On 12 May 2008, the devastating Wenchuan earthquake struck the Longmenshan fault zone, which comprised the eastern margin of the Tibetan Plateau, and this fault zone was predominantly a convergent boundary with a righ...On 12 May 2008, the devastating Wenchuan earthquake struck the Longmenshan fault zone, which comprised the eastern margin of the Tibetan Plateau, and this fault zone was predominantly a convergent boundary with a right-lateral strike-slip component. After such a large-magnitude earthquake, it was crucial to analyze the influences of the earthquake on the surrounding faults and the potential seismic activity. In this paper, a complex viscoelastic model of western Sichuan and eastern Tibet regions was constructed including the topography. Based on the findings of co-seismic static slip distribution, we calculated the stress change caused by the Wenchuan earthquake with the post-seismic relaxation into consideration. Our preliminary results indicated that： （1） The tectonic stressing rate was relatively high in Kunlun mountain pass-Jiangcuo, Ganzi-Yushu, Xianshuihe and Zemuhe faults; while in the east Kunlun and Longriba was medium; also the value was less in the Minjiang, Longmenshan, Anninghe and Huya faults. As to the Longmenshan fault, the value was 0.28×10-3 MPa/a to 0.35×10-3 MPa/a, which is coincident with the previous long recurrence interval of Wenchuan earthquake; （2） The Wenchuan earthquake not only caused the Coulomb stress decrease in the source region, but also the stress increase in the two terminals, especially the northeastern segment, which is comparatively consistent with the aftershock distribution. Meanwhile, the high concentration areas of the static slip distribution were corresponding to the Coulomb stress reductions; （3） The Coulomb stress change caused by Wenchuan earthquake showed significant increase on five major faults, which were northwestern segment of Xianshuihe fault, eastern Kunlun fault, Longriba fault, Minjiang fault and Huya fault respectively; also the Coulomb stress on the fault plane of the Yushu earthquake was faintly increased; （4） We defined the recurrence interval as the time needed to accumulate the magnitude of the stress drop, and the recurrence interval of Wenchuan earthquake was estimated about 1 714 a to 2 143 a correspondingly.展开更多
Multiple faults are easily confused with single faults.In order to identify multiple faults more accurately,a highly efficient learning method is proposed based on a double parallel two-hidden-layer extreme learning m...Multiple faults are easily confused with single faults.In order to identify multiple faults more accurately,a highly efficient learning method is proposed based on a double parallel two-hidden-layer extreme learning machine,called DPTELM.The DPT-ELM method is a variant of an extreme learning machine(ELM).There are some issues with ELM.First,achieving a high accuracy requires too many hidden nodes;second,the direct connection between the input layer and the output layer is ignored.Accordingly,to deal with the above-mentioned problems,DPT-ELM extends the single-hidden-layer ELM to a two-hidden-layer ELM,which can achieve a desired performance with fewer hidden nodes.In addition,a direct connection is built between the input layer and the output layer.Since the input layer weights and the thresholds of the two hidden layers are determined randomly,this simplifies the improved model and shortens the calculation time.Additionally,to improve the signal to noise ratio(SNR),an adaptive waveform decomposition(AWD)algorithm is used to denoise the vibration signal.Then,the denoised signal is used to extract the eigenvalues by the time-domain and frequency-domain methods.Finally,the eigenvalues are input to the DPT-ELM classifier.In this paper,two groups of rolling bearing data at different speeds,which were collected from a real experimental platform,are used to test the method.Each set of data includes three single fault states,two complex fault states and a healthy state.The experimental results demonstrate that the DPT-ELM method achieves fast learning speed and a high accuracy.Moreover,based on 10-fold cross-validation,it proves to be an effective method to improve the accuracy with fewer hidden nodes.展开更多
The purpose of this paper is to analyze the regional fault systems o f Qaidam basin and adjacent orogenic belts. Field investigation and seismic interp retation indicate that five regional fault systems occurred in t...The purpose of this paper is to analyze the regional fault systems o f Qaidam basin and adjacent orogenic belts. Field investigation and seismic interp retation indicate that five regional fault systems occurred in the Qaidam and ad jacent mountain belts, controlling the development and evolution of the Qaidam b asin. These fault systems are: (1)north Qaidam Qilian Mountain fault system; (2 ) south Qaidam East Kunlun Mountain fault system; (3)Altun strike slip fault s ystem; (4)Elashan strike slip fault system, and (5) Gansen Xiaochaidan fault s ystem. It is indicated that the fault systems controlled the orientation of the Qaidam basin, the formation and distribution of secondary faults within the basi n, the migration of depocenters and the distribution of hydrocarbon accumulation belt.展开更多
