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Earthquake and blast recognition based on CEEMDAN multiscale fuzzy entropy and NSGAIII optimized 1D-CNN neural networks

Cong Pang1,2 ,  Tianwen Zhao3 ,  Guoqing Chen4,5 ,  Chawei Li1,2 ,  Zhongya Li1,2 ,  Piyapatr Busababodhin5,6 ,  Pornntiwa Pawara7*
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1 Institute of Seismology Earthquake Administration, Wuhan, Hubei, China
2 Fund of 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
6 The Digital Innovation Research Cluster for Integrated Disaster Management in the Watershed, Mahasarakham University, Kantharawichai, Maha Sarakham, Thailand
7 Department of Computer Science, Faculty of Informatics, Mahasarakham University, Kantarawichai, Maha Sarakham, Thailand
JSE 2025, 34(1), 025260029 https://doi.org/10.36922/JSE025260029
Submitted: 28 June 2025 | Revised: 20 July 2025 | Accepted: 25 July 2025 | Published: 31 July 2025
© 2025 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

This study proposes an enhanced method for natural earthquake and artificial explosion recognition, which comprises two parts, namely the multiscale fuzzy entropy (MFE) feature extraction of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the non-dominated sorting genetic algorithm III (NSGAIII) optimization of the one-dimensional convolutional neural network (1D-CNN). CEEMDAN decomposes earthquake signals into initial functions (intrinsic mode functions) and extracts fuzzy entropy features to construct a discriminative time-frequency representation. The hyperparameters of 1D-CNN (minimum batch size, initial learning rate, and learning rate drop factor) were optimized by NSGAIII, using a dual objective function to minimize mean squared error and maximize R2. Tests on 1000 earthquake events (883 earthquakes and 117 explosions) showed that the model has an accuracy of 97.82%, which is better than traditional networks (1D-CNN, generalized regression neural network, probabilistic neural network, back propagation neural network, and radial basis function neural network) and has better regression indicators (mean absolute error = 0.0795, root mean squared error = 0.1302, R2 = 0.7361). The Adam optimization algorithm achieved peak performance (99.50%), significantly surpassing SGD-M and RMSprop. This framework effectively solves the small sample and high-dimensional classification problems in earthquake monitoring and improves the automatic event detection capability of the early warning system.

Keywords
Seismic wave recognition
Multiscale fuzzy entropy
Complete ensemble empirical mode decomposition with adaptive noise
One-dimensional convolutional neural network
Third-generation non-dominated sorting genetic algorithm
Funding
This research was financially supported by Mahasarakham University; Open Fund of Wuhan Gravitation and Solid Earth Tides, National Observation and Research Station, (No.WHYWZ202406, WHYWZ202208); Scientific Research Fund of Institute of Seismology, China Earthquake Administration and National Institute of Natural Hazards, MEM, (No. IS202236328, IS202436357); The Spark Program of Earthquake Technology of CEA, (No. XH24025YC); Earthquake Monitoring and Forecasting and Early Warning Tasks for 2025, (No. CEA-JCYJ-202502015); Chengdu Jincheng College Green Data Integration Intelligence Research and Innovation Project (No. 2025-2027); and the High-Quality Development Research Center Project in the Tuojiang River Basin (No. TJGZL2024-07).
Conflict of interest
The authors declare they have no competing interests.
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Journal of Seismic Exploration, Print ISSN: 0963-0651, Published by AccScience Publishing
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