The application of hybrid optimization algorithm in joint inversion of surface and borehole magnetic data

摘要

Global optimization algorithm has strong generality without the use of problem's specific information, which resulted in a waste of information on known issues. Local optimization algorithms has strong dependence of the problem, but the utilization of information on specific problems can be quickly constructed a solution, its time performance is more satisfactory. Hybrid optimization algorithm combines the advantages of the local optimization algorithm and global optimization algorithm and overcomes their shortcomings. It can not only increase the computing speed, but also have a very good effect on improving the quality of solution. Particle Swarm Optimization (PSO) is a new efficient and parallel optimization algorithm, this algorithm has a profound intelligence background, and is simple and easy to implement. This paper gives out the process of the hybrid optimization inversion which combined PSO algorithm with the singular value truncated method, and creates a vertical cuboid as the model, then uses this hybrid optimization method for the joint inversion of surface and borehole magnetic data in the theoretical model tests and the practical example of Daye exhausted mine. The result shows that the relative error of this hybrid optimization algorithm is smaller than the above two kinds of algorithms, and the computing time is between the two algorithms. In order to find the balance of time-consuming and precision, using hybrid algorithm is superior than using the two single algorithm.