Magnitude estimation is a critical task in seismology,and conventional methods usually require dense seismic station arrays to provide data with sufficient spatiotemporal distribution.In this context,we propose the Ea...Magnitude estimation is a critical task in seismology,and conventional methods usually require dense seismic station arrays to provide data with sufficient spatiotemporal distribution.In this context,we propose the Earthquake Graph Network(EQGraphNet)to enhance the performance of single-station magnitude estimation.The 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 signals.Our work uses the STanford EArthquake Dataset for model training and performance testing.Compared with three existing deep learning models,EQGraphNet demonstrates improved accuracy for both local magnitude and duration magnitude scales.To 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 ratios.Additionally,by replacing various network components and comparing their estimation performances,we illustrate the contribution of each part of EQGraphNet,validating the rationality of our approach.We also demonstrate the generalization capability of our model across different earthquakes occurring environments,achieving mean errors of±0.1 units.Furthermore,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.展开更多
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...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.展开更多
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 ...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 crust.Anomalies 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 3,9,10,2021.Pearson’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 epicenter.TEC 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 EPZ.The study demonstrates the possibilities of determining the TEC anomalous zone in the ionosphere that coincides with the EPZ in the crustal rocks.The 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.展开更多
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 reduction.Here,we propose a real-time Earthquake Potential Damage pre...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 reduction.Here,we propose a real-time Earthquake Potential Damage predictor(EPDor)based on predicting peak ground velocities(PGVs)of sites.The 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 damage.Within 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 degree.Hence,we infer that the EPDor has potential application for future earthquakes.Meanwhile,it also has potential in Chinese earthquake early warning system.展开更多
Earthquake Early Warning ( EEW) has come to attention,as earthquake prediction is still unreliable. The paper comprehensively illustrates the research status and important issues of EEW from the aspects of concept,com...Earthquake Early Warning ( EEW) has come to attention,as earthquake prediction is still unreliable. The paper comprehensively illustrates the research status and important issues of EEW from the aspects of concept,composition and method. By analyzing the status of EEW in China,we find that the essential requirements have been met for building earthquake early warning systems in the country in terms of government and social needs, network construction and basic research. The technical difficulties and non-technical challenges in implementing EEW in China are evaluated, and some suggestions are proposed regarding the relevant legal measures,public education and protection against earthquake disasters. so as to bring into full play the role of the EEW system in earthquake disaster prevention and reduction.展开更多
Precise estimation of the location and magnitude of boundary layer transition is essential for the exact computation of aero-thermodynamics and the performance of hypersonic vehicles.Compared with resource-intensive m...Precise estimation of the location and magnitude of boundary layer transition is essential for the exact computation of aero-thermodynamics and the performance of hypersonic vehicles.Compared with resource-intensive methods such as large eddy simulation,direct numerical simulation,and experimental approaches,Reynolds-averaged Navier-Stokes(RANS)-based models offer an efficient and cost-effective solution for engineering applications.Therefore,this review focuses on the capabilities of various RANS-based models for prediction of boundary layer transition in hypersonic flows.The formulation and underlying assumptions of these models are described and their predictive performance in terms of transition initiation and length in hypersonic regimes is examined.Critical gaps and limitations of existing models are outlined and a framework is established for future development of RANS-based transition models,with the aim of developing more robust,reliable,and cost-effective techniques for prediction of hypersonic boundary layer transition that are suitable for use in current state-of-the-art computational codes.展开更多
1.Difficulties of conventional seismic studies on earthquake source parameters Earthquake source parameters,including magnitude,location,focal mechanism,rupture process are key factors for understanding seismogenic en...1.Difficulties of conventional seismic studies on earthquake source parameters Earthquake source parameters,including magnitude,location,focal mechanism,rupture process are key factors for understanding seismogenic environment,mitigating seismic hazards,estimating earthquake triggering,and tectonic analysis.Traditionally,source parameters are determined by seismological methods.For example,Fang L H et al.（2014）relocated the 2012 Ms6.6 Xinjiang Xinyuan earthquake sequence using local seismograms based on the double difference method,展开更多
基金supported by the National Natural Science Foundation of China under Grant 41974137.
