本文選題：地震勘探資料　切入點(diǎn)：時(shí)頻峰值濾波　出處：《吉林大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：油氣資源與人民生活和國(guó)民經(jīng)濟(jì)發(fā)展息息相關(guān)，，而地震勘探正是剖析、透視地下未知地理結(jié)構(gòu)和探尋未知油氣資源的重要手段。然而，地表環(huán)境和地底下構(gòu)造復(fù)雜，嚴(yán)重降低了地震勘探資料的信噪比，影響了研究者對(duì)地震勘探資料的解釋，進(jìn)而不能準(zhǔn)確定位油氣及礦藏資源的位置，最終浪費(fèi)了人力、物力。因此，有效的提高地震勘探資料的信噪比是地震勘探中具有挑戰(zhàn)性的難點(diǎn)問(wèn)題。為突破此問(wèn)題，就需要我們提出合適的方法，既清晰地保留地震勘探信號(hào)又有效地壓制地震勘探隨機(jī)噪聲。 時(shí)頻峰值濾波(time-frequency peak filtering, TFPF)在恢復(fù)低信噪比地震勘探資料同相軸方面已經(jīng)取得了一些成效，其無(wú)偏估計(jì)的條件為：信號(hào)線性且噪聲為高斯白噪聲。但實(shí)際地震勘探信號(hào)具有較強(qiáng)的非線性，不能滿足時(shí)頻峰值濾波無(wú)偏估計(jì)的線性條件。為此，在實(shí)際應(yīng)用中我們采用加時(shí)域窗的Wigner-Ville分布來(lái)提高信號(hào)的局部線性度，從而減小時(shí)頻峰值濾波偏差。但是單一窗長(zhǎng)很難在噪聲壓制和信號(hào)恢復(fù)兩方面得到權(quán)衡。選取小窗長(zhǎng)時(shí)，信號(hào)的線性度得到了提高，信號(hào)恢復(fù)效果較好，但噪聲壓制效果不理想；選取大窗長(zhǎng)時(shí)，噪聲能得到有效壓制，但信號(hào)線性度不能有效提高，信號(hào)幅值衰減嚴(yán)重。所以綜合考慮信號(hào)恢復(fù)和噪聲壓制，本文提出了基于Shapiro Wilk和Shapiro Francia檢驗(yàn)(SW檢驗(yàn))的時(shí)空時(shí)變軌線時(shí)頻峰值濾波消減地震勘探隨機(jī)噪聲。 在基于SW檢驗(yàn)的時(shí)空時(shí)變軌線時(shí)頻峰值濾波算法中，首先構(gòu)建了與不同彎曲程度的同相軸相匹配的時(shí)變?yōu)V波軌線。因?yàn)橥噍S具有橫向連續(xù)性，所以，沿時(shí)變軌線對(duì)不同彎曲程度同相軸重采樣后，得到的同一信號(hào)幅值幾乎相同，最大限度地提高了信號(hào)的線性度，解決了時(shí)頻峰值濾波對(duì)非線性地震勘探信號(hào)估計(jì)存在偏差的難題。其次為了得到時(shí)變?yōu)V波軌線，結(jié)合了統(tǒng)計(jì)學(xué)中的SW高斯性檢驗(yàn)，其主要思想為：根據(jù)地震勘探隨機(jī)噪聲和地震勘探信號(hào)的SW高斯性統(tǒng)計(jì)量值差異，檢測(cè)出地震勘探記錄中不同彎曲程度的同相軸，從而提取出與不同彎曲程度的同相軸相匹配的時(shí)空時(shí)變軌線。最后沿時(shí)變軌線對(duì)地震勘探記錄進(jìn)行重采樣，并對(duì)重采樣后的信號(hào)應(yīng)用時(shí)頻峰值濾波，提高了地震信號(hào)恢復(fù)的精度。 為了驗(yàn)證本文提出算法的有效性，將其應(yīng)用到了人工合成地震勘探記錄和實(shí)際地震勘探記錄的處理，并與傳統(tǒng)時(shí)頻峰值濾波和徑向道時(shí)頻峰值濾波結(jié)果進(jìn)行了對(duì)比。實(shí)驗(yàn)發(fā)現(xiàn)：本文提出算法在濾波后信噪比、信號(hào)峰值等方面都優(yōu)于其他兩種算法，說(shuō)明本文算法在壓制隨機(jī)噪聲的同時(shí)，能更好地恢復(fù)有效信號(hào)的細(xì)節(jié)，增強(qiáng)同相軸的連續(xù)性。
[Abstract]:Oil and gas resources are closely related to people's daily life and the development of national economy, and seismic exploration is an important means of analyzing, looking through the unknown geographical structure of underground and exploring unknown oil and gas resources. However, the surface environment and underground structure are complex. It seriously reduces the signal-to-noise ratio of seismic exploration data, affects the interpretation of seismic exploration data by researchers, and then fails to accurately locate the location of oil, gas and mineral resources, which ultimately wastes manpower and material resources. Improving the signal-to-noise ratio of seismic exploration data effectively is a challenging and difficult problem in seismic exploration. In order to break through this problem, we need to put forward a suitable method. The random noise of seismic exploration can be suppressed effectively as well as retaining the seismic exploration signal clearly. Time-frequency peak filtering (TFPF) has made some achievements in restoring the cophase axis of seismic exploration data with low signal-to-noise ratio (SNR). The condition of unbiased estimation is that the signal is linear and the noise is Gao Si white noise, but the actual