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[2512.07425] Seismic event classification with a lightweight Fourier Neural Operator model
Summary
This article presents a lightweight Fourier Neural Operator model for real-time seismic event classification, demonstrating high accuracy and reduced computational requirements.
Why It Matters
The ability to classify seismic events accurately and quickly is crucial for mitigating risks in various industries. This model addresses the computational limitations of existing deep learning approaches, making it suitable for real-time applications in resource-constrained environments.
Key Takeaways
- The proposed Fourier Neural Operator model achieves an F1 score of 95% in classifying microseismic events.
- It significantly reduces computational power requirements compared to traditional deep learning models.
- The model is effective even with sparse training data, showcasing its robustness.
- Real-world testing yielded an F1 score of 98%, outperforming many existing techniques.
- The source code will be made publicly available for further research and application.
Physics > Geophysics arXiv:2512.07425 (physics) [Submitted on 8 Dec 2025 (v1), last revised 16 Feb 2026 (this version, v2)] Title:Seismic event classification with a lightweight Fourier Neural Operator model Authors:Ayrat Abdullin, Umair bin Waheed, Leo Eisner, Abdullatif Al-Shuhail View a PDF of the paper titled Seismic event classification with a lightweight Fourier Neural Operator model, by Ayrat Abdullin and 3 other authors View PDF HTML (experimental) Abstract:Real-time monitoring of induced seismicity is critical to mitigate operational risks, relying on the rapid and accurate classification of triggered data from continuous data streams. Deep learning models are effective for this purpose but require substantial computational resources, making real-time processing difficult. To address this limitation, a lightweight model based on the Fourier Neural Operator (FNO) is proposed for the classification of microseismic events, leveraging its inherent resolution-invariance and computational efficiency for waveform processing. In the STanford EArthquake Dataset (STEAD), a global and large-scale database of seismic waveforms, the FNO-based model demonstrates high effectiveness for trigger classification, with an F1 score of 95% even in the scenario of data sparsity in training. The new FNO model greatly decreases the computer power needed relative to current deep learning models without sacrificing the classification success rate measured by the F1 score. A test on a real m...
