本文選題：地震盲反褶積 + 反射系數(shù)�。� 參考：《天津大學(xué)》2015年博士論文

【摘要】：地震盲反褶積是指在地震子波和反射系數(shù)均未知的情況下,僅利用地震記錄實(shí)現(xiàn)地震子波和反射系數(shù)的有效估計(jì),其目的是從地震記錄中分離地震子波,提取高精度的反射系數(shù),改善地震反射系數(shù)資料的分辨率,拓寬其有效頻譜,保留高頻分量,從而能更精確地表征地層結(jié)構(gòu)。地震盲反褶積算法廣泛應(yīng)用于地震信號(hào)分析、地質(zhì)考察、資源探測(cè)、石油勘探、海洋地震勘探等領(lǐng)域。本文主要研究基于Bayesian框架下的地震盲反褶積問(wèn)題,通過(guò)建立地震子波、地震記錄以及反射系數(shù)的相關(guān)先驗(yàn)?zāi)Ｐ?利用參數(shù)估計(jì)的方法獲得反射系數(shù)。所做的主要工作如下:(1)研究了變分Bayesian地震盲反褶積算法和稀疏表示的變分Bayesian地震盲反褶積算法,分別建立了地震子波、反射系數(shù)和地震記錄的分層Bayesian先驗(yàn)?zāi)Ｐ?推導(dǎo)了算法迭代公式,進(jìn)行了計(jì)算機(jī)仿真,結(jié)果表明新算法有效分離了地震子波,獲得了較高精度的反射系數(shù),改善了均方誤差等量化指標(biāo)。(2)研究了基于部分折疊Gibbs抽樣的Bayesian多通道地震盲反褶積算法,討論了多通道反射系數(shù)的馬爾可夫貝努利高斯模型,通過(guò)部分折疊Gibbs抽樣得到反射系數(shù)最大后驗(yàn)分布的近似解,進(jìn)而實(shí)現(xiàn)反射系數(shù)的估計(jì);同時(shí)分析了改進(jìn)的馬爾可夫貝努利高斯模型,研究了局部邊緣化Gibbs抽樣的Bayesian多通道地震盲反褶積算法。實(shí)驗(yàn)結(jié)果表明新算法拓寬了地震反射系數(shù)資料的有效頻譜,提高了地震反射系數(shù)的分辨率,改善了損失函數(shù)等量化評(píng)價(jià)指標(biāo)。(3)研究了基于線性小波和Curvelet變換的兩種Bayesian壓縮感知地震盲反褶積算法,分析了Bayesian壓縮感知框架下反射系數(shù)、地震子波以及超參數(shù)的先驗(yàn)分布,推導(dǎo)了算法迭代公式,利用期望最大化策略進(jìn)行參數(shù)估計(jì)。實(shí)驗(yàn)仿真分析表明,新算法改善了反褶積效果,降低了歸一化均方誤差,提高了地震反射系數(shù)的估計(jì)精度。
[Abstract]:Seismic blind deconvolution refers to the effective estimation of seismic wavelet and reflection coefficient only by using seismic records when the seismic wavelet and reflection coefficient are unknown. The purpose of the deconvolution is to separate seismic wavelet from seismic record. The high precision reflection coefficient is extracted, the resolution of seismic reflection coefficient data is improved, the effective frequency spectrum is widened, and the high frequency component is retained, thus the stratigraphic structure can be represented more accurately. Seismic blind deconvolution algorithm is widely used in seismic signal analysis, geological survey, resource exploration, petroleum exploration, marine seismic exploration and other fields. In this paper, the seismic blind deconvolution problem based on Bayesian framework is studied. The reflection coefficient is obtained by establishing a prior model of seismic wavelet, seismic record and reflection coefficient. The main work is as follows: (1) the variational Bayesian seismic blind deconvolution algorithm and the sparse representation variational Bayesian seismic blind deconvolution algorithm are studied. The hierarchical Bayesian prior models of seismic wavelet, reflection coefficient and seismic records are established, respectively. The iterative formula of the algorithm is derived and the computer simulation is carried out. The results show that the new algorithm can effectively separate seismic wavelet and obtain a high precision reflection coefficient. The blind Bayesian seismic deconvolution algorithm based on partially folded Gibbs sampling is studied, and the Markov Bernoulli Gao Si model with multi-channel reflection coefficient is discussed. The approximate solution of the maximum posterior distribution of the reflection coefficient is obtained by partially folded Gibbs sampling, and the estimation of the reflection coefficient is realized, and the improved Markov Bernoulli Gao Si model is also analyzed. A blind Bayesian seismic deconvolution algorithm based on locally marginalized Gibbs sampling is studied. The experimental results show that the new algorithm broadens the effective spectrum of seismic reflection coefficient data and improves the resolution of seismic reflection coefficient. Based on linear wavelet transform and Curvelet transform, two blind deconvolution algorithms for Bayesian compression sensing seismic are studied. The prior distributions of reflection coefficient, seismic wavelet and superparameter are analyzed under the frame of Bayesian compression perception. The iterative formula of the algorithm is derived, and the parameter estimation is carried out by using the expectation maximization strategy. The experimental results show that the new algorithm improves the deconvolution effect, reduces the normalized mean square error, and improves the estimation accuracy of seismic reflection coefficient.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：P631.4

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