基金financially supported by Basic Science and Research from the Institute of Crustal Dynamics,China Earthquake Administration (ZDJ2012-09,ZDJ2010-12)the National Key Technology Research and Development Program (2008BAC38B04)
文摘On 12 May 2008, the devastating Wenchuan earthquake struck the Longmenshan fault zone, which comprised the eastern margin of the Tibetan Plateau, and this fault zone was predominantly a convergent boundary with a right-lateral strike-slip component. After such a large-magnitude earthquake, it was crucial to analyze the influences of the earthquake on the surrounding faults and the potential seismic activity. In this paper, a complex viscoelastic model of western Sichuan and eastern Tibet regions was constructed including the topography. Based on the findings of co-seismic static slip distribution, we calculated the stress change caused by the Wenchuan earthquake with the post-seismic relaxation into consideration. Our preliminary results indicated that： （1） The tectonic stressing rate was relatively high in Kunlun mountain pass-Jiangcuo, Ganzi-Yushu, Xianshuihe and Zemuhe faults; while in the east Kunlun and Longriba was medium; also the value was less in the Minjiang, Longmenshan, Anninghe and Huya faults. As to the Longmenshan fault, the value was 0.28×10-3 MPa/a to 0.35×10-3 MPa/a, which is coincident with the previous long recurrence interval of Wenchuan earthquake; （2） The Wenchuan earthquake not only caused the Coulomb stress decrease in the source region, but also the stress increase in the two terminals, especially the northeastern segment, which is comparatively consistent with the aftershock distribution. Meanwhile, the high concentration areas of the static slip distribution were corresponding to the Coulomb stress reductions; （3） The Coulomb stress change caused by Wenchuan earthquake showed significant increase on five major faults, which were northwestern segment of Xianshuihe fault, eastern Kunlun fault, Longriba fault, Minjiang fault and Huya fault respectively; also the Coulomb stress on the fault plane of the Yushu earthquake was faintly increased; （4） We defined the recurrence interval as the time needed to accumulate the magnitude of the stress drop, and the recurrence interval of Wenchuan earthquake was estimated about 1 714 a to 2 143 a correspondingly.
基金supported by National Natural Science Foundation of China(51675035/51375037)
文摘Multiple faults are easily confused with single faults.In order to identify multiple faults more accurately,a highly efficient learning method is proposed based on a double parallel two-hidden-layer extreme learning machine,called DPTELM.The DPT-ELM method is a variant of an extreme learning machine(ELM).There are some issues with ELM.First,achieving a high accuracy requires too many hidden nodes;second,the direct connection between the input layer and the output layer is ignored.Accordingly,to deal with the above-mentioned problems,DPT-ELM extends the single-hidden-layer ELM to a two-hidden-layer ELM,which can achieve a desired performance with fewer hidden nodes.In addition,a direct connection is built between the input layer and the output layer.Since the input layer weights and the thresholds of the two hidden layers are determined randomly,this simplifies the improved model and shortens the calculation time.Additionally,to improve the signal to noise ratio(SNR),an adaptive waveform decomposition(AWD)algorithm is used to denoise the vibration signal.Then,the denoised signal is used to extract the eigenvalues by the time-domain and frequency-domain methods.Finally,the eigenvalues are input to the DPT-ELM classifier.In this paper,two groups of rolling bearing data at different speeds,which were collected from a real experimental platform,are used to test the method.Each set of data includes three single fault states,two complex fault states and a healthy state.The experimental results demonstrate that the DPT-ELM method achieves fast learning speed and a high accuracy.Moreover,based on 10-fold cross-validation,it proves to be an effective method to improve the accuracy with fewer hidden nodes.
文摘The purpose of this paper is to analyze the regional fault systems o f Qaidam basin and adjacent orogenic belts. Field investigation and seismic interp retation indicate that five regional fault systems occurred in the Qaidam and ad jacent mountain belts, controlling the development and evolution of the Qaidam b asin. These fault systems are: (1)north Qaidam Qilian Mountain fault system; (2 ) south Qaidam East Kunlun Mountain fault system; (3)Altun strike slip fault s ystem; (4)Elashan strike slip fault system, and (5) Gansen Xiaochaidan fault s ystem. It is indicated that the fault systems controlled the orientation of the Qaidam basin, the formation and distribution of secondary faults within the basi n, the migration of depocenters and the distribution of hydrocarbon accumulation belt.
基金Supported by the National Natural Science Foundation of China(61363073,61363033,61262032)Hunan Province Natural Science Foundation of China(13JJ6058)Research Foundation of Education Bureau of Hunan Province(13A075,14C0923,13C754)
