Earthquake prediction and seismic hazard assessment remain fundamental challenges in geophysics, with existing machine learning approaches often operating as black boxes that ignore established physical laws. We introduce POSEIDON (Physics-Optimized Seismic Energy Inference and Detection Operating Network), a physics-informed energy-based model for unified multi-task seismic event prediction, alongside the Poseidon dataset -- the largest open-source global earthquake catalog comprising 2.8 million events spanning 30 years. POSEIDON embeds fundamental seismological principles, including the Gutenberg-Richter magnitude-frequency relationship and Omori-Utsu aftershock decay law, as learnable constraints within an energy-based modeling framework. The architecture simultaneously addresses three interconnected prediction tasks: aftershock sequence identification, tsunami generation potential, and foreshock detection. Extensive experiments demonstrate that POSEIDON achieves state-of-the-art performance across all tasks, outperforming gradient boosting, random forest, and CNN baselines with the highest average F1 score among all compared methods. Crucially, the learned physics parameters converge to scientifically interpretable values -- Gutenberg-Richter b-value of 0.752 and Omori-Utsu parameters p=0.835, c=0.1948 days -- falling within established seismological ranges while enhancing rather than compromising predictive accuracy. The Poseidon dataset is publicly available at https://huggingface.co/datasets/BorisKriuk/Poseidon, providing pre-computed energy features, spatial grid indices, and standardized quality metrics to advance physics-informed seismic research.

翻译：地震预测与地震危险性评估仍是地球物理学中的基础性挑战，现有机器学习方法常作为忽略既定物理定律的黑箱运行。我们提出了POSEIDON（物理优化的地震能量推断与检测操作网络），一种基于物理信息的能量模型，用于统一的多任务地震事件预测，同时发布了Poseidon数据集——最大的开源全球地震目录，包含跨越30年的280万次事件。POSEIDON将基本地震学原理，包括古登堡-里克特震级-频度关系和Omori-Utsu余震衰减定律，作为可学习的约束嵌入到基于能量的建模框架中。该架构同时处理三个相互关联的预测任务：余震序列识别、海啸生成潜力和前震检测。大量实验表明，POSEIDON在所有任务上均实现了最先进的性能，优于梯度提升、随机森林和CNN基线，在所有对比方法中获得了最高的平均F1分数。至关重要的是，学习到的物理参数收敛于科学上可解释的数值——古登堡-里克特b值为0.752，Omori-Utsu参数p=0.835，c=0.1948天——这些值落在既定的地震学范围内，同时增强而非损害了预测准确性。Poseidon数据集已在https://huggingface.co/datasets/BorisKriuk/Poseidon公开提供，包含预计算的能量特征、空间网格索引和标准化质量指标，以推动基于物理信息的地震研究。