According to calculation results of ocean chlorophyll concentration based on SeaWiFS data by SeaBAM model and synchronous ship-measured data, this research set up an improved model for CaseⅠand CaseⅡwater bodies...According to calculation results of ocean chlorophyll concentration based on SeaWiFS data by SeaBAM model and synchronous ship-measured data, this research set up an improved model for CaseⅠand CaseⅡwater bodies respectively. The monthly chlorophyll distribution in the East China Sea in 1998 was obtained from this improved model on calculation results of SeaBAM. The euphotic depth distribution in 1998 in the East China Sea is calculated by using remote sensing data of K 490 from SeaWiFS according to the relation between the euphotic depth and the oceanic diffuse attenuation coefficient. With data of ocean chlorophyll concentration, euphotic depth, ocean surface photosynthetic available radiation (PAR), daily photoperiod and optimal rate of daily carbon fixation within a water column, the monthly and annual primary productivity spatio-temporal distributions in the East China Sea in 1998 were obtained based on VGPM model. Based on analysis of those distributions, the conclusion can be drawn that there is a clear bimodality character of primary productivity in the monthly distribution in the East China Sea. In detail, the monthly distribution of primary productivity stays the lowest level in winter and rises rapidly to the peak in spring. It gets down a little in summer, and gets up a little in autumn. The daily average of primary productivity in the whole East China Sea is 560.03 mg/m 2 /d, which is far higher than the average of subtropical ocean areas. The annual average of primary productivity is 236.95 g/m 2 /a. The research on the seasonal variety mechanism of primary productivity shows that several factors that affect the spatio-temporal distribution may include the chlorophyll concentration distribution, temperature condition, the Yangtze River diluted water variety, the euphotic depth, ocean current variety, etc. But the main influencing factors may be different in each local sea area.展开更多
Based on abundant aftershock sequence data of the Wenchuan Ms8.0 earthquake on May 12, 2008, we studied the spatio-temporal variation process and segmentation rupture characteristic. Dense aftershocks distribute along...Based on abundant aftershock sequence data of the Wenchuan Ms8.0 earthquake on May 12, 2008, we studied the spatio-temporal variation process and segmentation rupture characteristic. Dense aftershocks distribute along Longmenshan central fault zone of NE direction and form a narrow strip with the length of 325 krn and the depth between several and 40 km. The depth profile （section of NW direction） vertical to the strike of aftershock zone （NE direction） shows anisomerous wedgy distribution characteristic of afiershock concentrated regions; it is related to the force form of the Longmenshan nappe tectonic belt. The stronger aftershocks could be divided into northern segment and southern segment apparently and the focal depths of strong aftershocks in the 50 km area between northern segment and southern segment are shallower. It seems like ＇to be going to rupture＇ segment. We also study focal mechanisms and segmentation of strong aftershocks. The principal compressive stress azimuth of aftershock area is WNW direction and the faulting types of aftershocks at southern and northern segment have the same proportion. Because afiershocks distribute on different secondary faults, their focal mechanisms present complex local tectonic stress field. The faulting of seven strong earthquakes on the Longmenshan central fault is mainly characterized by thrust with the component of right-lateral strike-slip. Meantime six strong aftershocks on the Longmenshan back-range fault and Qingchuan fault present strike-slip faulting. At last we discuss the complex segmentation rupture mechanism of the Wenchuan earthquake.展开更多
Improving short-term wind speed prediction accuracy and stability remains a challenge for wind forecasting researchers.This paper proposes a new variational mode decomposition(VMD)-attention-based spatio-temporal netw...Improving short-term wind speed prediction accuracy and stability remains a challenge for wind forecasting researchers.This paper proposes a new variational mode decomposition(VMD)-attention-based spatio-temporal network(VASTN)method that takes advantage of both temporal and spatial correlations of wind speed.First,VASTN is a hybrid wind speed prediction model that combines VMD,squeeze-and-excitation network(SENet),and attention mechanism(AM)-based bidirectional long short-term memory(BiLSTM).VASTN initially employs VMD to decompose the wind speed matrix into a series of intrinsic mode functions(IMF).Then,to extract the spatial features at the bottom of the model,each IMF employs an improved convolutional neural network algorithm based on channel AM,also known as SENet.Second,it combines BiLSTM and AM at the top layer to extract aggregated spatial features and capture temporal dependencies.Finally,VASTN accumulates the predictions of each IMF to obtain the predicted wind speed.This method employs VMD to reduce the randomness and instability of the original data before employing AM to improve prediction accuracy through mapping weight and parameter learning.Experimental results on real-world data demonstrate VASTN’s superiority over previous related algorithms.展开更多
