Research And Application Of High-Resolution Seismic Wave Impedance Inversion Based On Intelligent Computation In Coalfield

The focus of coal production in our country is shifting from east to west, for the west occupies most of coal resources. Compared to the east, in the west the formation of the coal field is simple, but the fabric of coal seam is much complicated leading to difficult comparison interpretation. So it is necessary to realize lithology interpretation from structure interpretation. Wave impedance inversion is a key method to achieve lithology interpretation and it plays a key role in geophysical prospecting. Wave impedance inversion belongs to nonlinear optimization. However, conventional inversion methods such as generalized linear inversion and so on are based on linear theory or quasi-linear theory, which results in sensitive to initial model and local extreme and multi-extreme. With the development of computer technology and the introduction of new leading subjects, new methods based on intelligent computation are becoming the focus in geophysical domain. In order to improve premature convergence and reduce calculation cost of the current commonly used intelligent inversion methods,in this dissertation hybrid intelligent algorithms and novel intelligent computation methods are introduced and to be applied to wave impedance inversion. In this paper, the main research works and the achievement obtained include the following contents.(1) GA-BP optimization algorithm was proposed to reconstruct sonic logging curve in coal field. Combining density and resistance, GA-BP algorithm improved the accuracy of reconstruction of the sonic logging curve, which provided reliable data for inversion.(2) The object function with restriction in wave impedance inversion was constructed. To improve the effect of inversion under the object function without restriction, sonic logging information and the relation between the wave impedance difference in the boundary and the amplitude were added to the object function. So the inverted impedance was limited in a certain range and the convergence speed was greatly increased. Considered the characteristic of wave impedance inversion the encoding and fitness function was also discussed.(3) Improved GASA optimization method was proposed by combing modified genetic operators and simulated annealing. According to the characteristic of wave impedance inversion, decimal encoding system was adopted to avoid the transformation from binary system to decimal system and speed the convergence. Fitness function was defined and drawn by simulated annealing mechanism. Computation results showed that without noise the inverted results were consistent to the real model and with the increase of noise and the inverted parameters the precision of inversion were decreased, but the precision were better than GA and SA.(4) Improved hybrid particle swarm optimization (PSO) algorithm was proposed. To avoid the premature convergence of particles and slow convergence in the late process, the immune memory idea and the selection strategy based on antibody density were introduced into PSO and the proposed two-stage search strategy took the global search ability and the local search ability into account. Furthermore, the proposed cloning selection operator accelerated the best particle away from the local extreme and Logistic sequence was adopted to extend the search scope and further the best particle mutation. The simulation results indicated that the improved hybrid PSO had better efficiency and higher accuracy. The theoretical model of two thin coal seam with 40 meters interval was constructed and the inverted results showed that the thickness of less 2m coal seam was identified.(5) Based on immune optimization operators such as immune selection, immune mutation, population recombination and chaos multiplication, an improved immune genetic algorithm was proposed. According to the wave impedance inversion, the size of population was dynamic adjusted to improve population diversity and global convergence. The simulation results indicated that the improved immune genetic algorithm had a better efficiency and a little higher accuracy than immune hybrid PSO algorithm. The theoretical model of three thin coal seam with intervals less than wave length was constructed and the inverted results showed that the thickness of 2m, 3m and 5m coal seams were identified.Finally, the proposed immune hybrid PSO and immune genetic algorithm were applied to Yangquan and Chengzhuang coalfield. The inversion results showed that the inversion resolution were obviously higher than the seismic resolution, the continuity and the detection capability of weak reflection were improved greatly, which provided an effective method for study lower group coal and lithology interpretation.