Earthquake early warning systems (EEWSs) are considered to be one of the most effective means for seismic risk mitigation, in terms of both losses and societal resilience, by releasing an alarm immediately after an earthquake occurs and before strong ground shaking arrives the target sites to be protected. To gain experience for the National System for Fast Seismic Intensity Report and Earthquake Early Warning project, we deployed a hybrid demonstration EEWS in the Sichuan–Yunnan border region with micro-electro-mechanical system-based sensors and broadband seismographs and low-latency data transmission. In this study, we described the structure of this EEWS and analyzed its performance in the first 2 yr from January 2017 to December 2018. During this test period, the EEWS detected and processed a total of 126 ML 3.0+ earthquakes, with excellent epicentral location and magnitude estimation. The average location and magnitude estimation errors for the first alert were 4.2±7.1  km and 0.2±0.31, respectively. For the earthquakes that occurred inside and outside the hybrid network, the first alert was generated 13.4±5.1  s and 26.3±13.5  s after the origin time (OT), respectively. We analyzed the performance of the EEWS for the 31 October 2018 M 5.1 earthquake, because it was the largest event that occurred inside the hybrid network during the test period. The first alert was obtained at 7.5 s after the OT, with a magnitude error of 0.1 magnitude unit, a location error of about 1 km, and a depth error of 8 km. Finally, we discussed the main differences between the EEWS’s estimates and the catalogs obtained by the China Earthquake Network Center, and proposed improvements to reduce the reporting time. This study demonstrated that we constructed a reliable, effective hybrid EEWS for the test region, which can provide sufficient support for the design of the National EEWS project.

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.

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In this paper, a nonlinear regression method called a support vector regression (SVR) is presented to establish the relationship between engineering ground motion parameters and macroseismic intensity (MSI). Sixteen ground motion parameters, including peak ground acceleration (PGA), peak ground velocity (PGV), Arias intensity, Housner intensity, acceleration spectrum intensity, velocity spectrum intensity, and others, are considered as candidates for feature selection to generate optimal SVR models. The datasets with both useable strong ground motion records and corresponding investigated MSIs in the Sichuan–Yunnan region, China, are all collected, and these 125 pairs of datasets are used for selecting features and comparing regression results. Nine ground motion parameters are selected as the most relevant features: PGA is the first fundamental one and PGV is the fifth relevant feature. Based on performance measures on the testing dataset, the best SVR model is given when the number of features is one all the way up to nine. According to predicted accuracy, SVR models with Gaussian kernel give much better MSI prediction than linear kernel SVR models and linear regression models. In particular, the Gaussian kernel SVR of PGA gives much higher MSI prediction accuracy than the linear regression model of PGV and PGA. The proposed SVR models are valid for MSI values from VI to IX, and they can be used for rapid mapping damage potential and reporting seismic intensity for this high-seismic-activity region.

China is currently building a nationwide earthquake early warning system (EEWS) which will be completed in June 2023. Several regions have been selected as pilot areas for instrumentation, software system and dissemination verification. For these regions, their construction tasks will be completed in advance with trial runs being carried out in June 2021. Before the trial operation, we need to understand the actual processing capabilities of different EEWSs. In this work, we focus on the system deployed in Sichuan province and evaluate its real-time performance during the 2019–2020 M6.0 Changning seismic sequence. This period was divided into two stages. The first stage was the time from the occurrence of the M6.0 (Mw5.7) mainshock (June 17, 2019) to the end of October 2019 with no MEMS-based stations around the Changning seismic sequence deployed and most of the broadband and short period seismic stations not upgraded to low latency streaming, and the second one was from the beginning of November 2019 to March 2021 with deployments of more than 700 MEMS-based stations and low latency upgrades of ∼30 seismic stations. Median errors for the epicentral locations, depths and magnitude estimations were almost the same for both stages, 1.5 ± 6.0 km, 0.0 ± 3.6 km and −0.1 ± 0.46 for the first stage and 2.3 ± 3.0 km, −3.0 ± 3.6 km and −0.2 ± 0.32 for the second one. However, an obvious underestimation of the magnitude for earthquakes with M 5.0 + occurring in the first stage was observed, which would be caused by the clipped waveforms, sensors deployed in short period seismic stations and MEMS-based stations, the adopted magnitude estimation method, and the method used to computer the network magnitude. The median reporting time was significantly improved from 10.5 ± 3.0 s after origin time for the first stage to 6.3 ± 3.5 s for the second stage because of introduction of newly deployed MEMS-based stations. The results obtained from the second stage indicate that the system has entered a stable operating stage and we can officially launch the trial operation in the pilot areas for public early warning services.