本文選題：地震圖像 + Curvelet分析　； 參考：《東北石油大學(xué)》2017年碩士論文

【摘要】：近年來隨著石油勘探的不斷發(fā)展,勘探地區(qū)的環(huán)境越來越復(fù)雜,加劇了地震勘探圖像的不規(guī)則和不完整情況,影響對資料的處理和解釋,最終影響油氣判斷。過去傳統(tǒng)用的重建方法受Nyquist采樣定理的限制需要較高的采樣率,面對復(fù)雜的勘探情況不能做適當(dāng)?shù)牟杉{(diào)整,過去的勘探成本非常大,對于此現(xiàn)象,需要研究出一個良好的重建算法,盡量實(shí)現(xiàn)完整的地震圖像重建,來使得地震資料的利用率得以提高。本文將Curvelet變換和壓縮感知理論結(jié)合,提出相關(guān)算法實(shí)現(xiàn)對地震圖像的重建,增強(qiáng)視覺質(zhì)量。主要研究內(nèi)容如下:1.基于Curvelet收縮閾值迭代算法的地震圖像重建的研究。結(jié)合壓縮感知理論,分析小波變換、DFT變換,Curvelet變換,并對地震圖像進(jìn)行重建,對比發(fā)現(xiàn),Curvelet變換具有良好的多尺度幾何分析能力,它能對具有曲線邊緣的地震圖像進(jìn)行最優(yōu)稀疏表達(dá)。把一個地震圖像區(qū)域分為多個子區(qū)域,實(shí)現(xiàn)一定間隔的隨機(jī)采樣。最后根據(jù)Curvelet變換高頻子帶信息熵變化的特點(diǎn),設(shè)計基于Curvelet變換的自適應(yīng)雙變量收縮閾值迭代重建算法。經(jīng)過對比實(shí)驗(yàn)的分析可知,該算法應(yīng)用在地震圖像中有良好的重建效果。2.基于Bregman迭代算法的地震圖像重建的研究。討論了重建算法中的Bregman迭代算法,Bregman迭代算法的基本概念,Bregman距離,分別列舉了常用的幾個算法:Bregman迭代算法、線性Bregman迭代算法、殘差Bregman迭代算法,比較它們優(yōu)缺點(diǎn)和特性,從而提出了改進(jìn)的Bregman迭代算法。在Bregman迭代框架中,采用軟閾值作為閾值算子H,并且提出了基于H-curve準(zhǔn)則的閾值參數(shù)選取,提高了地震圖像重建的準(zhǔn)確性。經(jīng)過對比實(shí)驗(yàn)的分析可知,改進(jìn)的Bregman迭代算法應(yīng)用在地震圖像中有良好的重建效果。3.基于壓縮感知觀測矩陣地震圖像重建的研究。根據(jù)地震圖像的特征,選取常用的幾種觀測矩陣,分析這五種觀測矩陣的特點(diǎn),將廣義輪換矩陣作為主要的討論對象。了解其構(gòu)造的原理,廣義輪換矩陣具有很強(qiáng)的穩(wěn)定性,其性能比其它觀測矩陣要好,除此之外廣義輪換矩陣同樣也是確定性觀測矩陣,容易硬件實(shí)現(xiàn)和存儲。通過研究發(fā)現(xiàn)它存在極強(qiáng)的列非相關(guān)性。也就是為了增強(qiáng)列與列之間非相關(guān)性可以修改觀測矩陣每一行前半段部分元素系數(shù),同時修改的系數(shù)值,強(qiáng)化了對低頻段的采樣。最后進(jìn)行對地震圖像重建的實(shí)驗(yàn),對實(shí)驗(yàn)結(jié)果進(jìn)行對比,發(fā)現(xiàn)廣義輪換矩陣的作為觀測矩陣時重建效果最好。
[Abstract]:In recent years, with the development of petroleum exploration, the environment of exploration area becomes more and more complex, which intensifies the irregular and incomplete situation of seismic exploration image, affects the processing and interpretation of data, and ultimately affects the judgment of oil and gas. The traditional reconstruction method used in the past is limited by the Nyquist sampling theorem and requires a high sampling rate. In the face of complex exploration conditions, it is impossible to make appropriate acquisition adjustment, and the exploration cost in the past was very large. It is necessary to develop a good reconstruction algorithm to realize the complete seismic image reconstruction as far as possible so as to improve the utilization ratio of seismic data. In this paper, Curvelet transform and compression sensing theory are combined to realize the reconstruction of seismic images and enhance the visual quality. The main research contents are as follows: 1. Research on seismic image reconstruction based on Curvelet shrinkage threshold iteration algorithm. Combined with the theory of compression perception, the wavelet transform DFT transform and Curvelet transform are analyzed, and the seismic images are reconstructed. It is found that the Curvelet transform has good multi-scale geometric analysis ability. It can perform optimal sparse representation of seismic images with curve edges. A seismic image region is divided into several sub-regions to realize random sampling at certain intervals. Finally, according to the characteristics of information entropy change in high frequency subband of Curvelet transform, an adaptive two-variable shrinkage threshold iterative reconstruction algorithm based on Curvelet transform is designed. The results of comparative experiments show that the algorithm has a good reconstruction effect in seismic images. Research on seismic image reconstruction based on Bregman iterative algorithm. In this paper, the basic concept of Bregman iterative algorithm and its basic concept, Bregman distance, are discussed. Several commonly used algorithms, such as: Bregman iterative algorithm, linear Bregman iterative algorithm, residual Bregman iterative algorithm, are listed respectively, and their advantages, disadvantages and characteristics are compared. Thus, an improved Bregman iterative algorithm is proposed. In the framework of Bregman iteration, the soft threshold is used as the threshold operator H, and the selection of threshold parameters based on H-curve criterion is proposed to improve the accuracy of seismic image reconstruction. The comparison experiment shows that the improved Bregman iterative algorithm has a good reconstruction effect in seismic images. Research on seismic image reconstruction based on compressed perceptual observation matrix. According to the characteristics of seismic images, several commonly used observation matrices are selected, the characteristics of these five observation matrices are analyzed, and the generalized rotation matrix is considered as the main object of discussion. Knowing the principle of its construction, the generalized rotation matrix has strong stability and its performance is better than that of other observation matrices. Besides, the generalized rotation matrix is also a deterministic observation matrix, which is easy to be implemented and stored in hardware. It is found that it has strong column noncorrelation. In other words, in order to enhance the non-correlation between the columns, the element coefficients of the first half of each row of the observation matrix can be modified, and the coefficient values can be modified at the same time, so that the sampling in the low frequency band can be strengthened. Finally, the experiment of seismic image reconstruction is carried out, and the experimental results are compared. It is found that the generalized rotation matrix is the best when it is used as the observation matrix.
【學(xué)位授予單位】：東北石油大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：P631.4

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