Time-frequency analysis is a successfully used tool for analyzing the local features of seismic data.However,it suffers from several inevitable limitations,such as the restricted time-frequency resolution,the difficul...Time-frequency analysis is a successfully used tool for analyzing the local features of seismic data.However,it suffers from several inevitable limitations,such as the restricted time-frequency resolution,the difficulty in selecting parameters,and the low computational efficiency.Inspired by deep learning,we suggest a deep learning-based workflow for seismic time-frequency analysis.The sparse S transform network(SSTNet)is first built to map the relationship between synthetic traces and sparse S transform spectra,which can be easily pre-trained by using synthetic traces and training labels.Next,we introduce knowledge distillation(KD)based transfer learning to re-train SSTNet by using a field data set without training labels,which is named the sparse S transform network with knowledge distillation(KD-SSTNet).In this way,we can effectively calculate the sparse time-frequency spectra of field data and avoid the use of field training labels.To test the availability of the suggested KD-SSTNet,we apply it to field data to estimate seismic attenuation for reservoir characterization and make detailed comparisons with the traditional time-frequency analysis methods.展开更多
The spatial and temporal distribution of bacterioplankton communities plays a vital role in understanding the ecological dynamics and health of aquatic ecosystems.In this study,we conducted a comprehensive investigati...The spatial and temporal distribution of bacterioplankton communities plays a vital role in understanding the ecological dynamics and health of aquatic ecosystems.In this study,we conducted a comprehensive investigation of the bacterioplankton communities in the Qiantang River(Hangzhou section).Water samples were collected quarterly from seven sites over a one-year period.Physical and chemical parameters,including dissolved oxygen(DO),water temperature(WT),chemical oxygen demand(COD),nitrite(NO_(2)^(-)),active phosphate(PO_(4)^(3-))and other indices were determined.In this study,theαdiversity,βdiversity and abundance differences of bacterial communities were investigated using 16S rRNA high-throughput sequencing analysis.The spatial and temporal distribution characteristics and main driving factors of the bacterioplankton community structure and diversity were discussed.The results showed that a total of 57 phyla were detected in the bacterioplankton community,among which Proteobacteria and Actinomycetes were the two dominant groups with the highest relative abundance.The results of PCoA based on Bray-Curtis distance showed that the sampling season had a slightly greater effect on the changes in bacterioplankton community structure in the Qiantang River.Mantel and partial Mantel test showed that environmental variables(Mantel r=0.6739,P<0.0001;partial Mantel r=0.507,P=0.0001)were more important than geographical distance(Mantel r=0.5322,P<0.001;partial Mantel r=0.1563,P=0.001).The distance attenuation model showed that there was significant distance attenuation in all four seasons,and the maximum limit of bacterial community diffusion was found in spring.RDA analysis showed that nine environmental factors,including COD,WT and DO,significantly affected community distribution(P<0.05).This study provides valuable insights into the spatial and temporal distribution characteristics of bacterioplankton communities,shedding light on their ecological roles in the Qiantang River.The information obtained can guide future research on the interactions between bacterioplankton and their environment,enabling the development of effective conservation strategies and sustainable management practices for aquatic ecosystems.展开更多
In this work, the image reconstruction in π-scheme short-scan single-photon emission computed tomography （SPECT） with nonuniform attenuation is derived in its most general form when π-scheme short-scan SPECT entai...In this work, the image reconstruction in π-scheme short-scan single-photon emission computed tomography （SPECT） with nonuniform attenuation is derived in its most general form when π-scheme short-scan SPECT entails data acquisition over disjoint angular intervals without conjugate views totaling to π radians. The reconstruction results are based on decomposition of Novikov＇s inversion operator into three parts bounded in the L2 sense. The first part involves the measured partial data; the second part is a skew-symmetric operator; the third part is a symmetric and compact contribution. It is showed firstly that the operators involved belong to L（L^2（B）. Furthermore numerical simulations are conducted to demonstrate the effectiveness of the developed method.展开更多
探讨了利用广义 S 变换代替短时 Fourier 变换或连续小波变换,进行吸收衰减补偿的方法。对短时 Fou- tier 变换、连续小波变换、S 变换和广义 S 变换进行了分析和比较,给出了基于广义 S 变换的吸收衰减补偿方法。该方法的实现步骤是：...探讨了利用广义 S 变换代替短时 Fourier 变换或连续小波变换,进行吸收衰减补偿的方法。对短时 Fou- tier 变换、连续小波变换、S 变换和广义 S 变换进行了分析和比较,给出了基于广义 S 变换的吸收衰减补偿方法。该方法的实现步骤是：①用广义 S 变换对高信噪比的叠加地震信号逐道进行时频分析；②在每个时间点,根据地层吸收特点提取各个频率的能量吸收衰减因子；③用加权方法对每个时间所对应的各个频率的广义 S 变换系数进行补偿,使各个频率在不同时间的能量相同；④将所有时间各个频率加权补偿的结果重构回地震记录,实现对地层吸收的补偿。模拟结果表明,广义 S 变换时频分析方法能够提高信号时频分布的分辨率。对实际二维地震数据的试算结果表明,基于广义 S 变换的吸收衰减补偿方法能较好地对地层吸收进行补偿,提高地震资料的分辨率,改善地震资料的品质。展开更多
基金supported by the Natural Science Foundation of Jiangsu Province(Grant No.BK20200021)the National Natural Science Foundation of China(Grant No.42174161 and 41974123)the Natural Science Foundation of Heilongjiang Province of China(YQ2023D005).
