In order to explore the abnormal variations before and after the Wen＇an MS. 1 earthquake on July 4, 2006, the gravity data observed by the Wenba Gravity Observation Network before and after the earthquake was analyze...In order to explore the abnormal variations before and after the Wen＇an MS. 1 earthquake on July 4, 2006, the gravity data observed by the Wenba Gravity Observation Network before and after the earthquake was analyzed. The relationships between gravity change and vertical displacement and shallow groundwater level were discussed, and elevation correction of the gravity was preliminarily performed. The results show that there were abnormal gravity changes before the MS. ! earthquake, which appeared as gravity rising in the whole observation network, especially in the south part. A high gradient of gravity variation appeared around the epicenter before the MS. 1 earthquake, especially during the short period between October 2005 to April 2006. The boundary of the positive and negative gravity variations gradually deflected and began to recover from October 2007.展开更多
On the basis of the comparison data of Stage II of the tunnel site leveling project at Hutubi seismic station and the observation data of Stage IV of the site cross fault leveling project at Hutubi and the level obser...On the basis of the comparison data of Stage II of the tunnel site leveling project at Hutubi seismic station and the observation data of Stage IV of the site cross fault leveling project at Hutubi and the level observation data from the cross fault survey lines in Dafeng from 1987 to 2012,this paper analyses the variation rates of the tunnel site leveling observation results and the difference of annual change rates of the cross fault level observations at Hongshan seismic station in Hutubi. This paper concludes the reliability of the Ni004 optical level used by the station and puts forward a proposal based on the analysis. This paper also explores the cross fault leveling research on the ground deformation in the region concerned on the basis of the historical observation of the cross fault level at Dafeng and the comparison results of the tunnel site leveling observation in Hutubi.展开更多
In real-life freeway transportation system, a few number of incident observation （very rare event） is available while there are large numbers of normal condition dataset. Most of researches on freeway incident detec...In real-life freeway transportation system, a few number of incident observation （very rare event） is available while there are large numbers of normal condition dataset. Most of researches on freeway incident detection have considered the incident detection problem as classification one. However, because of insufficiency of incident events, most of previous researches have utilized simulated incident events to develop freeway incident detection models. In order to overcome this drawback, this paper proposes a wavelet-based Hotelling 7a control chart for freeway incident detection, which integrates a wavelet transform into an abnormal detection method. Firstly, wavelet transform extracts useful features from noisy original traffic observations, leading to reduce the dimensionality of input vectors. Then, a Hotelling T2 control chart describes a decision boundary with only normal traffic observations with the selected features in the wavelet domain. Unlike the existing incident detection algorithms, which require lots of incident observations to construct incident detection models, the proposed approach can decide a decision boundary given only normal training observations. The proposed method is evaluated in comparison with California algorithm, Minnesota algorithm and conventional neural networks. The experimental results present that the proposed algorithm in this paper is a promising alternative for freeway automatic incident detections.展开更多
基金Supported by the Technology Research and Development Program of Institute of Earthquake Science, CEAthe National Key Technology R&D Program(2006BAC01B02-02),China
文摘In order to explore the abnormal variations before and after the Wen＇an MS. 1 earthquake on July 4, 2006, the gravity data observed by the Wenba Gravity Observation Network before and after the earthquake was analyzed. The relationships between gravity change and vertical displacement and shallow groundwater level were discussed, and elevation correction of the gravity was preliminarily performed. The results show that there were abnormal gravity changes before the MS. ! earthquake, which appeared as gravity rising in the whole observation network, especially in the south part. A high gradient of gravity variation appeared around the epicenter before the MS. 1 earthquake, especially during the short period between October 2005 to April 2006. The boundary of the positive and negative gravity variations gradually deflected and began to recover from October 2007.
基金sponsored by the Natural Science Foundation of Xinjiang Uighur Autonomous Region2012211B56)the Natural Science Foundation of China(41374031)the Earthquake Science and Technology Spark Plan(XH1030),and the Earthquake Science and Technology Spark Progam XH14054Y)
文摘On the basis of the comparison data of Stage II of the tunnel site leveling project at Hutubi seismic station and the observation data of Stage IV of the site cross fault leveling project at Hutubi and the level observation data from the cross fault survey lines in Dafeng from 1987 to 2012,this paper analyses the variation rates of the tunnel site leveling observation results and the difference of annual change rates of the cross fault level observations at Hongshan seismic station in Hutubi. This paper concludes the reliability of the Ni004 optical level used by the station and puts forward a proposal based on the analysis. This paper also explores the cross fault leveling research on the ground deformation in the region concerned on the basis of the historical observation of the cross fault level at Dafeng and the comparison results of the tunnel site leveling observation in Hutubi.
文摘In real-life freeway transportation system, a few number of incident observation （very rare event） is available while there are large numbers of normal condition dataset. Most of researches on freeway incident detection have considered the incident detection problem as classification one. However, because of insufficiency of incident events, most of previous researches have utilized simulated incident events to develop freeway incident detection models. In order to overcome this drawback, this paper proposes a wavelet-based Hotelling 7a control chart for freeway incident detection, which integrates a wavelet transform into an abnormal detection method. Firstly, wavelet transform extracts useful features from noisy original traffic observations, leading to reduce the dimensionality of input vectors. Then, a Hotelling T2 control chart describes a decision boundary with only normal traffic observations with the selected features in the wavelet domain. Unlike the existing incident detection algorithms, which require lots of incident observations to construct incident detection models, the proposed approach can decide a decision boundary given only normal training observations. The proposed method is evaluated in comparison with California algorithm, Minnesota algorithm and conventional neural networks. The experimental results present that the proposed algorithm in this paper is a promising alternative for freeway automatic incident detections.
