将物理定律融入地震预测模型，提升准确率并可解释。
POSEIDON: Physics-Optimized Seismic Energy Inference and Detection Operating Network
- 用能量模型嵌入地震学规律，统一处理多个任务。
- 在280万事件数据上表现最优，关键参数符合科学范围。
- 适合地震研究者与灾害预警系统开发者使用。
地震预测与地震风险评估仍是地球物理学中的核心挑战，现有机器学习方法常作为黑箱模型，忽略已知物理规律。我们提出POSEIDON（Physics-Optimized Seismic Energy Inference and Detection Operating Network），一种基于物理约束的能量模型，用于统一的多任务地震事件预测，并发布Poseidon数据集——目前最大的开源全球地震目录，包含280万条记录，覆盖30年。POSEIDON将古腾堡-里克特震级频度关系和奥莫里-乌茨后震衰减定律等基本地震学原理，作为可学习约束嵌入能量模型框架中。该架构同时解决三个关联任务：后震序列识别、海啸生成潜力判断及前震检测。大量实验表明，POSEIDON在所有任务上均达到最先进性能，优于梯度提升、随机森林和卷积神经网络基线，平均F1分数最高。关键的是，学习到的物理参数收敛至科学可解释值：古腾堡-里克特b值为0.752，奥莫里-乌茨参数p=0.835，c=0.1948天，均处于公认范围，且未牺牲预测精度。Poseidon数据集已在https://huggingface.co/datasets/BorisKriuk/Poseidon公开，提供预计算能量特征、空间网格索引和标准化质量指标，推动物理信息地震研究发展。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。