本文選題：近震 + P波震相�。� 參考：《中國地震局地球物理研究所》2015年碩士論文

【摘要】：地震震相的檢測和識別是地震學(xué)研究中的重要課題,它是地震定位、地震預(yù)警及地球深部結(jié)構(gòu)等研究的基礎(chǔ)。地震震相的自動識別可大大提高地震速報和地震預(yù)警的速度,為震后應(yīng)急救援贏得寶貴時間。總結(jié)了目前較常用的震相自動識別方法,對比分析了三大類方法即單特征法(包括能量分析、偏振分析、高階統(tǒng)計量、分形分維、赤池信息量和頻譜分析等)、多特征法(包括全波震相分析、相關(guān)法及人工神經(jīng)網(wǎng)絡(luò)等)和綜合分析方法的優(yōu)缺點,并指出了震相自動識別的發(fā)展方向。在前人研究的基礎(chǔ)上,提出了一種震相自動識別新方法,即小波包-峰度AIC(Akaike Information Criteria,赤池信息準(zhǔn)則)方法。該方法由三部分組成：(1)利用加權(quán)STA/LTA (short term average/long term average,長短時窗平均比)方法自動檢測出有效的地震事件并拾取P波初至的粗略到時；(2)對粗略拾取的到時前后各推3秒的時間窗內(nèi)的信號進行小波包三尺度分解重構(gòu)；(3)分別計算三個尺度重構(gòu)信號的峰度AIC曲線并進行疊加,將疊加的AIC曲線的最小值作為最終拾取到的精細(xì)P波初至到時。為檢驗新方法效果,將其應(yīng)用于模擬事件的理論地震記錄,模擬事件參照云南地區(qū)實際震例設(shè)計。對理論地震記錄加入不同信噪比的高斯白噪聲和實際地震噪聲,以由射線追蹤技術(shù)得到的到時為標(biāo)準(zhǔn),對比了加權(quán)STA/LTA法、峰度AIC法和本文方法識別P波的效果。結(jié)果表明本文方法具有更強的抗噪能力,P波識別的精度更高。以云南地區(qū)722個近震垂直向記錄為例,考察了濾波方法、信噪比及初至清晰度對震相識別精度的影響。結(jié)果表明：FIR最佳頻帶濾波方法在提高信噪比及P波識別的精度上優(yōu)勢更突出；相對信噪比的影響,P波識別的精度受初至清晰度的影響更大。以人工拾取的震相到時為標(biāo)準(zhǔn),與加權(quán)STA/LTA、峰度AIC兩種方法相比,本文方法效果更好。對比了人工與自動拾取的P波走時曲線,進一步驗證了本文方法的可靠性。
[Abstract]:Seismic phase detection and identification is an important subject in seismology. It is the basis of earthquake location, earthquake warning and deep structure of the earth. The automatic identification of seismic phases can greatly improve the speed of earthquake rapid reporting and earthquake warning, and win valuable time for post-earthquake emergency rescue. In this paper, the methods of automatic seismic phase identification are summarized, and three kinds of methods, I. e., single feature method (including energy analysis, polarization analysis, high-order statistics, fractal dimension), are compared and analyzed. The advantages and disadvantages of red pool information and spectrum analysis, multi-feature method (including full-wave phase analysis, correlation method and artificial neural network) and synthetic analysis method are discussed. The development direction of automatic seismic phase recognition is pointed out. On the basis of previous studies, a new method of automatic seismic phase recognition is proposed, that is, wavelet packet kurtosis AIC(Akaike Information criteria (red cell information criterion). This method consists of three parts: 1) using weighted STA/LTA / short term average/long term average ratio) method to automatically detect effective seismic events and pick up the rough arrival of P wave. The signal in the time window is reconstructed by wavelet packet three-scale decomposition. (3) the kurtosis AIC curves of the three scale reconstructed signals are calculated and superposed, respectively. The minimum value of the superimposed AIC curve is regarded as the first arrival time of the finer P wave which is finally picked up. In order to test the effect of the new method, it is applied to the theoretical seismic records of the simulated events, and the simulated events are designed according to the actual earthquake examples in Yunnan region. The white Gao Si noise with different signal-to-noise ratio and the actual seismic noise are added to the theoretical seismic records. Using the arrival time obtained from the ray tracing technique as the standard, the effects of weighted STA/LTA method, kurtosis AIC method and the present method on P wave identification are compared. The results show that the proposed method has stronger anti-noise ability and higher accuracy of P wave recognition. Taking 722 vertical seismic records in Yunnan as an example, the effects of filtering method, signal-to-noise ratio (SNR) and initial resolution on the accuracy of seismic phase identification are investigated. The results show that the ratio Fir optimal band filtering method has more advantages in improving the signal-to-noise ratio and the accuracy of P wave recognition, and the relative signal-to-noise ratio affects the accuracy of P wave recognition more greatly due to the initial clarity. According to the criterion of phase arrival, the proposed method is more effective than the weighted STA-LTA and kurtosis AIC methods. The P wave travel time curves of manual and automatic pick-up are compared, and the reliability of this method is further verified.
【學(xué)位授予單位】：中國地震局地球物理研究所
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：P315.6

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