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This research aims to enhance earthquake magnitude prediction accuracy using an end-to-end deep learning framework.

MP-Net: An end-to-end approach based on time-frequency fusion for earthquake magnitude prediction

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Randomized trial evaluates a deep learning framework for earthquake magnitude prediction, suggesting improved accuracy for early warning systems.

This research aims to enhance earthquake magnitude prediction accuracy using an end-to-end deep learning framework.
Developed MP-Net, which includes dual branches for time-domain waveforms and frequency-domain spectrograms.
Employed multi-scale convolutions and adaptive spectral attention mechanisms for feature extraction in both domains.
Implemented a cross-attention fusion module to integrate information from both modalities.
Mean absolute error decreased to 0.28 and R2 value reached 0.872.
82.5% of predictions achieved acceptable precision (error≤0.5).
Experimental results showed significant improvements over baseline models.

Wang et al. (2026) studied this question.

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Randomized trial evaluates a deep learning framework for earthquake magnitude prediction, suggesting improved accuracy for early warning systems.