發(fā)布時(shí)間：2018-11-20 21:16

【摘要】：大地電磁法作為一種重要的地球物理勘探手段,現(xiàn)已被廣泛應(yīng)用于研究地殼和上地幔地質(zhì)構(gòu)造以及深部礦產(chǎn)勘探。作為連接地球物理觀測與解釋的橋梁,反演方法研究一直都是學(xué)者關(guān)注的熱點(diǎn)。本文首先將智能優(yōu)化算法——果蠅優(yōu)化算法引入到大地電磁數(shù)據(jù)反演,避免線性化迭代方法需要計(jì)算偏導(dǎo)數(shù)矩陣、對初始模型依賴等缺點(diǎn)。果蠅優(yōu)化算法具有原理簡單、控制參數(shù)少,容易程序?qū)崿F(xiàn)等優(yōu)點(diǎn),通過對標(biāo)準(zhǔn)果蠅優(yōu)化算法的分析,發(fā)現(xiàn)其在處理高維、多峰的目標(biāo)函數(shù)時(shí)存在收斂緩慢、易陷入局部極值的情況,為此對其進(jìn)行了改進(jìn),加入了差分進(jìn)化算法的交叉操作和變異操作,增加了果蠅的種群多樣性,提高全局優(yōu)化能力,同時(shí)利用變異尺度因子,將果蠅的固定搜索步長方式改為逐步遞減的搜索步長,以達(dá)到平衡算法全局優(yōu)化和局部優(yōu)化的目的。利用多個(gè)測試函數(shù)對改進(jìn)的果蠅優(yōu)化算法進(jìn)行測試,并與標(biāo)準(zhǔn)果蠅算法以及差分進(jìn)化算法結(jié)果進(jìn)行了比較,結(jié)果表明改進(jìn)的果蠅優(yōu)化算法具有尋優(yōu)快,優(yōu)化精度高,不易早熟收斂的優(yōu)點(diǎn)。在此基礎(chǔ)上,結(jié)合大地電磁反演理論,利用改進(jìn)果蠅優(yōu)化算法對大地電磁一維模型進(jìn)行反演,并利用不同噪聲水平的模型對算法進(jìn)行了測試,結(jié)果表明改進(jìn)的果蠅優(yōu)化算法能有效的處理大地電磁數(shù)據(jù),反演結(jié)果精度高,算法魯棒性好。本文還對基于貝葉斯理論的統(tǒng)計(jì)反演方法進(jìn)行了研究,對非線性貝葉斯反演的基本原理、目前常見的非線性數(shù)值采樣方法進(jìn)行了歸納和總結(jié)。貝葉斯反演理論將反演模型參數(shù)看成是隨機(jī)變量,反演的結(jié)果是統(tǒng)計(jì)意義上的后驗(yàn)概率分布,能直觀的對結(jié)果進(jìn)行評價(jià)�；谧兙S反演的思想,利用可逆跳躍馬爾科夫鏈蒙特卡洛方法對一維大地電磁數(shù)據(jù)進(jìn)行反演。貝葉斯反演結(jié)果基于大量樣本,因此采樣速度的快慢對算法具有重要影響,為了加快算法收斂速度,利用改進(jìn)的并行回火技術(shù),將副本之間的相鄰交換方式替換為隨機(jī)交換方式,使得算法能夠快速對整個(gè)空間進(jìn)行采樣,獲得解的大量樣本。結(jié)果表明變維反演能夠有效的對層狀介質(zhì)進(jìn)行自動分層,有效減少人為因素的干擾,并行回火技術(shù)能夠加速采樣過程收斂。
[Abstract]:As an important geophysical exploration method, magnetotelluric method has been widely used to study the geological structure of crust and upper mantle, as well as deep mineral exploration. As a bridge between geophysical observation and interpretation, the research of inversion method has always been a hot topic. In this paper, an intelligent optimization algorithm, Drosophila optimization algorithm, is introduced to magnetotelluric data inversion, which avoids the disadvantages of computing partial derivative matrix and dependence on initial model in order to avoid linearization iteration. Drosophila optimization algorithm has the advantages of simple principle, few control parameters and easy programming. Through the analysis of the standard Drosophila optimization algorithm, it is found that the algorithm has slow convergence in dealing with high-dimensional and multi-peak objective functions. It is easy to fall into local extremum, so it is improved by adding the crossover operation and mutation operation of differential evolution algorithm to increase the diversity of Drosophila population and improve the ability of global optimization. At the same time, the variation scale factor is used. In order to achieve the goal of global and local optimization, the fixed search step of Drosophila was changed to a progressively decreasing search step. The improved Drosophila optimization algorithm is tested by several test functions and compared with the results of the standard Drosophila algorithm and the differential evolutionary algorithm. The results show that the improved algorithm has the advantages of fast searching and high precision. The advantage of not being easy to converge prematurely. On this basis, combined with the magnetotelluric inversion theory, the improved Drosophila optimization algorithm is used to inverse the magnetotelluric one-dimensional model, and the algorithm is tested by the model with different noise levels. The results show that the improved algorithm can deal with magnetotelluric data effectively, and the inversion results are accurate and robust. The statistical inversion method based on Bayesian theory is also studied in this paper. The basic principle of nonlinear Bayesian inversion and the common nonlinear numerical sampling methods are summarized and summarized. Bayesian inversion theory regards the parameters of the inversion model as random variables, and the result of inversion is a statistical posteriori probability distribution, which can directly evaluate the results. Based on the idea of variable dimensional inversion, the reversible jump Markov chain Monte Carlo method is used to inverse the one-dimensional magnetotelluric data. The result of Bayesian inversion is based on a large number of samples, so the speed of sampling has an important effect on the algorithm. In order to speed up the convergence of the algorithm, an improved parallel tempering technique is used. By replacing the adjacent exchange between replicas with random switching, the algorithm can quickly sample the whole space and obtain a large number of samples of the solution. The results show that the variable dimension inversion can effectively delaminate the layered media automatically and reduce the interference of human factors effectively. The parallel tempering technique can accelerate the convergence of the sampling process.
【學(xué)位授予單位】：中國地質(zhì)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：P631.325

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