Treffer: Enhanced Sliding-window Deep Learning for Earthquake Magnitude Prediction: A Multi-regional Study on USGS Data from Java–Bali, Iran, and Chile (1970– 2020).

Data-driven earthquake magnitude prediction models typically rely on sliding-window representations of seismic catalogs, yet the choice of window length and stride is often made heuristically. This paper investigates how an enhanced sliding-window construction and its systematic optimisation affect next-event magnitude prediction across three tectonically distinct regions. We compile multi-decadal earthquake catalogs from the USGS ComCat for Java-Bali, Chile, and Iran (1970-2020) and construct supervised samples by segmenting each regional catalog into overlapping sequences of the last L events. We then treat the window length L and stride S as explicit hyperparameters and select an enhanced configuration (L*, S*) on the Java-Bali validation set using a compact model. On top of this representation, we evaluate BiLSTM-only and CNN-BiLSTM architectures alongside naive (mean and persistence) and window-based deep learning baselines. In the Java-Bali region, the enhanced sliding-window BiLSTM achieves the lowest test errors (MAE ≈ 0.47, RMSE ≈ 0.62), outperforming fixed-window and short-history baselines and indicating that carefully tuned temporal representation can be as important as architectural complexity. Cross-regional experiments, in which a model trained on Java-Bali is transferred without fine-tuning to Chile and Iran, reveal higher errors and occasionally negative R² values, reflecting the difficulty of next-event magnitude prediction under strong domain shift. Nevertheless, the enhanced representation typically performs competitively with, and often better than, naive and fixed-window baselines. Overall, our results highlight the critical role of sliding-window design in catalog-based magnitude prediction and provide a multi-regional benchmark for future work on domain-adapted and probabilistic deep learning models. [ABSTRACT FROM AUTHOR]

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