Abstract
Regularization inversion uses constraints and a regularization factor to solve ill-posed inversion problems in geophysics. The choice of the regularization factor and of the initial model is critical in regularization inversion. To deal with these problems, we propose a multiobjective particle swarm inversion (MOPSOI) algorithm to simultaneously minimize the data misfit and model constraints, and obtain a multiobjective inversion solution set without the gradient information of the objective function and the regularization factor. We then choose the optimum solution from the solution set based on the trade-off between data misfit and constraints that substitute for the regularization factor. The inversion of synthetic two-dimensional magnetic data suggests that the MOPSOI algorithm can obtain as many feasible solutions as possible; thus, deeper insights of the inversion process can be gained and more reasonable solutions can be obtained by balancing the data misfit and constraints. The proposed MOPSOI algorithm can deal with the problems of choosing the right regularization factor and the initial model.
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This work was supported by the Natural Science Foundation of China (No. 61273179) and Department of Education, Science and Technology Research Project of Hubei Province of China (No. D20131206, No. 20141304)
Xiong Jie received his PhD in Geophysics and Information Technology from the China University of Geosciences (Beijing) in 2012. He is currently an Associate Professor at Yangtze University. His research interests are geophysical inversion theory, applied geophysics, and computer applications.
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Xiong, J., Zhang, T. Multiobjective particle swarm inversion algorithm for two-dimensional magnetic data. Appl. Geophys. 12, 127–136 (2015). https://doi.org/10.1007/s11770-015-0486-0
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DOI: https://doi.org/10.1007/s11770-015-0486-0