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Particle Swarm Impedance Inversion Method Research and Appicatin

Author: WangLi
Tutor: WangShanShan
School: Chengdu University of Technology
Course: Earth Exploration and Information Technology
Keywords: Wave impedance inversion Particle Swarm Optimization Layered constraints
CLC: P631.44
Type: Master's thesis
Year: 2011
Downloads: 73
Quote: 0
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Abstract


Oil and gas resources is still the coming decades is difficult to replace energy sources, with the oil and gas exploration technology continues to evolve, the type is relatively simple to construct oil and gas reservoir development has almost exhausted, how to find lithologic reservoirs will become The main research directions. Seismic exploration is an important means to find and discriminant lithologic reservoirs, seismic wave impedance inversion contribute to reservoir description and post-seismic data interpretation, it can truly reflect the underground rock formations and geological structure, to provide a reliable drilling basis. Many of the problems in the actual project, by its very nature can be transformed into optimization problems, seismic wave impedance inversion will be part of a multi-parameter nonlinear optimization problems, its main purpose is to obtain the reflection coefficient of the subsurface, and then get the density, velocity and other related parameters. In recent years, the rapid development of a variety of non-linear method, with each passing day, from early Newton method, the gradient method and Monte Carlo method and the traditional non-linear inversion method, to the subsequent emergence of the genetic algorithm, simulated annealing, ant colony algorithm and heuristic inversion method of artificial neural network method for seismic inversion, it is these new approaches are emerging hope and the dawn. Particle swarm optimization algorithm as a new efficient algorithm, has aroused wide attention from scholars at home and abroad. The algorithm is derived from research on the birds of motor behavior, the advantage of the kind proposed by the United States electrical engineer of Eberhart and social psychologist Kennedy computing technology, based on swarm intelligence evolution particle swarm optimization algorithm virtue of the principle is simple and easy to implement, has gradually become a hot research topic. At present, the particle swarm optimization algorithm has been applied to the neural network training function optimization, fuzzy system control and many other fields and achieved good results. This paper first introduces the topics in accordance with the seismic inversion and wave impedance inversion in the overview of the development at home and abroad, explained briefly the theoretical basis and research status of the particle swarm optimization algorithm, the principle of the particle swarm optimization algorithm and processes. Particle swarm optimization, parameter settings will affect the results of the optimization algorithm, therefore, how to choose the appropriate parameters in order to obtain a satisfactory solution is one of the algorithm needs to overcome the problem. In this paper, the function of the number of different dimensions trial carried out a detailed study on particle swarm optimization algorithm parameter selection, discussed the impact of the various parameters of the algorithm and selected rules of thumb, particle swarm optimization algorithm parameter selection theoretically guidance and reference. In a large number of read and fully understood the Particle Swarm Optimization algorithm theory based on particle swarm optimization algorithm is applied to the seismic wave impedance inversion, and for routine particle swarm optimization algorithm late slow convergence, inversion accuracy drawback. a layered model under the constraint of particle swarm optimization algorithm. The basic idea of ??the method is the seismic records each corresponding to underground structures as the layered wave impedance model, adjust the number of layers of the model and the number of samples of each layer to achieve a layered constraint. The spreadsheet results show that the layered model under the constraint of particle swarm optimization algorithm can effectively overcome the conventional particle swarm optimization algorithm inversion results of random fluctuations, accelerate the speed of convergence of the algorithm inversion efficiency. Layered model under the constraint of particle swarm optimization velocity inversion and wave impedance inversion, two-dimensional theoretical model and real seismic data proved the practicality and effectiveness of the algorithm.

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CLC: > Astronomy,Earth Sciences > Geology > Geology, mineral prospecting and exploration > Geophysical exploration > Seismic exploration >
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