Efficient Filter Generation Based on Particle Swarm Optimization Algorithm

Liang Zeng; Jintai Li; Jianxin Liu; Rongwen Guo; Hang Chen; Rong Liu

doi:10.1109/ACCESS.2021.3056464

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Efficient Filter Generation Based on Particle Swarm Optimization Algorithm

Journal article

Open access

Peer reviewed

Efficient Filter Generation Based on Particle Swarm Optimization Algorithm

Liang Zeng, Jintai Li, Jianxin Liu, Rongwen Guo, Hang Chen and Rong Liu

IEEE access, Vol.9, pp.22816-22823

2021

DOI: 10.1109/ACCESS.2021.3056464

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Abstract

The evaluation of Hankel integration is an important part in the interpretation of electromagnetic (EM) data, especially in physical and geophysical applications. The digital linear filter (DLF) method is commonly applied. For an optimal digital filter design based on matrix inversion, it requires optimization over the model space of the spacing and shift. This is typically obtained by a grid search algorithm on gradually refined grids. In this paper, we apply a particle swarm algorithm to optimize the spacing and shift in the model space. In this algorithm, we use a group of particles to search the model space and do not have to grid the model space sequentially to finer meshes. It has been applied to search the optimal spacing and shift for analytical function pairs, indicating a fast convergence. The performance of the obtained 201-point filter is examined and compared with other published filters based on analytic function pairs and controlled source electromagnetic (CSEM) applications. The results indicate that the proposed 201-point filters have a good numerical performance in terms of accuracy over a large offset range.

Convolution

digital filters

Electromagnetics

Hankel transform

Kernel

Mathematical model

Optimization

Particle swarm optimization

Transforms

Details

Title: Subtitle: Efficient Filter Generation Based on Particle Swarm Optimization Algorithm
Creators: Liang Zeng - Xiamen University
Jintai Li - Central South University
Jianxin Liu - Central South University
Rongwen Guo - Central South University
Hang Chen - Central South University
Rong Liu - Central South University
Resource Type: Journal article
Publication Details: IEEE access, Vol.9, pp.22816-22823
DOI: 10.1109/ACCESS.2021.3056464
ISSN: 2169-3536
eISSN: 2169-3536
Publisher: IEEE
Number of pages: 8
Grant note: 41904158; 42004065; 42074165 / National Natural Science Foundation of China (10.13039/501100001809)
Language: English
Date published: 2021
Academic Unit: Earth and Environmental Sciences
Record Identifier: 9984962626402771

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