Seismic event classification with a lightweight Fourier Neural Operator model

TL;DR

This paper introduces a lightweight Fourier Neural Operator model for real-time seismic event classification, achieving high accuracy with reduced computational resources suitable for deployment in resource-constrained environments.

Contribution

The paper presents a novel FNO-based model that is computationally efficient and outperforms traditional deep learning methods in seismic event classification tasks.

Findings

FNO model achieves 95% F1 score on STEAD dataset.

FNO model attains 98% F1 score on real microseismic data.

Significantly reduces computational cost compared to existing deep learning models.

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 microseismic dataset shows a classification success rate with an F1 score of 98%, outperforming many traditional deep-learning techniques. The reduced computational cost makes the proposed FNO model well suited for deployment in resource-constrained, near-real-time seismic monitoring workflows, including traffic-light implementations. The source code for the proposed FNO classifier will be available at: https://github.com/ayratabd/FNOclass.