Machine Learning and Point Processes for Insights into Earthquakes and Volcanoes

用于洞察地震和火山的机器学习和点过程

• 批准号: 2437813
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2437813
• 关键词: Machine; Learning; Point; Processes; Insights

Recent advances in the processing of seismic data have resulted in a drastic increase in the size of earthquake catalogs available globally. This data as well as the field of Seismology, however, has gone relatively unnoticed by the machine learning community. In my PhD I aim to bridge this gap through an investigation into what machine learning methods can be applied to the task of earthquake forecasting. More specifically, since earthquake events are generally modelled as a point process, I will look at developing new methodology for the learning of point processes. This will begin with an investigation into whether neural point process models can better capture the real patterns of earthquakes than the most up to date parametric model, the Epidemic-Type Aftershock Sequence model. Further to developing the methodology of point processes, I seek to create software that the seismology community can use in practice in an attempt to reduce the impact on human lives caused seismic hazards.

地震数据处理的最新进展导致全球可用地震目录的规模急剧增加。然而，这些数据以及地震学领域相对未被机器学习社区所注意。在我的博士学位中，我的目标是通过调查机器学习方法可以应用于地震预测任务来弥合这一差距。更具体地说，由于地震事件通常被建模为一个点过程，我将着眼于开发新的方法来学习点过程。这将开始调查是否神经点过程模型可以更好地捕捉真实的模式的地震比最新的参数模型，流行病型余震序列模型。进一步发展点过程的方法，我试图创建软件，地震学界可以在实践中使用，试图减少对人类生活造成的地震灾害的影响。