【摘要】：電法勘探作為傳統(tǒng)物探方法,在地質勘探、地下水勘察、油氣開采以及金屬探礦等領域都普遍應用。阻率層析成像(Electrical Resistance Tomography,簡稱ERT)是一種典型的電法勘探手段,以其場源實現簡單、實施方便、分辨率高、經濟實惠、穿透深度大等優(yōu)點等成為物探技術的前沿課題之一,具有廣泛的應用前景。由于電阻率剖面數據的計算精度和有效解釋是其應用的重要指標,ERT電阻率數據反演解釋是處理實際資料的主要手段,因此ERT數據的反演也一直是國內外專家學者廣泛研究的焦點。本文在改進粒子群優(yōu)化算法(Particle Swarm Optimization,簡稱PSO)對二維ERT模擬數據的非線性反演方面,開展了全面細致的研究工作,主要包括:(1)介紹ERT的發(fā)展以及反演現狀,闡述了PSO算法的基本原理,總結相關改進方向。針對其全局搜索后期收斂慢,易陷入局部極值的缺點,提出本文改進PSO算法的方向——在各影響粒子群速度更新的參數中引入混沌搜索,并用典型多峰值函數測試標準粒子群算法、阻尼粒子群算法算法和本文的改進粒子群算法,實驗結果表明,改進后的算法在搜索精度、收斂時間以及穩(wěn)定性方面都優(yōu)于標準PSO算法和阻尼PSO算法,在保證跳出局部極值的同時,提高了收斂速度。(2)詳細推導了采用非結構三角形單元時,用有限元法離散微分方程,形成正演矩陣,求得波數域結果,經過傅里葉逆變換轉換到空間域,完成正演研究。并在Matlab中編制正演FEM數值模擬程序,經過模型驗證,對比解析解,證明FEM是一種較好的正演模擬方法。(3)闡述二維ERT反演理論,對比線性舍棄高次項的處理方法,推導出非線性反演的最小化目標函數,設計直流電阻率模型、時域激發(fā)極化模型和頻域激發(fā)極化復電阻率正演模型,然后分別在加入一定程度噪聲的三種正演模型數據上進行模擬反演,并將其性能與標準PSO算法和奧卡姆算法進行對比,模型實驗表明,該方法不容易陷于局部最優(yōu),數值穩(wěn)定性好,具有較快的收斂速度,具有較高的性能。
[Abstract]:As a traditional geophysical method, electrical exploration is widely used in geological exploration, groundwater exploration, oil and gas exploration and metal prospecting. Resistive rate tomography (Electrical Resistance Tomography,) is a typical method of electrical exploration. Because of its advantages of simple field source, convenient implementation, high resolution, economical benefit, high penetration depth and so on, it has become one of the frontier topics of geophysical exploration technology. It has wide application prospect. Because the calculation accuracy and effective interpretation of resistivity profile data are important indexes for its application, inversion interpretation of ERT resistivity data is the main means to process the actual data. Therefore, the inversion of ERT data has always been the focus of extensive research by experts and scholars at home and abroad. In this paper, a comprehensive and detailed study on nonlinear inversion of two-dimensional ERT simulation data based on improved particle swarm optimization (Particle Swarm Optimization,) algorithm called PSO is carried out, including: (1) introducing the development of ERT and the present situation of inversion. The basic principle of PSO algorithm is expounded, and the improvement direction is summarized. In view of the disadvantages of slow convergence and easy to fall into local extremum in the late stage of global search, this paper presents the direction of improved PSO algorithm, which is to introduce chaos search into the parameters that affect the velocity update of particle swarm. The standard particle swarm optimization algorithm, damping particle swarm optimization algorithm and the improved particle swarm optimization algorithm of this paper are tested with typical multi-peak function. The experimental results show that the improved algorithm is accurate in searching. The convergence time and stability are better than the standard PSO algorithm and the damped PSO algorithm. The convergence rate is improved while the local extremum is removed. (2) the finite element method is used to discretize the differential equations when the unstructured triangular element is used. The forward modeling matrix is formed, the wavenumber domain results are obtained, and the forward modeling is completed by the inverse Fourier transform to the spatial domain. The forward FEM numerical simulation program is compiled in Matlab. After model verification and analysis, it is proved that FEM is a good forward modeling method. (3) the theory of two-dimensional ERT inversion is expounded, and the processing method of linear renunciation of higher term is compared. The minimization objective function of nonlinear inversion is derived. The DC resistivity model, the time-domain induced polarization model and the frequency-domain induced polarization complex resistivity model are designed. Then the simulation inversion is carried out on the three kinds of forward modeling data with certain noise, and its performance is compared with the standard PSO algorithm and Occam algorithm. The model experiment shows that the method is not easy to be trapped in local optimum. It has good numerical stability, fast convergence speed and high performance.
【學位授予單位】：湖南師范大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：P631.322

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本文編號：2270972