本文選題：微地震事件辨識(shí) + 震相初至�。� 參考：《山東科技大學(xué)》2017年碩士論文

【摘要】：基于聲發(fā)射學(xué)、地震學(xué)發(fā)展而來的微地震監(jiān)測(cè)技術(shù)是一種針對(duì)人類開采活動(dòng)中發(fā)生的渺小地震事件進(jìn)行采集、觀測(cè),進(jìn)而分析出人類活動(dòng)對(duì)地下巖層狀態(tài)產(chǎn)生的影響以及影響范圍的地球物理勘探技術(shù)。微地震監(jiān)測(cè)技術(shù)已經(jīng)廣泛應(yīng)用于沖擊地壓、礦井突水等礦井動(dòng)力災(zāi)害監(jiān)測(cè)預(yù)警,并已取得許多研究成果。微地震有效事件辨識(shí)及震相到時(shí)拾取是微地震監(jiān)測(cè)技術(shù)的核心問題之一,把微地震事件從海量震動(dòng)監(jiān)測(cè)數(shù)據(jù)中識(shí)別出來,并進(jìn)行震相初至?xí)r刻的準(zhǔn)確拾取是數(shù)據(jù)處理的首要步驟,也是震源定位研究、震源參數(shù)反演研究和震源機(jī)制解釋研究的基礎(chǔ)與條件。微地震監(jiān)測(cè)系統(tǒng)采集的微震數(shù)據(jù)具有非平穩(wěn)性、多樣性等特點(diǎn),由于受到采集現(xiàn)場機(jī)械震動(dòng)、電磁噪聲、巖石破裂、爆破振動(dòng)等多種外界因素的影響,采集的數(shù)據(jù)信噪比較低。常規(guī)的微地震事件辨識(shí)和震相初至拾取方法不能有效處理低信噪比信號(hào)。研究表明微地震信號(hào)在時(shí)頻域較為稀疏,基于此,本文提出了一種新的微地震事件辨識(shí)和震相到時(shí)自動(dòng)拾取方法,簡稱為S-AIC方法。該方法首先對(duì)采集的信號(hào)應(yīng)用小波變換進(jìn)行時(shí)頻分析,再利用Renyi計(jì)算函數(shù)計(jì)量時(shí)頻分析結(jié)果,根據(jù)熵值判定信號(hào)中是否存在微地震有效事件;其次,對(duì)含有微地震事件的監(jiān)測(cè)數(shù)據(jù)應(yīng)用基于小波變換的分層閾值法降噪;再次,基于時(shí)頻分析法計(jì)算出震相到時(shí)的大致位置,并為赤池信息準(zhǔn)則(AIC)算法選擇合適的時(shí)窗;最后,在選擇的時(shí)窗長度內(nèi)利用AIC算法計(jì)算出精確的震相到時(shí)。論文中使用程序?qū)ξ⒌卣饠?shù)據(jù)進(jìn)行了實(shí)驗(yàn),通過對(duì)比發(fā)現(xiàn):以人工辨識(shí)結(jié)果為參考,誤差在0-20ms范圍內(nèi)判定為準(zhǔn)確辨識(shí),本文方法對(duì)高信噪比信號(hào)的辨識(shí)準(zhǔn)確率較高,為100%,對(duì)低信噪比信號(hào)的辨識(shí)準(zhǔn)確率達(dá)到了 92%,優(yōu)于常規(guī)辨識(shí)方法。應(yīng)用AIC方法和S-AIC方法對(duì)抽選的高信噪比和低信噪比兩組數(shù)據(jù)共100個(gè)微地震信號(hào)進(jìn)行了震相初至拾取實(shí)驗(yàn),以人工拾取結(jié)果為參考,誤差在0-10ms范圍內(nèi)判定為準(zhǔn)確拾取,S-AIC方法對(duì)高信噪比信號(hào)拾取準(zhǔn)確率為100%,耗時(shí)0.812s;對(duì)低信噪比信號(hào)的拾取準(zhǔn)確率為91%,平均耗時(shí)1.325s。
[Abstract]:The microseismic monitoring technology based on acoustic emission and seismology is a kind of acquisition and observation of small earthquake events occurring in human mining activities. Furthermore, the influence of human activities on the state of underground strata and the geophysical exploration technique of the influence range are analyzed. Micro-seismic monitoring technology has been widely used in monitoring and warning of mine dynamic disasters, such as impact ground pressure, mine water inrush and so on, and many research results have been obtained. Effective microseismic event identification and phase arrival detection is one of the core problems of microseismic monitoring technology. The micro-seismic events are identified from massive seismic monitoring data. It is the first step of data processing and the foundation and condition of source location, source parameter inversion and focal mechanism interpretation. The microseismic data collected by the micro-seismic monitoring system have the characteristics of non-stationarity and diversity. Due to the influence of many external factors such as mechanical vibration, electromagnetic noise, rock rupture and blasting vibration, the SNR of the collected data is low. The conventional methods of microseismic event identification and phase first arrival pickup can not effectively deal with low signal-to-noise ratio (SNR) signals. It is shown that the microseismic signal is sparse in time-frequency domain. Based on this, a new method of microseismic event identification and phase arrival automatic pick-up, called S-AIC method, is proposed in this paper. In this method, wavelet transform is applied to time-frequency analysis of collected signals, and then Renyi function is used to calculate the time-frequency analysis results to determine whether there are micro-seismic effective events in the signals according to entropy. For the monitoring data containing micro-seismic events, the hierarchical threshold method based on wavelet transform is used to reduce the noise. Thirdly, the approximate position of phase arrival is calculated based on time-frequency analysis method, and the appropriate time window is selected for the red pool information criterion (AIC) algorithm. The exact phase arrival time is calculated by using the AIC algorithm in the selected window length. In this paper, the microseismic data are tested by the program. It is found that the error is identified accurately in the range of 0-20ms with the reference of manual identification results, and the accuracy of identification of high signal-to-noise ratio (SNR) signals by this method is high. The accuracy of low signal-to-noise ratio (SNR) signal identification is 92, which is superior to the conventional identification method. Using AIC method and S-AIC method, a total of 100 microseismic signals with high SNR and low signal-to-noise ratio (SNR) were tested. The results of manual pickup were used as a reference. The accuracy rate of the S-AIC method is 0.812 s for high SNR signals and 91s for low signal-to-noise ratio signals, with an average time of 1.325 s.
【學(xué)位授予單位】：山東科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：P631.4

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本文編號(hào)：2099590