The sensible and latent heat fluxes over the ocean area near China were calculated and analyzed by usingthe Goddard Earth Observing System (GEOS) - four-dimensional Data Assimilation System (DAS). The calculated resul...The sensible and latent heat fluxes over the ocean area near China were calculated and analyzed by usingthe Goddard Earth Observing System (GEOS) - four-dimensional Data Assimilation System (DAS). The calculated results showed that the sensible heat flux had its large value in winter and autumn , small value in spring and summer overthe ocean area near China. In winter, the sensible heat flux increased distinctly with latitude, and its isolines were verycrowded. Over the ocean area east of Taiwan Province and south of Japan, the direction of isoline was from southwestto northeast. In the South China Sea the sensible heat flux was lower than that of surrounding ocean areas,and its isoline was distributed into a type of an inverted trough. In autumn and winter, the maximum center of latent heat flux appeared over the ocean area northeast of Taiwan Province and south and southeast of Japan, meanwhile, the isoline wasin the direction of southwest to northeast. In spring and summer, the latent heat flux had minimum value in the Huanghai Sea. At the same time, the maximum value center of latent heat flux appeared over the ocean area south of Japan inspring.展开更多
Distributed acoustic sensing(DAS)has rapidly emerged as a transformative technology in seismic exploration,particularly in vertical seismic profiles(vsP).However,the acquired vsP data suffer from strong coherent DAs c...Distributed acoustic sensing(DAS)has rapidly emerged as a transformative technology in seismic exploration,particularly in vertical seismic profiles(vsP).However,the acquired vsP data suffer from strong coherent DAs coupling noise and random noise.Current deep learning denoising methods,dependent on noise labels derived from conventional denoising techniques,fall short in addressing the unique noise properties inherent in DAS data.To address this challenge,we propose an exponential decay curve-constrained empirical mode decomposition(EDcc-EMD)analysis-based supervised denoising network.Our method begins with extracting the initial noise from the field DAs vsP data through the traditional EMD method.Despite containing some signal leakage,this noise is further processed through EMD to derive intrinsic mode functions(IMFs).We,then,analyze the correlation coefficients between these IMFs and the initial noise,applying an exponential decay curve(EDC)law to isolate pure noise.This refined noise data serves as accurate labels,enhancing the denoising network's precision.Meanwhile,most of the methods usually consider the t-x domain features and ignore the important frequency-domain features.Consequently,we train our network with frequency-domain data instead of time domain data,capitalizing on the more distinct separation of noise and signal characteristics,thereby facilitating more effective noise-signal discrimination.The experimental results demonstrate that our method significantly enhances the denoising performance and successfully recovers weak signals.展开更多
文摘The sensible and latent heat fluxes over the ocean area near China were calculated and analyzed by usingthe Goddard Earth Observing System (GEOS) - four-dimensional Data Assimilation System (DAS). The calculated results showed that the sensible heat flux had its large value in winter and autumn , small value in spring and summer overthe ocean area near China. In winter, the sensible heat flux increased distinctly with latitude, and its isolines were verycrowded. Over the ocean area east of Taiwan Province and south of Japan, the direction of isoline was from southwestto northeast. In the South China Sea the sensible heat flux was lower than that of surrounding ocean areas,and its isoline was distributed into a type of an inverted trough. In autumn and winter, the maximum center of latent heat flux appeared over the ocean area northeast of Taiwan Province and south and southeast of Japan, meanwhile, the isoline wasin the direction of southwest to northeast. In spring and summer, the latent heat flux had minimum value in the Huanghai Sea. At the same time, the maximum value center of latent heat flux appeared over the ocean area south of Japan inspring.
基金supported by the National Natural Science Foundation of China(Nos.42404140,42130808)the National Key Research and Development Program of China under grant 2021YFA0716802.We thank Professor Xiang-Fang Zeng from Innovation Academy for Precision measurement Science and Technology,Chinese Academy of Sciences for his valuable discussions.
文摘Distributed acoustic sensing(DAS)has rapidly emerged as a transformative technology in seismic exploration,particularly in vertical seismic profiles(vsP).However,the acquired vsP data suffer from strong coherent DAs coupling noise and random noise.Current deep learning denoising methods,dependent on noise labels derived from conventional denoising techniques,fall short in addressing the unique noise properties inherent in DAS data.To address this challenge,we propose an exponential decay curve-constrained empirical mode decomposition(EDcc-EMD)analysis-based supervised denoising network.Our method begins with extracting the initial noise from the field DAs vsP data through the traditional EMD method.Despite containing some signal leakage,this noise is further processed through EMD to derive intrinsic mode functions(IMFs).We,then,analyze the correlation coefficients between these IMFs and the initial noise,applying an exponential decay curve(EDC)law to isolate pure noise.This refined noise data serves as accurate labels,enhancing the denoising network's precision.Meanwhile,most of the methods usually consider the t-x domain features and ignore the important frequency-domain features.Consequently,we train our network with frequency-domain data instead of time domain data,capitalizing on the more distinct separation of noise and signal characteristics,thereby facilitating more effective noise-signal discrimination.The experimental results demonstrate that our method significantly enhances the denoising performance and successfully recovers weak signals.
