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淺地層剖面數(shù)據(jù)多次波自動搜索與壓制

發(fā)布時間:2018-08-13 21:07
【摘要】:伴隨著國內(nèi)經(jīng)濟飛速發(fā)展和技術革新,陸地上資源被逐步開發(fā)消耗,為了解決資源短缺的問題我們把目標轉(zhuǎn)向了資源豐富的海洋。海上淺地層剖面地震勘探是海洋地質(zhì)資源普查和勘探的重要方法,因海水面和海底地層強反射界面的影響,使海上地震多次波發(fā)育明顯。多次波的存在是對有效一次反射波的干擾,使實際資料信噪比降低及對有效波的識別及解釋造成困難,多次波分離去除是海洋勘探領域地震數(shù)據(jù)處理中的一項難題。因此,如何壓制淺地層剖面多次波是本文最主要的研究內(nèi)容。淺地層剖面得到的地震數(shù)據(jù)是單道自激自收觀測得到的數(shù)據(jù),沒有偏移距也無法求取速度信息,該類數(shù)據(jù)中多次波不具有有效波和多次波之間正常時差差異特征,也無法準確求出多次波的時間、周期、振幅、以及相位等信息,常用的多次波壓制方法存在不足之處;陬A測反褶積算法,本文研究多次波自動搜索與壓制的方法,利用自相關函數(shù)對步長進行自動搜索,將得到的步長去求解托布利茲方程得到濾波系數(shù),去除含有多次波的濾波系數(shù)褶積后得到壓制多次波后的結(jié)果。在壓制多次波前,需要進行數(shù)據(jù)預處理,首先對數(shù)據(jù)進行濾波和去除隨機噪音,提高數(shù)據(jù)信噪比;其次,設計合適的整形濾波算子對數(shù)據(jù)進行子波處理為多次波的去除打好基礎;最后,對每一道數(shù)據(jù)用自相關函數(shù)自動檢測出多次波的周期利用自動搜索與壓制的方法壓制多次波。針對零偏移距、超高主頻的淺地層剖面數(shù)據(jù)的多次波壓制難題,編寫了復雜海底地形地質(zhì)條件下淺地層剖面自相關、數(shù)據(jù)濾波、去除隨機噪音、子波處理和多次波自動搜索與壓制等程序。利用本文中的多次波自動搜索與壓制方法對模型數(shù)據(jù)進行試算,并用于實際淺地層剖面資料多次波壓制,結(jié)果驗證了多次波自動搜索與壓制程序的穩(wěn)定可靠,該處理方法能夠有效的壓制多次波。
[Abstract]:With the rapid development of domestic economy and technological innovation, the land resources have been gradually exploited and consumed. In order to solve the problem of resource shortage, we have turned to the resource-rich ocean. Seismic exploration of offshore shallow formation profile is an important method for marine geological resources survey and exploration. Due to the influence of strong reflection interface between sea water surface and undersea strata, seismic multiples on the sea have developed obviously. The existence of multiple waves is a kind of interference to the effective primary reflection wave, which reduces the signal-to-noise ratio of the actual data and makes it difficult to identify and interpret the effective wave. The separation and removal of multiple waves is a difficult problem in seismic data processing in the field of ocean exploration. Therefore, how to suppress the shallow stratigraphic profile multiple is the main research content in this paper. The seismic data obtained from the shallow formation profile are the data obtained from the single track self-excited self-receiving observation, and the velocity information can not be obtained without migration distance. The multiples in this kind of data do not have the characteristics of normal time difference between effective waves and multiple waves. The information of time, period, amplitude and phase of multiple waves can not be accurately calculated. Based on predictive deconvolution algorithm, the method of multiple automatic searching and suppression is studied in this paper. The step size is automatically searched by autocorrelation function, and the step size is obtained to solve the Tobriz equation to get the filtering coefficient. After removing the convolution of filtering coefficients containing multiple waves, the results of suppressing multiple waves are obtained. In the suppression of multiples, data preprocessing is needed. Firstly, the data is filtered and the random noise is removed to improve the signal-to-noise ratio of the data. Designing proper shaping filter operator for wavelet processing of data is a good foundation for the removal of multiple waves. Finally, the period of multiple waves is automatically detected by autocorrelation function, and the method of automatic searching and suppression is used to suppress multiple waves. In view of the problem of multi-wave suppression of zero-offset and ultra-high frequency shallow stratum profile data, the autocorrelation, data filtering and random noise removal of shallow stratum profile under complex submarine topography and geology are compiled. Wavelet processing and multiple automatic search and suppression programs. The method of automatic search and suppression of multiple waves in this paper is used to calculate the model data, and it is used to suppress multiple waves of actual shallow profile data. The results show that the program of automatic search and suppression of multiple waves is stable and reliable. The method can suppress multiple waves effectively.
【學位授予單位】:中國地質(zhì)大學(北京)
【學位級別】:碩士
【學位授予年份】:2017
【分類號】:P631.4

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