What question did this study set out to answer?

The aim is to enhance earthquake detection accuracy while maintaining interpretability in an automated system.

March 28, 2026Open Access

A Bi-LSTM Attention Mechanism for Monitoring Seismic Events—Solving the Issue of Noise & Interpretability

Key Points

The aim is to enhance earthquake detection accuracy while maintaining interpretability in an automated system.
Developed a Bidirectional Long Short-Memory network with attention mechanism (Bi-LSTM-Attn).
Implemented a comprehensive preprocessing pipeline including signal filtering, normalization, and noise reduction.
Optimized model parameters through hyperparameter tuning.
Evaluated model performance using accuracy, precision, recall, F1-score, and MSE.
Bi-LSTM-Attn achieved higher accuracy in true-positive detections compared to baseline LSTM and GRU models.
Demonstrated fewer false alarms while maintaining high precision and recall.
Provided interpretable results through attention weight visualization.

Abstract

The nonlinearity and the extreme variability of seismic signals makes the detection of earthquakes difficult. Although the conventional deep-learning models can be used to extract useful features, they cannot be used in early-warning systems due to their non-interpretability. In this study, a Bidirectional Long Short-Memory network with an attention system (Bi-LSTM-Attn) is proposed to detect seismic events using the ConvNetQuake dataset. To improve the quality of data, the entire preprocessing pipeline, such as signal filtering, segmentation, normalization, and noise reduction is employed. The model was optimized using hyperparameter tuning of sequence length, learning rates, and attention weighting to achieve the best number of true-positive detections and a minimum number of false alarms. The accuracy, precision and recall, F1-score, MSE, and ROC curves were used to assess the performance and the attention weight visualization allowed interpreting the model. It is proven through experiments that the Bi-LSTM-Attn model provides more credible and comprehensible forecasting in relation to baseline LSTM and GRU models. Making the high-accuracy classification and the transparent decision behavior, the approach will increase the level of trust to the automated seismic surveillance, as well as help to build the reliable global networks of earthquake early-warnings.

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