Advanced Seismic Magnitude Classification Through Convolutional and Reinforcement Learning Techniques
Earthquake Early Warning (EEW) systems are crucial in reducing the dangers associated with earthquakes. This paper delves into the realm of EEWs, focusing on rapidly determining earthquake magnitudes (EMs). Traditional methods for swift magnitude categorization often grapple with challenges such as...
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| Published in: | International journal of advanced computer science & applications Vol. 14; no. 11 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
West Yorkshire
Science and Information (SAI) Organization Limited
2023
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| Subjects: | |
| ISSN: | 2158-107X, 2156-5570 |
| Online Access: | Get full text |
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| Summary: | Earthquake Early Warning (EEW) systems are crucial in reducing the dangers associated with earthquakes. This paper delves into the realm of EEWs, focusing on rapidly determining earthquake magnitudes (EMs). Traditional methods for swift magnitude categorization often grapple with challenges such as data disparity and cumbersome processes. Our research introduces an innovative EEW model, employing a 7-second seismic waveform record from three different components provided by the China Earthquake Network Center (CENC). This empirical, quantitative study pioneers a method combining dilated convolutional techniques with a novel mutual learning-based artificial bee colony (ML-ABC) algorithm and reinforcement learning (RL) for EM classification. The proposed model utilizes an ensemble of convolutional neural networks (CNNs) to simultaneously extract feature vectors from input images, which are then amalgamated for classification. To address the imbalances in the dataset, we implement an RL-based algorithm, conceptualizing the training process as a series of decisions with individual samples representing distinct states. Within this framework, the network operates as an agent, receiving rewards or penalties based on its precision in distinguishing between the minority and majority classes. A key innovation in our approach is the initial weight pre-training using the ML-ABC method. This technique dynamically optimizes the "food source" for candidates, integrating mutual learning elements related to the initial weights. Extensive experiments were carried out on the selected dataset to ascertain the most effective parameter values, including the reward function. The findings demonstrate the superiority of our proposed model over other evaluated methods, highlighting its potential as a robust tool for EM classification in seismology. This research provides valuable insights for both seismologists and developers of EEW systems, offering a novel, efficient approach to earthquake magnitude determination. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 2158-107X 2156-5570 |
| DOI: | 10.14569/IJACSA.2023.0141118 |