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EQGraphNet:Advancing single-station earthquake magnitude estimation via deep graph networks with residual connections

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Artificial Intelligence in Geosciences 2024年第1期5卷 257-268页

作者： Zhiguo Wang Ziwei Chen Huai Zhang School of Mathematics and Statistics Xi’an Jiaotong UniversityXi’an710049ShaanxiPR China School of Information and Communications Engineering Xi’an Jiaotong UniversityXi’an710049ShaanxiPR China College of Earth and Planetary Sciences University of Chinese Academy of Sciences.Beijing100049BeijingPR China

magnitude estimation is a critical task in seismology,and conventional methods usually require dense seismic station arrays to provide data with sufficient spatiotemporal *** this context,we propose the Earthquake Graph Network(EQGraphNet)to enhance the performance of single-station magnitude *** backbone of the proposed model consists of eleven convolutional neural network layers and ten RCGL modules,where a RCGL combines a Residual Connection and a Graph convolutional Layer capable of mitigating the over-smoothing problem and simultaneously extracting temporal features of seismic *** work uses the STanford EArthquake Dataset for model training and performance *** with three existing deep learning models,EQGraphNet demonstrates improved accuracy for both local magnitude and duration magnitude *** evaluate the robustness,we add natural background noise to the model input and find that EQGraphNet achieves the best results,particularly for signals with lower signal-to-noise ***,by replacing various network components and comparing their estimation performances,we illustrate the contribution of each part of EQGraphNet,validating the rationality of our *** also demonstrate the generalization capability of our model across different earthquakes occurring environments,achieving mean errors of±0.1 ***,by demonstrating the effectiveness of deeper architectures,this work encourages further exploration of deeper GNN models for both multi-station and single-station magnitude estimation.

关键词： magnitude estimation Graph neural network Symmetric graph Single-station seismic signals

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Manifestation of earthquake preparation zone in the ionosphere before 2021 Sonitpur,Assam earthquake revealed by GPS-TEC data

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Geodesy and Geodynamics 2022年第3期13卷 230-237页

作者： Gopal Sharma North Eastern Space Applications Centre Umiam793103MeghalayaIndia

Global Positioning System(GPS)Continuously Operating Reference Station(CORS)data analysis shows that the ionosphere’s electron density variability is linked to the deformation and stress accumulation in the Earth’s *** in ionosphere total electron content(TEC)variability before 2021 M6.4 Sonitpur,Assam earthquake were detected using L1 and L2 GPS frequencies that showed three distinct abnormalities on April 3,9,10,***’s correlation coefficient(r)of TEC decreases in the CORS that lies away from the earthquake epicenter,indicating the possibilities of a positive relationship between TEC variability and earthquake *** concentration also decreases towards the epicenter within the earthquake preparation zone(EPZ).It is also observed that the Pearson’s correlation coefficient(r)of TEC decreases linearly near the *** study demonstrates the possibilities of determining the TEC anomalous zone in the ionosphere that coincides with the EPZ in the crustal *** research indicated the possibilities of magnitude estimation of an impending earthquake based on the TEC anomalous zone in the ionosphere using closely spaced dense CORS network data.

关键词： GPS TEC Sonitpur earthquake Ionosphere perturbation TEC anomaly magnitude estimation

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Evaluation of earthquake signal characteristics for early warning

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Earthquake Engineering and Engineering Vibration 2012年第3期11卷 435-443页

作者： Kong Qingkai Zhao Ming Building Engineering Department Tongji UniversityShanghai 200092China

This paper evaluates different characteristics for earthquake early warning. The scaling relationships between magnitude, epicenter distance and calculated parameters are derived from earthquake event data fi＇om USGS. The standard STA/LTA method is modified by adding two new parameters to eliminate the effects of the spike-type noise and small pulsetype noise ahead of the onset of the P-wave. After the detection of the P-wave, the algorithm extracts 12 kinds of parameters from the first 3 seconds of the P-wave. Then stepwise regression analysis of these parameters is performed to estimate the epicentral distance and magnitude. Six different parameters are selected to estimate the epicentral distance, and the median error for all 419 estimates is 16.5 krn. Four parameters are optimally combined to estimate the magnitude, and the mean error for all events is 0.0 magnitude units, with a standard deviation of 0.5. Finally, based on the estimation results, additional work is proposed to improve the accuracy of the results.

关键词： earthquake early warning epicentral distance estimation magnitude estimation

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Real-time prediction of earthquake potential damage:A case study for the January 8,2022 M_(S) 6.9 Menyuan earthquake in Qinghai,China

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Earthquake Research Advances 2023年第1期3卷 52-60页

作者： Jindong Song Jingbao Zhu Yongxiang Wei Shuilong Li Shanyou Li Key Laboratory of Earthquake Engineering and Engineering Vibration Institute of Engineering MechanicsChina Earthquake AdministrationHarbin 150080China Key Laboratory of Earthquake Disaster Mitigation Ministry of Emergency ManagementHarbin 150080China Fujian Earthquake Agency Fuzhou 350003China

It is critical to determine whether a site has potential damage in real-time after an earthquake occurs,which is a challenge in earthquake disaster ***,we propose a real-time Earthquake Potential Damage predictor(EPDor)based on predicting peak ground velocities(PGVs)of *** EPDor is composed of three parts:(1)predicting the magnitude of an earthquake and PGVs of triggered stations based on the machine learning prediction models;(2)predicting the PGVs at distant sites based on the empirical ground motion prediction equation;(3)generating the PGV map through predicting the PGV of each grid point based on an interpolation process of weighted average based on the predicted values in(1)and(2).We apply the EPDor to the 2022 M_(S) 6.9 Menyuan earthquake in Qinghai Province,China to predict its potential *** the initial few seconds after the first station is triggered,the EPDor can determine directly whether there is potential damage for some sites to a certain ***,we infer that the EPDor has potential application for future ***,it also has potential in Chinese earthquake early warning system.

关键词： Earthquake early warning Potential damage Machine learning 2022 M_(S)6.9 Menyuan earthquake magnitude estimation On-site peak ground velocity prediction

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