基于多尺度主成分分析的地震數(shù)據(jù)局部斜率的計(jì)算
發(fā)布時間:2018-02-21 08:07
本文關(guān)鍵詞: 數(shù)據(jù)斜率 主成分分析 多尺度分解 出處:《哈爾濱工業(yè)大學(xué)》2015年碩士論文 論文類型:學(xué)位論文
【摘要】:地震勘探是地球物理研究的重要途徑之一。傳統(tǒng)的地震數(shù)據(jù)分析主要是在預(yù)處理數(shù)據(jù)的基礎(chǔ)上,建立合適的速度模型,通過精確的速度信息進(jìn)行數(shù)據(jù)處理。然而,精確的速度求取,不僅需要花費(fèi)大量的時間,而且涉及的參數(shù)不穩(wěn)定,使得數(shù)據(jù)處理結(jié)果存在一定偏差。而數(shù)據(jù)的斜率屬性幾乎包含了數(shù)據(jù)的全部信息,并且容易求取,因此,基于局部斜率的數(shù)據(jù)處理方法在地震勘探中發(fā)揮了越來越重要的作用。本文提出了一種基于多尺度分解和主成分分析(PCA)原理的地震反射波同向軸斜率計(jì)算的方法。該方法通過圖像的高斯金字塔分解,將圖像分解到不同分辨率上,然后,進(jìn)行各尺度圖像邊緣提取和圖像梯度的計(jì)算,并對各個尺度的圖像梯度進(jìn)行相同模式和數(shù)量的分塊。由于主成分分析方法和奇異值分解(SVD)的等價(jià)性,我們采用SVD方法簡便得到數(shù)據(jù)塊的局部方向的極大似然估計(jì),并通過不同尺度間信息的遞歸,將低分辨率斜率信息傳遞到高分辨率圖像層。在整個的計(jì)算中,通過不同尺度信息的傳遞減少了噪聲對圖像數(shù)據(jù)斜率計(jì)算精度的影響,得到比較精確地斜率信息。此外,本文將分別實(shí)現(xiàn)不含噪聲、含有噪聲的合成地震數(shù)據(jù)及真實(shí)數(shù)據(jù)進(jìn)行斜率計(jì)算的數(shù)值模擬,以證明該方法的可行性、對噪聲的魯棒性。
[Abstract]:Seismic exploration is one of the important ways in geophysical research. Traditional seismic data analysis is mainly based on preprocessing data, establishing appropriate velocity model and processing data by accurate velocity information. It not only takes a lot of time to calculate the accurate speed, but also the parameter involved is unstable, which makes the data processing result have certain deviation. The slope attribute of the data contains almost all the information of the data, and it is easy to get, therefore, the slope attribute of the data contains almost all the information of the data, so, The data processing method based on local slope plays a more and more important role in seismic exploration. In this paper, a method for calculating coaxial slope of seismic reflection wave based on the principle of multi-scale decomposition and principal component analysis (PCA) is proposed. The method is decomposed by Gao Si pyramid of the image, The image is decomposed into different resolutions, and then the image edge extraction and image gradient are calculated at various scales. Because of the equivalence of principal component analysis (PCA) and singular value decomposition (SVD), we use the SVD method to obtain the maximum likelihood estimation of the local direction of the data block. The low resolution slope information is transferred to the high resolution image layer through the recursion of the information between different scales. In the whole calculation, the influence of noise on the accuracy of slope calculation of image data is reduced by the transmission of different scale information. In addition, the slope of synthetic seismic data without noise and real data are simulated respectively to prove the feasibility of the method and its robustness to noise.
【學(xué)位授予單位】:哈爾濱工業(yè)大學(xué)
【學(xué)位級別】:碩士
【學(xué)位授予年份】:2015
【分類號】:P631.4;TP391.41
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