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Performance Evaluation of a Dense MEMS-Based Seismic Sensor Array Deployed in the Sichuan-Yunnan Border Region for Earthquake Early Warning

机译：四川-云南边境地区部署的基于MEMS的密集地震传感器阵列的性能评估

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With the last decades of development, earthquake early warning (EEW) has proven to be one of the potential means for disaster mitigation. Usually, the density of the EEW network determines the performance of the EEW system. For reducing the cost of sensors and building a dense EEW network, an upgraded low-cost Micro Electro Mechanical System (MEMS)-based sensor named GL-P2B was developed in this research. This device uses a new high-performance CPU board and is built on a custom-tailored Linux 3.6.9 operating system integrating with seismological processing. Approximately 170 GL-P2Bs were installed and tested in the Sichuan-Yunnan border region from January 2017 to December 2018. We evaluated its performance on noise-level, dynamic range (DR), useful resolution (NU), collocated recording comparison, and shake map generation. The results proved that GL-P2B can be classified as a type of Class-B sensor. The records obtained are consistent with the data obtained by the collocated traditional force-balanced accelerometers even for stations with an epicenter distance of more than 150 km, and most of the relative percentage difference of peak ground acceleration (PGA) values is smaller than 10%. In addition, with the current density of the GL-P2B seismic network, near-real-time refined shake maps without using values derived for virtual stations could be directly generated, which will significantly improve the capability for earthquake emergency response. Overall, this MEMS-based sensor can meet the requirements of dense EEW purpose and lower the total investment of the National System for Fast Seismic Intensity Report and Earthquake Early Warning project.
机译：随着最近几十年的发展，地震预警（EEW）已被证明是减轻灾害的潜在手段之一。通常，EEW网络的密度决定了EEW系统的性能。为了降低传感器的成本并建立密集的EEW网络，在本研究中开发了一种升级的低成本基于微机电系统（MEMS）的传感器，名为GL-P2B。该设备使用新的高性能CPU板，并在定制的Linux 3.6.9操作系统上集成了地震处理功能。从2017年1月至2018年12月，在四川-云南边境地区安装并测试了约170台GL-P2B。我们评估了其在噪声级，动态范围（DR），有用分辨率（NU），并置记录比较和抖动方面的性能。地图生成。结果证明，GL-P2B可归为B类传感器。所获得的记录与并置的传统力平衡加速度计获得的数据一致，即使对于震中距离超过150 km的站，并且地面加速度峰值（PGA）值的大部分相对百分比差异也小于10％ 。此外，利用GL-P2B地震网络的当前密度，可以直接生成不使用为虚拟台站得出的值的近实时精简摇动图，这将显着提高地震应急响应的能力。总体而言，这种基于MEMS的传感器可以满足密集EEW目的的要求，并降低了国家快速地震强度报告和地震预警系统的总投资。

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