文摘Magnitude estimation is a critical task in seismology,and conventional methods usually require dense seismic station arrays to provide data with sufficient spatiotemporal distribution.In this context,we propose the Earthquake Graph Network(EQGraphNet)to enhance the performance of single-station magnitude estimation.The 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 signals.Our work uses the STanford EArthquake Dataset for model training and performance testing.Compared with three existing deep learning models,EQGraphNet demonstrates improved accuracy for both local magnitude and duration magnitude scales.To 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 ratios.Additionally,by replacing various network components and comparing their estimation performances,we illustrate the contribution of each part of EQGraphNet,validating the rationality of our approach.We also demonstrate the generalization capability of our model across different earthquakes occurring environments,achieving mean errors of±0.1 units.Furthermore,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.
文摘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.
文摘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 crust.Anomalies 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 3,9,10,2021.Pearson’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 epicenter.TEC 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 EPZ.The study demonstrates the possibilities of determining the TEC anomalous zone in the ionosphere that coincides with the EPZ in the crustal rocks.The 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.
基金financially supported by the National Natural Science Foundation of China (U2039209, U1839208, and 51408564)the Natural Science Foundation of Heilongjiang Province (LH2021E119)+1 种基金Spark Program of Earthquake Science (XH23027YB)the National Key Research and Development Program of China (2018YFC1504003).
文摘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 reduction.Here,we propose a real-time Earthquake Potential Damage predictor(EPDor)based on predicting peak ground velocities(PGVs)of sites.The 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 damage.Within 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 degree.Hence,we infer that the EPDor has potential application for future earthquakes.Meanwhile,it also has potential in Chinese earthquake early warning system.
基金funded by Key Projects in the National Science & Technology Pillar Program ( Grant No. 2012BAK19B04)the National Natural Science Foundation ( Grant No. 41104023)the Science & Technology Development Project of Shandong Province ( Grant No. 2011GSF12004)
文摘Earthquake Early Warning ( EEW) has come to attention,as earthquake prediction is still unreliable. The paper comprehensively illustrates the research status and important issues of EEW from the aspects of concept,composition and method. By analyzing the status of EEW in China,we find that the essential requirements have been met for building earthquake early warning systems in the country in terms of government and social needs, network construction and basic research. The technical difficulties and non-technical challenges in implementing EEW in China are evaluated, and some suggestions are proposed regarding the relevant legal measures,public education and protection against earthquake disasters. so as to bring into full play the role of the EEW system in earthquake disaster prevention and reduction.
基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korean Government-(No.RS-2025-00557769).
文摘Precise estimation of the location and magnitude of boundary layer transition is essential for the exact computation of aero-thermodynamics and the performance of hypersonic vehicles.Compared with resource-intensive methods such as large eddy simulation,direct numerical simulation,and experimental approaches,Reynolds-averaged Navier-Stokes(RANS)-based models offer an efficient and cost-effective solution for engineering applications.Therefore,this review focuses on the capabilities of various RANS-based models for prediction of boundary layer transition in hypersonic flows.The formulation and underlying assumptions of these models are described and their predictive performance in terms of transition initiation and length in hypersonic regimes is examined.Critical gaps and limitations of existing models are outlined and a framework is established for future development of RANS-based transition models,with the aim of developing more robust,reliable,and cost-effective techniques for prediction of hypersonic boundary layer transition that are suitable for use in current state-of-the-art computational codes.
文摘1.Difficulties of conventional seismic studies on earthquake source parameters Earthquake source parameters,including magnitude,location,focal mechanism,rupture process are key factors for understanding seismogenic environment,mitigating seismic hazards,estimating earthquake triggering,and tectonic analysis.Traditionally,source parameters are determined by seismological methods.For example,Fang L H et al.（2014）relocated the 2012 Ms6.6 Xinjiang Xinyuan earthquake sequence using local seismograms based on the double difference method,
