Wave Impedance Inversion Based On Improved Firefly Algorithm

Oil and natural gas resources are still the most important energy sources that are difficult to replace at present.With the continuous development of oil and gas exploration and development technologies,the reserves of oil and gas reservoirs with relatively simple and easy-to-detect shallow structures are declining or even exhausted.In order to increase oil and gas production In addition to oil and gas reserves,the detection and excavation of deep complex oil and gas reservoirs has become the main research content for searching for oil and gas reservoirs.Seismic exploration is the main technical means to find and identify underground oil and gas reservoirs.Seismic impedance inversion is conducive to the description of underground geological bodies and the interpretation of indoor logging data.It can reflect the underground rock formations and geological structure to the greatest extent,and is related to logging research.Provide a reliable basis.Seismic exploration in actual engineering is mainly a geophysical inverse problem.Wave impedance inversion is one of the representative inverse problems.Its main purpose is to obtain the reflection coefficient of the underground medium,thereby obtaining density,velocity and other related parameters.Since the equation to be solved in impedance inversion is a multi-parameter nonlinear problem,using traditional linear methods to solve nonlinear problems often results in serious diversification.With the rapid development of non-linear algorithms,the continuous emergence of swarm heuristic optimization methods such as bat algorithm,artificial fish school method,genetic algorithm,etc.,guides new directions for seismic inversion.In recent years,with the development of the theory and application of artificial intelligence,various swarm intelligence optimization algorithms have gradually become a research hotspot for scientists.Firefly Algorithm(FA)is a heuristic swarm intelligence optimization algorithm proposed by imitating the lighting behavior of fireflies in nature.This algorithm uses fireflies with strong fluorescent brightness in the search space to attract other individuals to move to complete the location update,thereby achieving the purpose of optimization.It is another intelligent algorithm after particle swarm optimization,simulated annealing algorithm,and ant colony algorithm.New swarm intelligence optimization algorithm.Firefly algorithm(FA)is compared with classical deterministic optimization methods such as gradient algorithm(GA)and least square algorithm(LS).It does not require continuous and derivable conditions,and is simple to operate,simple to implement,and requires less adjustment parameters.The characteristics of high computational efficiency,strong practicability,and wide application range have attracted more and more researchers' attention,and are widely used in combinatorial optimization,engineering technology,and cluster analysis.This article introduces in detail the background basis of the topic selection,wave impedance inversion and the development of the firefly algorithm(FA)at home and abroad,briefly describes the theoretical basis and research status of the firefly algorithm(FA),and focuses on the analysis of the principle and the firefly algorithm(FA)Algorithm flow.Aiming at the problems of the basic firefly algorithm in the global optimization search process,such as low accuracy,slow convergence speed,and easy to fall into local extremes,this paper proposes a firefly algorithm(CDFA)based on the combination of chaotic search and dynamic step size.At the beginning of the search,the CDFA algorithm uses a bounded,random and ergodic chaotic sequence to set the initial position of the firefly population;a new type of hyperbolic decreasing dynamic step size is used to replace the traditional fixed step size,and the above improvement methods are adopted.,To improve the algorithm's solution accuracy,convergence speed,population distribution uniformity,and avoid repeated oscillations in the later stages of the iteration.Based on a lot of reading and a full understanding of the firefly algorithm theory,the CDFA algorithm is applied to the seismic data wave impedance inversion,and the feasibility and effectiveness of the CDFA algorithm is demonstrated through the simulation of the horizontal stratum theoretical model,and the use of different Intensity noise tests the anti-noise performance of the CDFA algorithm.The test results show that the CDFA algorithm has a good inversion effect and a certain degree of anti-noise ability.