

2 李學(xué)龍;李忠輝;王恩元;景林波;馮俊軍;陳亮;;礦山爆破地震波混沌特征[J];遼寧工程技術(shù)大學(xué)學(xué)報(自然科學(xué)版);2016年02期

3 賈瑞生;譚云亮;孫紅梅;洪永發(fā);;低信噪比微震P波震相初至自動拾取方法[J];煤炭學(xué)報;2015年08期

4 欒俊寶;鄧兵;;短時分?jǐn)?shù)階傅里葉變換對調(diào)頻信號的時頻分辨能力[J];電訊技術(shù);2015年07期

5 田中大;李樹江;王艷紅;高憲文;;基于小波變換的風(fēng)電場短期風(fēng)速組合預(yù)測[J];電工技術(shù)學(xué)報;2015年09期

6 李慶忠;劉清;;基于小波變換的低照度圖像自適應(yīng)增強(qiáng)算法[J];中國激光;2015年02期

7 江文武;楊作林;謝建敏;李家福;;FFT頻譜分析在微震信號識別中的應(yīng)用[J];科技導(dǎo)報;2015年02期

8 趙國彥;鄧青林;馬舉;;基于FSWT時頻分析的礦山微震信號分析與識別[J];巖土工程學(xué)報;2015年02期

9 趙國彥;鄧青林;;基于LCD分解的微震信號分析與識別[J];科技導(dǎo)報;2014年27期

10 苗晟;王威廉;姚紹文;;Hilbert-Huang變換發(fā)展歷程及其應(yīng)用[J];電子測量與儀器學(xué)報;2014年08期

相關(guān)博士學(xué)位論文 前1條

1 李楠;微震震源定位的關(guān)鍵因素作用機(jī)制及可靠性研究[D];中國礦業(yè)大學(xué);2014年

相關(guān)碩士學(xué)位論文 前2條

1 馬舉;基于波形特征的礦山微震與爆破信號模式識別[D];中南大學(xué);2014年

2 景林波;煤礦微震信號特征及傳播規(guī)律研究[D];中國礦業(yè)大學(xué);2014年

，

本文編號：2174360