研究人员开发了一种名为 EarthquakeNet 的新神经网络架构，以改进每周地震发生频率的预测。该模型通过估计内生的每单元过度离散参数来解决标准方法的局限性，捕捉地震聚集的空间异质性。评估显示，与现有方法相比，EarthquakeNet 的预测误差降低了 8.6%，在预测极端事件方面提高了 12.5%。 AI
影响 引入了一种新颖的神经网络架构用于地震预测，有可能提高极端事件的准确性和风险评估。
排序理由 该集群包含一篇详细介绍新模型架构及其评估的学术论文。
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Standard approaches to forecasting the weekly number of earthquakes on a spatial grid rely on the Poisson distribution with a single global dispersion assumption. We show that this assumption is systematically violated in seismic data from Central Asia (2010-2024), where a likeli…
arXiv:2605.21437v1 Announce Type: cross Abstract: Standard approaches to forecasting the weekly number of earthquakes on a spatial grid rely on the Poisson distribution with a single global dispersion assumption. We show that this assumption is systematically violated in seismic …
Standard approaches to forecasting the weekly number of earthquakes on a spatial grid rely on the Poisson distribution with a single global dispersion assumption. We show that this assumption is systematically violated in seismic data from Central Asia (2010-2024), where a likeli…