基金The Key National Project for the Ninth Five-Year PlanNo.HY126-06-04-04
文摘According to calculation results of ocean chlorophyll concentration based on SeaWiFS data by SeaBAM model and synchronous ship-measured data, this research set up an improved model for CaseⅠand CaseⅡwater bodies respectively. The monthly chlorophyll distribution in the East China Sea in 1998 was obtained from this improved model on calculation results of SeaBAM. The euphotic depth distribution in 1998 in the East China Sea is calculated by using remote sensing data of K 490 from SeaWiFS according to the relation between the euphotic depth and the oceanic diffuse attenuation coefficient. With data of ocean chlorophyll concentration, euphotic depth, ocean surface photosynthetic available radiation (PAR), daily photoperiod and optimal rate of daily carbon fixation within a water column, the monthly and annual primary productivity spatio-temporal distributions in the East China Sea in 1998 were obtained based on VGPM model. Based on analysis of those distributions, the conclusion can be drawn that there is a clear bimodality character of primary productivity in the monthly distribution in the East China Sea. In detail, the monthly distribution of primary productivity stays the lowest level in winter and rises rapidly to the peak in spring. It gets down a little in summer, and gets up a little in autumn. The daily average of primary productivity in the whole East China Sea is 560.03 mg/m 2 /d, which is far higher than the average of subtropical ocean areas. The annual average of primary productivity is 236.95 g/m 2 /a. The research on the seasonal variety mechanism of primary productivity shows that several factors that affect the spatio-temporal distribution may include the chlorophyll concentration distribution, temperature condition, the Yangtze River diluted water variety, the euphotic depth, ocean current variety, etc. But the main influencing factors may be different in each local sea area.
基金supported by National Key Basic Research 973bNational Scientific Technology Support Plan (2006BAC01B02-01-01).
文摘Based on abundant aftershock sequence data of the Wenchuan Ms8.0 earthquake on May 12, 2008, we studied the spatio-temporal variation process and segmentation rupture characteristic. Dense aftershocks distribute along Longmenshan central fault zone of NE direction and form a narrow strip with the length of 325 krn and the depth between several and 40 km. The depth profile （section of NW direction） vertical to the strike of aftershock zone （NE direction） shows anisomerous wedgy distribution characteristic of afiershock concentrated regions; it is related to the force form of the Longmenshan nappe tectonic belt. The stronger aftershocks could be divided into northern segment and southern segment apparently and the focal depths of strong aftershocks in the 50 km area between northern segment and southern segment are shallower. It seems like ＇to be going to rupture＇ segment. We also study focal mechanisms and segmentation of strong aftershocks. The principal compressive stress azimuth of aftershock area is WNW direction and the faulting types of aftershocks at southern and northern segment have the same proportion. Because afiershocks distribute on different secondary faults, their focal mechanisms present complex local tectonic stress field. The faulting of seven strong earthquakes on the Longmenshan central fault is mainly characterized by thrust with the component of right-lateral strike-slip. Meantime six strong aftershocks on the Longmenshan back-range fault and Qingchuan fault present strike-slip faulting. At last we discuss the complex segmentation rupture mechanism of the Wenchuan earthquake.
基金supported by the undergraduate training program for innovation and entrepreneurship of NUIST(XJDC202110300239).
文摘Improving short-term wind speed prediction accuracy and stability remains a challenge for wind forecasting researchers.This paper proposes a new variational mode decomposition(VMD)-attention-based spatio-temporal network(VASTN)method that takes advantage of both temporal and spatial correlations of wind speed.First,VASTN is a hybrid wind speed prediction model that combines VMD,squeeze-and-excitation network(SENet),and attention mechanism(AM)-based bidirectional long short-term memory(BiLSTM).VASTN initially employs VMD to decompose the wind speed matrix into a series of intrinsic mode functions(IMF).Then,to extract the spatial features at the bottom of the model,each IMF employs an improved convolutional neural network algorithm based on channel AM,also known as SENet.Second,it combines BiLSTM and AM at the top layer to extract aggregated spatial features and capture temporal dependencies.Finally,VASTN accumulates the predictions of each IMF to obtain the predicted wind speed.This method employs VMD to reduce the randomness and instability of the original data before employing AM to improve prediction accuracy through mapping weight and parameter learning.Experimental results on real-world data demonstrate VASTN’s superiority over previous related algorithms.
