本文選題：反褶積　切入點(diǎn)：短時傅立葉變換譜　出處：《成都理工大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：由于傅立葉變換是反映全局信息的變換,因此,無法表現(xiàn)信號的時間—頻率的局部的特性。地震波在地下傳播時,由于受到散射、吸收等影響,因而地震信號為非平穩(wěn)信號。其統(tǒng)計(jì)量是隨時間變化的函數(shù),其頻率特性也是改變的,一個單一的時間域或頻域分析遠(yuǎn)不能滿足實(shí)際的應(yīng)用的需要。常規(guī)傅立葉變換只適用于平穩(wěn)信號分析,不適用于非平穩(wěn)信號,因而無法描述地震信號的時間—頻率信息。因此,應(yīng)該使用聯(lián)合的信號時頻分析特性分布的方法來展現(xiàn)地震信號的特征。近年來,時頻分析已經(jīng)是信號處理中的一個熱點(diǎn)。采用時頻分析方法,將時域轉(zhuǎn)換到頻域,可以在時頻分布圖顯示出信號頻譜與時間的關(guān)聯(lián)特性。本文具體介紹了短時傅立葉變換、小波變換、S變換、廣義S變換、基于匹配追蹤的時頻分析方法和反褶積短時傅立葉變換的基本原理以及優(yōu)缺點(diǎn)。文中對幾種理論信號進(jìn)行多種時頻分析方法的計(jì)算機(jī)仿真效果對比,結(jié)果證明,反褶積短時傅立葉變換與傳統(tǒng)的時頻分析方法相比更具優(yōu)勢。最近幾年,利用地震信號的低頻信息進(jìn)行儲層預(yù)測及油氣識別方面取得新的進(jìn)展,低頻信息的應(yīng)用越來越受到重視。理論及實(shí)踐表明:地震波穿過流體時,地震信號的高頻部分能量被吸收,而頂界面高頻能量不會發(fā)生衰減,使得在單頻剖面上含油氣儲層下方能量與含油氣儲層中上部分的能量出現(xiàn)差異。這個現(xiàn)象為低頻陰影現(xiàn)象。在一定條件下,低頻陰影可作為含油氣性的一個直接指標(biāo)。油氣藏識別的有利標(biāo)志為:低頻時,上強(qiáng)下強(qiáng);高頻時,上強(qiáng)下弱。然而對于薄儲層,低頻陰影現(xiàn)象仍然較弱,因此有必要采用具有更高時頻分辨率的時頻分析方法進(jìn)行低頻陰影的識別。本文對三層水平層狀模型進(jìn)行低頻陰影現(xiàn)象的數(shù)值模擬,由結(jié)果可知,反褶積短時傅立葉變換方法比廣義S變換在薄儲層預(yù)測中取得了更好的效果。筆者采用反褶積短時傅立葉變換和廣義S變換兩種時頻分析方法對實(shí)際地震數(shù)據(jù)提取單頻剖面,結(jié)合原始全頻帶地震數(shù)據(jù)分析進(jìn)行油氣檢測。對比發(fā)現(xiàn),高分辨時頻分析方法(如反褶積短時傅立葉變換)取得更好效果。
[Abstract]:Because Fourier transform is a transformation that reflects global information, it can not express the local characteristics of time-frequency of the signal. The seismic wave is affected by scattering, absorption and so on when it propagates underground. Therefore, the seismic signal is a non-stationary signal. Its statistic is a function of time variation, and its frequency characteristic is also changed. A single time-domain or frequency-domain analysis is far from meeting the needs of practical applications. The conventional Fourier transform is only suitable for stationary signal analysis, not for non-stationary signals, so it is impossible to describe the time-frequency information of seismic signals. In recent years, time-frequency analysis has become a hot spot in signal processing. The time-frequency analysis method is used to convert time domain to frequency domain. The correlation between signal spectrum and time can be shown in time-frequency distribution diagram. In this paper, the short time Fourier transform, wavelet transform S transform, generalized S transform, generalized S transform are introduced in detail. The basic principle and advantages and disadvantages of time-frequency analysis method based on matching tracing and deconvolution short-time Fourier transform are presented. The computer simulation results of several theoretical signals are compared and the results show that, The short-time Fourier transform of deconvolution is superior to the traditional time-frequency analysis method. In recent years, new progress has been made in reservoir prediction and oil and gas identification by using low-frequency information of seismic signals. The application of low frequency information has been paid more and more attention. The theory and practice show that the high frequency partial energy of the seismic signal is absorbed when the seismic wave passes through the fluid, but the high frequency energy of the top interface does not decay. The difference between the energy under the oil and gas reservoir and the energy in the upper part of the oil and gas reservoir on the single frequency profile is shown. This phenomenon is a low frequency shadow phenomenon. Under certain conditions, Low frequency shadow can be used as a direct index of oil and gas bearing property. The favorable marks of oil and gas reservoir identification are: low frequency, strong and strong, high frequency, strong and weak, but for thin reservoir, low frequency shadow is still weak. Therefore, it is necessary to use time-frequency analysis method with higher time-frequency resolution to identify low-frequency shadows. The method of deconvolution short-time Fourier transform is more effective than generalized S-transform in thin reservoir prediction. Two time-frequency analysis methods of deconvolution short-time Fourier transform and generalized S-transform are used to extract the single frequency section from the actual seismic data. Combined with the original full-band seismic data analysis for oil and gas detection, it is found that the high-resolution time-frequency analysis method (such as deconvolution short-time Fourier transform) has better results.
【學(xué)位授予單位】：成都理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：P631.4;P618.13

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本文編號：1564905