【摘要】：地震勘探是目前尋找石油及天然氣的主要方法,地震專家通過地震勘探資料可以識別地下巖層的地質(zhì)結(jié)構(gòu),從而推斷油氣煤田的地下位置,為石油、天然氣的開采提供有力的查找依據(jù)。地震勘探分成三個部分：地震資料采集、地震資料處理、地震資料解釋。其中,地震資料處理是地震勘探的重要部分,地震資料處理為下一步地震資料解釋提供清晰可辯的地震資料。在地震資料處理中如何獲得高信噪比的地震資料顯得尤為重要,地震勘探資料的去噪方法研究已成為地震勘探領(lǐng)域的一個研究熱點。時頻峰值濾波算法是基于時頻分析的理論來消減隨機噪聲。金雷等將其引用到地震勘探記錄中壓制隨機噪聲,并取得了較好的效果,時頻峰值濾波不依賴于信號的先驗信息,可以恢復出湮沒在較低信噪比噪聲的地震信號,提高地震勘探資料的信噪比。然而傳統(tǒng)的時頻峰值濾波采用的是固定窗長,未能兼顧隨機噪聲壓制與有效信號幅值的保持；時頻峰值濾波只是沿時間方向上進行濾波,是一維的時頻濾波算法,忽略了地震勘探資料空間中道與道之間信息,不能體現(xiàn)地震資料道與道之間的相關(guān)性,吳寧等引入徑向時頻峰值濾波算法,將地震資料通過徑向道變換,在徑向域進行時頻峰值濾波,然后將濾波后的記錄進行徑向道反變換。徑向道變換能將有效信號進行拉伸,在徑向道域進行時頻峰值濾波可選取較大的濾波窗長,使有效信號在窗內(nèi)盡可能是線性函數(shù),從而選取較大的窗長進行濾波,不影響信號幅值的保持,大窗長濾波更有利于隨機噪聲的壓制,改善了傳統(tǒng)時頻峰值濾波的不足,但是徑向時頻峰值濾波采用的是固定角度的采樣方式,在采樣軌線與同相軸夾角過大的地方,徑向道變換對信號的拉伸不明顯,徑向道變換會產(chǎn)生畸變,如果仍選用較大的窗長,地震記錄幅值會出現(xiàn)很大的衰減,使濾波出現(xiàn)誤差。本文提出基于相關(guān)積分的徑向時頻峰值濾波。在徑向道域中求出每道序列的相關(guān)積分值,利用噪聲序列與信號序列的相關(guān)積分值的不同,區(qū)分出含有有效信號段和噪聲段,選取大窗長對噪聲部分進行時頻峰值濾波,選取小窗長對信號部分進行時頻峰值濾波,將濾波后的記錄進行徑向道反變換。處理模擬地震記錄和實際地震記錄表明本文方法能有效提高了有效信號的幅值,噪聲也得到了很好的壓制,有效提高地震勘探資料的信噪比。
[Abstract]:Seismic exploration is the main method to search for oil and natural gas at present. Seismic experts can identify the geological structure of underground strata by seismic exploration data, and then infer the underground position of oil and gas coalfield, which is petroleum. The exploitation of natural gas provides a powerful basis for searching. Seismic exploration is divided into three parts: seismic data acquisition, seismic data processing, seismic data interpretation. Seismic data processing is an important part of seismic exploration, and seismic data processing provides clear and recognizable seismic data for the next step of seismic data interpretation. How to obtain the seismic data with high signal-to-noise ratio in seismic data processing is particularly important. The research on denoising methods of seismic exploration data has become a hot spot in the field of seismic exploration. Time-frequency peak filtering algorithm is based on the theory of time-frequency analysis to reduce random noise. Jin Lei et al used it to suppress random noise in seismic exploration records, and achieved good results. The time-frequency peak filtering does not depend on the prior information of the signal, and can recover the seismic signal which is annihilated in the lower SNR noise, and the time-frequency peak filtering does not depend on the prior information of the signal. Improve the signal-to-noise ratio of seismic exploration data. However, the traditional time-frequency peak filter uses a fixed window length, which fails to keep the amplitude of the effective signal and the random noise suppression. Time-frequency peak filtering is a one-dimensional time-frequency filtering algorithm, which only filters along the time direction. It ignores the information between trace and track in seismic exploration data space, and can not reflect the correlation between seismic data track and track. Wu Ning et al introduced the radial time-frequency peak filtering algorithm. The seismic data were filtered by radial channel transform and time-frequency peak filtering in radial domain. Then the filtered records were transformed by radial channel inverse transform. Radial channel transform can stretch the effective signal, and the filter window length can be selected by the time-frequency peak filtering in the radial channel domain, so that the effective signal can be a linear function in the window as far as possible, thus the larger window length can be selected to filter. The large window length filter is more favorable to suppress random noise and improve the deficiency of traditional time-frequency peak filtering, but the radial time-frequency peak filter adopts fixed-angle sampling mode, which does not affect the maintenance of signal amplitude, and large window length filter is more conducive to the suppression of random noise. Where the angle between the sampling trajectory and the in-phase axis is too large, the radial channel transformation does not stretch the signal obviously, and the radial channel transformation will cause distortion. If the window length is still large, the amplitude of the seismic record will be greatly attenuated and the filtering error will occur. In this paper, a radial time-frequency peak filter based on correlation integral is proposed. The correlation integral values of each channel sequence are obtained in the radial channel domain. By using the difference between the correlation integral values of the noise sequence and the signal sequence, the effective signal segment and the noise segment are distinguished, and the noise part is filtered by the time-frequency peak value of the noise part with large window length. Small window length is selected to filter the time-frequency peak value of the signal, and the filtered record is transformed by the radial channel inverse transform. The processing of simulated seismic records and actual seismic records shows that the proposed method can effectively increase the amplitude of effective signals, suppress the noise well, and effectively improve the signal-to-noise ratio of seismic exploration data.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：P631.4

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本文編號：2457741