As one of the most earthquake-prone regions in the world, China faces extremely serious earthquake threats, especially for those heavily populated urban areas located near large fault zones. To improve the ability to prevent and minimize earthquake disaster risks, and to reduce earthquake disaster losses, China is currently building a nationwide earthquake early warning system (EEWS) with the largest seismic network in the world. In this paper, we present the newest progress of this project through describing the overall architecture of the national EEWS and evaluating the system performance during the 2022 Lushan M6.1 earthquake. The accuracy of the source characterization for the Lushan earthquake is discussed by comparing the continually estimated location and magnitude with the catalogs obtained from the China Earthquake Networks Center. For this earthquake, the EEWS generated a total of five alerts, and an initial alert was created 5.7 s after its occurrence, with excellent epicentral location and origin time estimation. The final alert was issued 16.5 s after origin time with a magnitude estimate of M6.1, the same as the catalog value. However, from the point view of alerting performance, the radius of the real blind zone without warning time was about 30 km and much larger than the theoretical result, mainly caused by the releasing system not considering the epicenter distance of each terminal when issuing the alerts. Although the earthquake exposed some limitations that need to be addressed in future upgrades, the results showed that most aspects of the EEWS presented a robust performance, with continuous, reliable event detections and early-warning information releasing.

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.

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.

The Sichuan–Yunnan region is a seismically active area. To explore the feasibility of using the support vector machine (SVM) method for magnitude estimation in the area and to improve the rapid magnitude estimation accuracy, we construct an SVM magnitude estimation model using transfer learning (TLSVM‐M model) based on a single‐station record in this study. We find that the magnitude estimation of a single station shows that for the test dataset, within the 3 s time window after the P‐wave arrival, the average absolute error (which reflects the size of the estimated magnitude error as a whole) and standard deviation (which reflects the scatter of magnitude estimation error) of the magnitudes estimated by the TLSVM‐M model are 0.31 and 0.41, respectively, which are less than those of the SVM magnitude estimation model without transfer learning (0.44 and 0.55, respectively), the $τc$ method (1.35 and 1.74, respectively) and the $Pd$ method (0.44 and 0.56, respectively). In addition, in test involving five earthquake events via the TLSVM‐M model, at 1 s after the first station is triggered, the magnitudes of three events (⁠ $Ms$ 4.2, 5.2, and 6.3) are estimated within an error range of ±0.3 magnitude units. For the other two earthquakes (⁠ $Ms$ 6.6 and 7.0), there is an obvious magnitude underestimation problem at 1 s after the first station is triggered, with less underestimation by increasing time after the first station is triggered. Meanwhile, for these two events (⁠ $Ms$ 6.6 and 7.0), within 13 s after the first station was triggered, the magnitude estimation errors are both within ±0.3 magnitude units. The TLSVM‐M model has the capability of rapid magnitude estimation for small‐to‐moderate events in the Sichuan–Yunnan region. Meanwhile, we infer that the proposed model may have potential in earthquake early warning.

Strong‐motion recordings observed during the $Ms$ 7.0 Jiuzhaigou earthquake, which occurred on 8 August 2017 in western China, were used to reveal the underlying source, propagation path, and site effects in comparison with observations from the 2013 Lushan earthquake of identical magnitude ($Ms$ 7.0). The similar $VS30$ distributions at the strong‐motion stations considered in both events reflect approximately consistent site effects. Amplitudes of short‐ and intermediate‐period ground motions (e.g., peak ground accelerations, peak ground velocities, and pseudospectral accelerations [PSAs] at $T=0.2$ and 2.0 s) observed in the Jiuzhaigou event were smaller than in the Lushan earthquake at sites with a Joyner–Boore distance ($RJB$) of $<200km$, but their amplitudes were similar at far‐field sites ($RJB>200km$). However, irrespective of $RJB$, larger amplitudes of long‐period ground motions (e.g., PSAs at $T=5.0s$) were observed in the Jiuzhaigou earthquake compared with the Lushan event. This study revealed that different fault styles (strike slip for the Jiuzhaigou event and reverse slip for the Lushan event), and different centroid depths could be two reasons for the period‐dependent differences of ground motions between the two earthquakes. Analysis of within‐event residuals suggested that the Jiuzhaigou region exhibited slower anelastic attenuation for short‐ and intermediate‐period ground motions than the Lushan region, but the anelastic attenuations for long‐period ground motions of both regions were similar. The durations of ground motions generated by both earthquakes were found comparable, as were the dependences on distance in both seismogenic regions.