Jing Zhao
Seismic events extraction method based on the B-COSFIRE filter combined with the differential evolution algorithm.
Zhao, Jing; Li, Yang; Lei, Haojie; Ren, Jinchang; Zhang, Fuku; Shen, Hongyan
Authors
Yang Li
Haojie Lei
Professor Jinchang Ren j.ren@rgu.ac.uk
Professor of Computing Science
Fuku Zhang
Hongyan Shen
Abstract
Based on an analysis of the information processing mechanism in the primary visual cortex of biological vision, this study proposes an integration method of bar-combination of shifted filter responses (B-COSFIRE) filter with the differential evolution (DE) algorithm for enhancing the precision of events extraction. First, the B-COSFIRE filter incorporates trainable and unsupervised features, utilizing the two-dimensional expression of the difference-of-Gaussians (DoG) model to simulate the receptive field model. By capitalizing on the blur and shift properties of the DoG response, the proposed approach enhances the continuous effective signal while attenuating discontinuous noise signal, thereby demonstrating superior noise robustness compared to conventional methods. Second, the selectivity of proposed filter is not predefined during the implementation but automatically determined based on the given prototype pattern during the configuration process, resulting in a universal solution adaptable to various target patterns. Lastly, we employ the DE algorithm to optimize the feature selection process, enabling the extraction of a minimum feature subset that maximizes the performance of events characterization. The B-COSFIRE method is widely used in the field of image processing. When applying it to seismic exploration, the seismic data used by this algorithm is in 'sgy' format, providing richer information than traditional image data. The proposed model can effectively detect the event in seismic data with significant data volume and substantial noise interference. The B-COSFIRE filter method outperforms conventional edge detection techniques by accurately capturing seismic events of varying widths, aligning with the principles observed in biological vision mechanisms. The extracted events exhibit enhanced continuity and accuracy compared to existing approaches.
Citation
ZHAO, J., LI, Y., LEI, H., REN, J., ZHANG, F. and SHEN, H. 2024. Seismic events extraction method based on the B-COSFIRE filter combined with the differential evolution algorithm. ACTA geophysica [online], 72(4), pages 2447-2467. Available from: https://doi.org/10.1007/s11600-023-01222-1
Journal Article Type | Article |
---|---|
Acceptance Date | Aug 29, 2023 |
Online Publication Date | Nov 30, 2023 |
Publication Date | Aug 31, 2024 |
Deposit Date | Jan 9, 2024 |
Publicly Available Date | Dec 1, 2024 |
Journal | Acta geophysica |
Print ISSN | 1895-6572 |
Electronic ISSN | 1895-7455 |
Publisher | De Gruyter |
Peer Reviewed | Peer Reviewed |
Volume | 72 |
Issue | 4 |
Pages | 2447-2464 |
DOI | https://doi.org/10.1007/s11600-023-01222-1 |
Keywords | Events extraction; B-COSFIRE filter; The differential evolution algorithm; Biological vision; The DoG model |
Public URL | https://rgu-repository.worktribe.com/output/2174595 |
Files
ZHAO 2024 Seismic events extraction (AAM)
(5.2 Mb)
PDF
Copyright Statement
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11600-023-01222-1
You might also like
Two-click based fast small object annotation in remote sensing images.
(2024)
Journal Article
Prompting-to-distill semantic knowledge for few-shot learning.
(2024)
Journal Article
Detection-driven exposure-correction network for nighttime drone-view object detection.
(2024)
Journal Article
Feature aggregation and region-aware learning for detection of splicing forgery.
(2024)
Journal Article
Downloadable Citations
About OpenAIR@RGU
Administrator e-mail: publications@rgu.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search