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
The star identification algorithm usually identifies stars by angular distance matching.However,under high dynamic conditions,the rolling shutter effect distorts the angular distances between the measured and true sta...The star identification algorithm usually identifies stars by angular distance matching.However,under high dynamic conditions,the rolling shutter effect distorts the angular distances between the measured and true star positions,leading to plethoric false matches and requiring complex and time-consuming verification for star identification.Low identification rate hinders the application of low-noise and cost-effective rolling shutter image sensors.In this work,we first study a rolling shutter distortion model of angular distances between stars,and then propose a novel three-stage star identification algorithm to identify distorted star images captured by the rolling shutter star sensor.The first stage uses a modified grid algorithm with adaptive error tolerance and an expanded pattern database to efficiently eliminate spurious matches.The second stage performs angular velocity estimation based on Hough transform to verify the matches that follow the same distortion pattern.The third stage applies a rolling shutter error correction method for further verification.Both the simulation and night sky image test demonstrate the effectiveness and efficiency of our algorithm under high dynamic conditions.The accuracy of angular velocity estimation method by Hough transform is evaluated and the root mean square error is below 0.5(°)/s.Our algorithm achieves a 95.7% identification rate at an angular velocity of 10(°)/s,which is much higher than traditional algorithms.展开更多
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supported by the National Key Research and Development Program of China(No.2019YFA0706002).
文摘The star identification algorithm usually identifies stars by angular distance matching.However,under high dynamic conditions,the rolling shutter effect distorts the angular distances between the measured and true star positions,leading to plethoric false matches and requiring complex and time-consuming verification for star identification.Low identification rate hinders the application of low-noise and cost-effective rolling shutter image sensors.In this work,we first study a rolling shutter distortion model of angular distances between stars,and then propose a novel three-stage star identification algorithm to identify distorted star images captured by the rolling shutter star sensor.The first stage uses a modified grid algorithm with adaptive error tolerance and an expanded pattern database to efficiently eliminate spurious matches.The second stage performs angular velocity estimation based on Hough transform to verify the matches that follow the same distortion pattern.The third stage applies a rolling shutter error correction method for further verification.Both the simulation and night sky image test demonstrate the effectiveness and efficiency of our algorithm under high dynamic conditions.The accuracy of angular velocity estimation method by Hough transform is evaluated and the root mean square error is below 0.5(°)/s.Our algorithm achieves a 95.7% identification rate at an angular velocity of 10(°)/s,which is much higher than traditional algorithms.
