基于概率统计和机器学习的城市区域地震监测——以洛杉矶市区为例

Improved Earthquake Monitoring towards High-Risk Metropolitan Los Angeles

杨蕾 博士

东京大学 特任研究员

2021.11.12（星期五）15:00，理科二号楼2821

报告摘要：

Earthquake risk is highest in urban settings, owing to population density and to the presence of extensive and vulnerable infrastructure. Ideally, the intensive earthquake monitoring efforts in urban areas would be used to characterize the fault systems that pose the most immediate and direct threats to cities. However, the same factors – population and infrastructure – that cause risk exposure to be high, also make earthquake monitoring difficult to carry out.

In this study, we work on methods to improve the earthquake monitoring capacity for the high-risk metropolitan area. Firstly, we propose a new detection criterion based on a trace-randomization idea following back-projection to develop a reliable imaging threshold for candidate events. Alternatively, we develop a deep-learning-based seismic denoising algorithm, UrbanDenoiser, which can strongly suppress the urban noise levels relative to the signals. Both methods are applied to a dense seismic nodal array in Los Angeles. The seismic detection and localization results do not support the previously reported observation of mantle seismicity beneath Los Angeles, but suggest a seismogenic model in which the base of the seismogenic zone accommodates more seismic activity than faults at shallower depth, because of the stress concentration near the seismic-aseismic transition.

报告人简介：

杨蕾，现任东京大学特任研究员。2018~2021年在斯坦福大学从事博士后研究。于2013年获中国地质大学（北京）学士学位，2017年获中国科学院大学博士学位。主要研究方向包括微震监测，基于机器学习的地震数据管理，地震波传播以及地震成像。主持由美国自然科学基金（NSF）和美国地质调查局（USGS）联合资助的项目3项。发表国际期刊论文十余篇，其中以第一或通讯作者在Science Advances，Geophysics等期刊发表学术论文7篇。