seismic exploration signal has strong nonlinearity and can not satisfy the linear condition of time-frequency peak filter unbiased estimation. In practical application, we use the Wigner-Ville distribution with time-domain window to improve the local linearity of the signal, thus reducing the peak filtering deviation of the hourly frequency. However, the single window length is difficult to be balanced between noise suppression and signal recovery. The linearity of signal is improved, the effect of signal recovery is better, but the effect of noise suppression is not ideal. When the window is large, the noise can be suppressed effectively, but the linearity of signal can not be improved effectively. The amplitude of the signal attenuates seriously. Therefore, considering the signal recovery and noise suppression, a time-frequency peak filtering method based on Shapiro Wilk and Shapiro Francia test is proposed to reduce the random noise in seismic exploration. In the time-frequency peak filtering algorithm of time-varying track based on SW test, the time-varying filter rail line matching with the in-phase axis with different bending degree is first constructed. Because the in-phase axis has transverse continuity, so, The amplitude of the same signal is almost the same after sampling the same phase with different bending degree along the time-varying rail line, which improves the linearity of the signal to the maximum extent. In order to obtain the time-varying filtering trajectory, the time-frequency peak filter is combined with the SW Gao Si test in statistics to solve the problem of the deviation of time-frequency peak filtering to the estimation of nonlinear seismic exploration signal. The main idea is: according to the difference of SW Gao Si statistical value between random noise of seismic exploration and seismic exploration signal, the same phase axis with different bending degree in seismic exploration record is detected. In order to extract the time-varying track line matching the same phase axis with different bending degree, finally resampling the seismic exploration record along the time-varying track line, and applying time-frequency peak filter to the signal after resampling. The accuracy of seismic signal recovery is improved. In order to verify the validity of the proposed algorithm, the proposed algorithm is applied to the processing of synthetic seismic exploration records and actual seismic exploration records. Compared with the results of traditional time-frequency peak filtering and radial channel time-frequency peak filtering, the experimental results show that the proposed algorithm is superior to the other two algorithms in signal-to-noise ratio (SNR) and signal peak value after filtering. It is shown that the proposed algorithm can recover the details of the effective signal and enhance the continuity of the cophase axis while suppressing the random noise.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：P631.4;TN911.4

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