The Potential of Spiking Neural Networks in Predicting Earthquakes in New Zealand
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Tuwhera
Abstract
This study investigates the use of Spiking Neural Networks (SNNs) in earthquake prediction, focusing on New Zealand, a seismically active region. Traditional earthquake prediction methods struggle with accuracy and real-time warning capabilities. SNNs, inspired by the brain’s biological processes, excel at handling dynamic time-series data, making them a promising tool for tasks involving spatio-temporal patterns such as seismic waveforms. Utilizing the NeuCube platform([1]), we processed seismic data from 56 stations across New Zealand in 2022. Our model achieved an accuracy increase from 38% to 70% as the seismic event approached, highlighting its potential for real-time earthquake monitoring. Although further optimization is required, this research demonstrates SNNs' potential in improving early earthquake warning systems. Future work will focus on refining the model's architecture and incorporating multimodal data to enhance prediction accuracy and applicability.Description
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Wang, Z., & Doborjeh, M. (2025, March 17). The potential of Spiking Neural Networks in predicting earthquakes in New Zealand. 31th International Conference on Neural Information Processing (ICONIP) Abstracts. Presented at the 31st International Conference on Neural Information Processing. doi:10.24135/iconip20
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Copyright (c) 2025 The Authors(s). Creative Commons License. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.