本文選題：地震子波估計(jì) + 共軛梯度��； 參考：《西南交通大學(xué)》2015年碩士論文

【摘要】：在地震勘探工作中,對(duì)地震資料進(jìn)行反褶積、波阻抗反演、AVO反演以及正演模型的建立這些工作都依賴于高精度的地震子波。而在實(shí)際勘探過(guò)程中,地震子波常常是未知的,必須通過(guò)我們已有的地震資料提取出真實(shí)的子波,常規(guī)的子波提取方法有直接法、確定性方法和統(tǒng)計(jì)性方法：直接法就是利用探測(cè)儀器直接提取地震子波,確定性估計(jì)方法就是在測(cè)井資料已知的前提下,首先由測(cè)井曲線計(jì)算出地層反射系數(shù),然后根據(jù)地震褶積模型結(jié)合地震數(shù)據(jù)提取出真實(shí)子波,其最大的優(yōu)點(diǎn)就是可直接計(jì)算出真實(shí)子波而不需要對(duì)子波和反射系數(shù)進(jìn)行假設(shè),但是對(duì)地震資料井震匹配程度,測(cè)井?dāng)?shù)據(jù)的準(zhǔn)確性依賴比較大；統(tǒng)計(jì)性子波估計(jì)方法是僅通過(guò)地震道自身的二階或高階統(tǒng)計(jì)特性來(lái)提取子波,該方法不需要測(cè)井?dāng)?shù)據(jù),但需要對(duì)地層反射系數(shù)和子波做出一定的假設(shè)。本文主要針對(duì)測(cè)井處和井旁地震道進(jìn)行研究。在測(cè)井?dāng)?shù)據(jù)已知的情況下,利用測(cè)井聲波和密度資料計(jì)算出實(shí)際地層反射系數(shù)進(jìn)行確定性子波估計(jì)。通過(guò)對(duì)地震褶積模型的觀察,我們可以將地震記錄的形成過(guò)程理解為,地層反射系數(shù)信號(hào)經(jīng)過(guò)子波系統(tǒng)濾波后得到的輸出信號(hào)即為地震道記錄,并將褶積模型下的子波估計(jì)問(wèn)題理解為自適應(yīng)信號(hào)處理中的系統(tǒng)識(shí)別問(wèn)題,從這個(gè)角度出發(fā)利用自適應(yīng)濾波算法來(lái)估計(jì)出最優(yōu)的子波。在自適應(yīng)算法中,LMS類算法具有結(jié)構(gòu)簡(jiǎn)單容易實(shí)現(xiàn)、計(jì)算復(fù)雜度低等優(yōu)點(diǎn),其最大的缺點(diǎn)就是收斂慢,而RLS類算法雖然能快速收斂,但其計(jì)算復(fù)雜,需要較大的存儲(chǔ)空間進(jìn)行矩陣運(yùn)算,且存在數(shù)值不穩(wěn)定。共軛梯度(CG)算法是一種在性能上介于LMS和RLS之間的算法,它在收斂快的同時(shí)具有較低的計(jì)算復(fù)雜度。因此,本文選擇共軛梯度濾波算法來(lái)研究地震子波估計(jì)問(wèn)題。通過(guò)對(duì)現(xiàn)有子波估計(jì)方法進(jìn)行研究,針對(duì)子波估計(jì)的實(shí)際問(wèn)題,提出相應(yīng)的解決方案。針對(duì)在實(shí)際地震勘探中子波長(zhǎng)度的不確定性,利用高階累計(jì)量MA模型階數(shù)判定方法粗略的估計(jì)出地震子波長(zhǎng)度；考慮到大多數(shù)地震數(shù)據(jù)長(zhǎng)度有限,提出利用遞歸塊方法來(lái)提高共軛梯度算法的收斂性能,使得算法能在有限次迭代內(nèi)收斂；傳統(tǒng)的子波估計(jì)方法往往都是在背景噪聲為白噪聲或色噪聲假設(shè)下進(jìn)行的,考慮到地震勘探環(huán)境復(fù)雜,既有可能存在脈沖噪聲的情形,結(jié)合M-估計(jì)算法提出針對(duì)非高斯噪聲的魯棒性子波估計(jì)方法；觀察通過(guò)對(duì)實(shí)際子波波形的觀察,我們發(fā)現(xiàn)子波能量比較集中,且存在較長(zhǎng)的零區(qū)間,這里我們就可以將子波理解為稀疏或半稀疏信號(hào),通過(guò)對(duì)目標(biāo)函數(shù)加入估計(jì)子波稀疏性約束提高算法性能和子波的精確度。理論模型仿真和實(shí)際地震資料處理都表明,本文提出的算法能有效的抑制高斯和脈沖非高斯噪聲,快速有效的提取出精確的子波。
[Abstract]:In seismic exploration, seismic data deconvolution, wave impedance inversion and AVO inversion, as well as the establishment of forward model, all depend on high-precision seismic wavelet. In the actual exploration process, the seismic wavelet is often unknown. We must extract the real wavelet from the existing seismic data. The conventional wavelet extraction method has the direct method. Deterministic method and statistical method: the direct method is to extract seismic wavelet directly by means of detecting instruments. The deterministic estimation method is to calculate the formation reflection coefficient from the log curve on the premise that the log data is known. Then the real wavelet is extracted according to the seismic convolution model and seismic data. Its biggest advantage is that the real wavelet can be directly calculated without the assumption of wavelet and reflection coefficient, but the well earthquake matching degree of seismic data can be obtained. The accuracy of logging data depends heavily on the statistical wavelet estimation method, which only uses the second-order or high-order statistical characteristics of the seismic trace itself to extract the