基金supported by the National Natural Science Foundation of China (42274144,42304122,and 41974155)the Key Research and Development Program of Shaanxi (2023-YBGY-076)+1 种基金the National Key R&D Program of China (2020YFA0713404)the China Uranium Industry and East China University of Technology Joint Innovation Fund (NRE202107)。
文摘Time-frequency analysis is a successfully used tool for analyzing the local features of seismic data.However,it suffers from several inevitable limitations,such as the restricted time-frequency resolution,the difficulty in selecting parameters,and the low computational efficiency.Inspired by deep learning,we suggest a deep learning-based workflow for seismic time-frequency analysis.The sparse S transform network(SSTNet)is first built to map the relationship between synthetic traces and sparse S transform spectra,which can be easily pre-trained by using synthetic traces and training labels.Next,we introduce knowledge distillation(KD)based transfer learning to re-train SSTNet by using a field data set without training labels,which is named the sparse S transform network with knowledge distillation(KD-SSTNet).In this way,we can effectively calculate the sparse time-frequency spectra of field data and avoid the use of field training labels.To test the availability of the suggested KD-SSTNet,we apply it to field data to estimate seismic attenuation for reservoir characterization and make detailed comparisons with the traditional time-frequency analysis methods.
基金financially supported by the Fisheries Species Conservation Program of the Agricultural Department of China (Nos.171821303154051044,17190236)the Natural Science Foundation of Zhejiang Province (No.LQ20C190003)+1 种基金the Natural Science Foundation of Ningbo Municipality (Nos.2019A610421,2019A 610443)the K.C.Wong Magna Fund in Ningbo University。
文摘The spatial and temporal distribution of bacterioplankton communities plays a vital role in understanding the ecological dynamics and health of aquatic ecosystems.In this study,we conducted a comprehensive investigation of the bacterioplankton communities in the Qiantang River(Hangzhou section).Water samples were collected quarterly from seven sites over a one-year period.Physical and chemical parameters,including dissolved oxygen(DO),water temperature(WT),chemical oxygen demand(COD),nitrite(NO_(2)^(-)),active phosphate(PO_(4)^(3-))and other indices were determined.In this study,theαdiversity,βdiversity and abundance differences of bacterial communities were investigated using 16S rRNA high-throughput sequencing analysis.The spatial and temporal distribution characteristics and main driving factors of the bacterioplankton community structure and diversity were discussed.The results showed that a total of 57 phyla were detected in the bacterioplankton community,among which Proteobacteria and Actinomycetes were the two dominant groups with the highest relative abundance.The results of PCoA based on Bray-Curtis distance showed that the sampling season had a slightly greater effect on the changes in bacterioplankton community structure in the Qiantang River.Mantel and partial Mantel test showed that environmental variables(Mantel r=0.6739,P<0.0001;partial Mantel r=0.507,P=0.0001)were more important than geographical distance(Mantel r=0.5322,P<0.001;partial Mantel r=0.1563,P=0.001).The distance attenuation model showed that there was significant distance attenuation in all four seasons,and the maximum limit of bacterial community diffusion was found in spring.RDA analysis showed that nine environmental factors,including COD,WT and DO,significantly affected community distribution(P<0.05).This study provides valuable insights into the spatial and temporal distribution characteristics of bacterioplankton communities,shedding light on their ecological roles in the Qiantang River.The information obtained can guide future research on the interactions between bacterioplankton and their environment,enabling the development of effective conservation strategies and sustainable management practices for aquatic ecosystems.
基金supported by the National Natural Science Foundation of China(61271398)K.C.Wong Magna Fund in Ningbo University
文摘In this work, the image reconstruction in π-scheme short-scan single-photon emission computed tomography （SPECT） with nonuniform attenuation is derived in its most general form when π-scheme short-scan SPECT entails data acquisition over disjoint angular intervals without conjugate views totaling to π radians. The reconstruction results are based on decomposition of Novikov＇s inversion operator into three parts bounded in the L2 sense. The first part involves the measured partial data; the second part is a skew-symmetric operator; the third part is a symmetric and compact contribution. It is showed firstly that the operators involved belong to L（L^2（B）. Furthermore numerical simulations are conducted to demonstrate the effectiveness of the developed method.
文摘探讨了利用广义 S 变换代替短时 Fourier 变换或连续小波变换,进行吸收衰减补偿的方法。对短时 Fou- tier 变换、连续小波变换、S 变换和广义 S 变换进行了分析和比较,给出了基于广义 S 变换的吸收衰减补偿方法。该方法的实现步骤是：①用广义 S 变换对高信噪比的叠加地震信号逐道进行时频分析；②在每个时间点,根据地层吸收特点提取各个频率的能量吸收衰减因子；③用加权方法对每个时间所对应的各个频率的广义 S 变换系数进行补偿,使各个频率在不同时间的能量相同；④将所有时间各个频率加权补偿的结果重构回地震记录,实现对地层吸收的补偿。模拟结果表明,广义 S 变换时频分析方法能够提高信号时频分布的分辨率。对实际二维地震数据的试算结果表明,基于广义 S 变换的吸收衰减补偿方法能较好地对地层吸收进行补偿,提高地震资料的分辨率,改善地震资料的品质。
