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A multi-class anomaly signal classification method for fixed-point deformation combining PSO–VMD multiscale adaptive sample entropy and ALO–SOM model

Cong Pang1,2 ,  Tianwen Zhao3 ,  Guoqing Chen4,5 ,  Wugang Ma1 ,  Chawei Li1 ,  Yong Jiang1 ,  Piyapatr Busababodhin5*
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1 Key Laboratory of Earthquake Geodesy, Institute of Seismology, China Earthquake Administration, Wuhan, Hubei , China
2 Wuhan Gravitation and Solid Earth Tides, National Observation and Research Station, Wuhan, Hubei , China
3 Department of Trade and Logistics, Daegu Catholic University, Gyeongsan, Daegu , Republic of Korea
4 Mathematical Modeling Research Center, Chengdu Jincheng College, Chengdu, Sichuan , China
5 Department of Mathematics, Faculty of Science, Mahasarakham University, Kantharawichai, Maha Sarakham , Thailand
JSE, 026290141 https://doi.org/10.36922/JSE026290141
Received: 16 July 2026 | Revised: 30 September 2026 | Accepted: 1 October 2026 | Published online: 4 November 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

To address the challenge of identifying five predefined categories of disturbance-related abnormal signals (lightning interference, artificial blasting, power-supply failure, collapse-related events, and meteorological anomalies) in fixed-point deformation monitoring, this paper proposes an identification framework integrating parameter-optimized variational mode decomposition (VMD), multiscale entropy analysis, and an intelligent classification model. Firstly, particle swarm optimization is adopted to adaptively determine the mode number and penalty factor for VMD to realize adaptive signal decomposition. Then, an adaptive sample entropy algorithm with self-adjustable similarity thresholds is applied to extract multiscale complexity-oriented features from each decomposed modal component. Finally, the ant lion optimizer optimizes the competitive-layer topology and training hyperparameters of a self-organizing map network to build the target classifier. Comparative experiments against 18 widely-used classification models (including AdaBoost ensemble learners, generalized regression neural networks, long short-term memory networks, and gated recurrent units) are carried out on the real-world observation dataset from the Yixian seismic station. The experimental results show that the proposed framework achieves competitive classification performance for the five anomaly categories. The mean test-set accuracy over 100 independent repeated-run experiments reaches 99.62%, with a standard deviation of 0.6361. This study offers an effective technical reference for identifying deformation-related abnormal waveforms under complex observational conditions.

Keywords
Fixed-point deformation multi-class anomaly classification
Self-organizing feature mapping
Ant lion optimization
Variational mode decomposition
Adaptive sample entropy
Particle swarm optimization
Funding
This research was financially supported by Mahasarakham University; Hubei Provincial Natural Science Foundation of China (No.2026AFB752;2025AFB834); Open Fund of Wuhan Gravitation and Solid Earth Tides, National Observation and Research Station, No. WHYWZ202406; The Spark Program of Earthquake Technology of CEA, No.XH25019C; Earthquake Monitoring and Early Warning Tasks, No.CEA-JCYJ-202601025; Hubei Provincial Program for Science and Technology Innovation Platforms, No.2026CFC002; Research grants from the National Institute of Natural Hazards, Ministry of Emergency Management of China (Grant Number: ZDJZDJ2025-58); 2025 Doctoral Special Support Program Project of Chengdu Jincheng College (NO.2025JCKY(B)0018).
Conflict of interest
The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.
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