wavelet, which does not require the logging data. But it is necessary to make some assumptions about the formation reflection coefficient and wavelet. This paper mainly focuses on the well logging and the seismic trace around the well. Under the condition that the log data are known, the reflection coefficient of the actual formation is calculated by using the log acoustic wave and density data to estimate the deterministic wavelet. By observing the seismic convolution model, we can interpret the formation process of seismic records as that the output signals of formation reflection coefficient signals filtered by wavelet system are seismic trace records. The wavelet estimation problem based on convolution model is interpreted as a system recognition problem in adaptive signal processing. From this point of view, the optimal wavelet estimation is obtained by using adaptive filtering algorithm. In the adaptive algorithm, the LMS-class algorithm has the advantages of simple structure, low computational complexity and so on. Its biggest disadvantage is the slow convergence, while the RLS class algorithm can converge quickly, but its calculation is complex. Large storage space is needed for matrix operation, and the numerical value is unstable. The conjugate gradient CGG algorithm is an algorithm between LMS and RLS in performance. It has fast convergence and low computational complexity. Therefore, the conjugate gradient filtering algorithm is chosen to study the problem of seismic wavelet estimation. By studying the existing wavelet estimation methods, a corresponding solution is proposed to solve the practical problems of wavelet estimation. In view of the uncertainty of neutron wave length in actual seismic exploration, seismic wavelet length is roughly estimated by using the order determination method of high order cumulant MA model, considering that the length of most seismic data is limited. A recursive block method is proposed to improve the convergence performance of the conjugate gradient algorithm, so that the algorithm can converge within a finite iteration, and the traditional wavelet estimation methods are usually carried out under the assumption that the background noise is white or colored noise. Considering the complexity of seismic exploration environment and the possibility of impulse noise, a robust wavelet estimation method for non- noise is proposed in combination with the M- estimation algorithm, and the actual wavelet waveform is observed. We find that wavelet energy is concentrated and there is a long zero interval. Here we can interpret wavelet as sparse or semi-sparse signal and improve the algorithm performance and wavelet accuracy by adding wavelet sparsity constraints to the objective function. Theoretical model simulation and actual seismic data processing show that the proposed algorithm can effectively suppress Gao Si and impulse non- noise and extract accurate wavelet quickly and effectively.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：P631.4

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本文編號(hào)：1785429