|Table of Contents|

Quasi-2D Stochastic Inversion of Airbone Transient Eletromagnetic Data Based on Quantum-behaved Particle Swarm Optimization Algorithm(PDF)

《地球科学与环境学报》[ISSN:1672-6561/CN:61-1423/P]

Issue:
2020年第06期
Page:
722-730
Research Field:
电磁法勘探专辑
Publishing date:

Info

Title:
Quasi-2D Stochastic Inversion of Airbone Transient Eletromagnetic Data Based on Quantum-behaved Particle Swarm Optimization Algorithm
Author(s):
HE Yi-ming1; 2; 3;  XUE Guo-qiang1; 2; 3; 4*;  ZHAO Yang1; 2; 3
1. Key Laboratory of Mineral Resources, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China; 2. Innovation Academy of Earth Science, Chinese Academy of Sciences, Beijing 100029, China; 3. College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; 4. Integrated Geophysical Simulation Lab(Key Laboratory of Chinese Geophysical Society), Chang'an University, Xi'an 710054, Shaanxi, China
Keywords:
airborne transient eletromagnetic method;  particle swarm optimization algorithm;  group theory;  quantum theory;  quasi-2D inversion;  α-Trimmed method;  Occam inversion;  focusing inversion
PACS:
P631
DOI:
10.19814/j.jese.2020.09007
Abstract:
The traditional deterministic inversion algorithm relies heavily on the initial model, which is easy to fall into the local minimum and leads to the final inversion results seriously deviate from the real model. As a relatively new stochastic inversion algorithm, particle swarm optimization(PSO)algorithm has a strong ability to jump out of local minimum. However, there are still some problems such as premature convergence and slow convergence speed, which limit the development of algorithm for 2- or 3-dimensional electromagnetic inversion. In view of the above problems, quantum-behaved particle swarm optimization was proposed to replace the traditional particle swarm optimization, and the law of quantum motion in the potential well was introduced into the particle swarm optimization algorithm, so that the particles could appear in any position with probability distribution in the potential well, which could effectively overcome the premature convergence problem caused by the aggregation of population. In addition, the quasi-2D inversion algorithm was used to replace the traditional 2-dimensional inversion algorithm. As the parameter dimension of inversion model decreases, the number of local minima was greatly reduced, which significantly improves the convergence speed of particle swarm optimization. However, if the traditional regularization process is still used in the quasi-2D inversion of particle swarm optimization, the optimization of regularization parameters will waste a lot of computing resources. Combining with the important position of global optimal particles at each measuring point in quantum-behaved particle swarm optimization in the evolution of particle swarm, the α-Trimmed method was introduced to realize fast lateral constraint inversion by parameter smoothing constraint of global optimal particle model between adjacent points. Finally, the quasi-2D inversion technique based on quantum-behaved particle swarm optimization algorithm was applied to the simulation data of airborne transient electromagnetic method with noise, and the final inversion results were in good agreement with the forward model.

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Last Update: 2020-12-20