Multi-wave exploration is an effective means for improving precision in the exploration and development of complex oil and gas reservoirs that are dense and have low permeability. However, convened wave data is charac...Multi-wave exploration is an effective means for improving precision in the exploration and development of complex oil and gas reservoirs that are dense and have low permeability. However, convened wave data is characterized by a low signal-to-noise ratio and low resolution, because the conventional deconvolution technology is easily affected by the frequency range limits, and there is limited scope for improving its resolution. The spectral inversion techniques is used to identify λ/8 thin layers and its breakthrough regarding band range limits has greatly improved the seismic resolution. The difficulty associated with this technology is how to use the stable inversion algorithm to obtain a high-precision reflection coefficient, and then to use this reflection coefficient to reconstruct broadband data for processing. In this paper, we focus on how to improve the vertical resolution of the converted PS-wave for multi-wave data processing. Based on previous research, we propose a least squares inversion algorithm with a total variation constraint, in which we uses the total variance as a priori information to solve under-determined problems, thereby improving the accuracy and stability of the inversion. Here, we simulate the Gaussian fitting amplitude spectrum to obtain broadband wavelet data, which we then process to obtain a higher resolution converted wave. We successfully apply the proposed inversion technology in the processing of high-resolution data from the Penglai region to obtain higher resolution convened wave data, which we then verify in a theoretical test. Improving the resolution of converted PS-wave data will provide more accurate data for subsequent velocity inversion and the extraction of reservoir reflection information.展开更多
Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data have been widely used for large area crop mapping.However,the temporal crop signatures generated fro...Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data have been widely used for large area crop mapping.However,the temporal crop signatures generated from these data were always accompanied by noise.In this study,a denoising method combined with Time series Inverse Distance Weighted (T-IDW) interpolating and Discrete Wavelet Transform (DWT) was presented.The detail crop planting patterns in Hebei Plain,China were classified using denoised time-series MODIS NDVI data at 250 m resolution.The denoising approach improved original MODIS NDVI product significantly in several periods,which may affect the accuracy of classification.The MODIS NDVI-derived crop map of the Hebei Plain achieved satisfactory classification accuracies through validation with field observation,statistical data and high resolution image.The field investigation accuracy was 85% at pixel level.At county-level,for winter wheat,there is relatively more significant correlation between the estimated area derived from satellite data with noise reduction and the statistical area (R2 = 0.814,p < 0.01).Moreover,the MODIS-derived crop patterns were highly consistent with the map generated by high resolution Landsat image in the same period.The overall accuracy achieved 91.01%.The results indicate that the method combining T-IDW and DWT can provide a gain in time-series MODIS NDVI data noise reduction and crop classification.展开更多
Dust aerosol optical depth (AOD) and its ac-companying shortwave radiative forcing (RF) are usually simulated by numerical models.Here,by using 9 months of Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol...Dust aerosol optical depth (AOD) and its ac-companying shortwave radiative forcing (RF) are usually simulated by numerical models.Here,by using 9 months of Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol product data in combination with Clouds and the Earth's Radiant Energy System Single Scanner Footprint (CERES/SSF) data,dust AOD and its shortwave RF were estimated over the cloud-free north-west (NW) Pacific Ocean in the springs of 2004,2005,and 2006.The results showed that in this region,the mean dust AOD and its shortwave RF were 0.10 and 5.51 W m 2,respectively.In order to validate the dust AOD de-rived by MODIS,results from the Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model were also used here.The correlation coefficient between the monthly averaged dust AOD derived by MODIS measurements and the model simulation results was approximately 0.53.Since the estimates of the dust AOD and its shortwave RF obtained in this study are based mainly on satellite data,they offer a good reference for numerical models.展开更多
A novel fiber Bragg grating(FBG) sensor with simultaneous sensing of displacement and temperature is presented.The FBG is affixed on the cantilever inclinedly.The midpoint of FBG exactly coincides with the zero strain...A novel fiber Bragg grating(FBG) sensor with simultaneous sensing of displacement and temperature is presented.The FBG is affixed on the cantilever inclinedly.The midpoint of FBG exactly coincides with the zero strain layer of a rectangular beam.The vertical displacement can be measured by the broadened bandwidth of FBG as the bandwidth is insensitive to temperature,while the temperature can be measured by the center wavelength shift as the wavelength shift is insensitive to vertical displacement.With 0.1 nm spectral resolution of the analyzer,sensitivities of bandwidth-displacement and center wavelength-temperature are 0.48 nm/mm and 0.05 nm/℃,resolutions are 0.2 mm and 2.0 ℃,and sensing ranges of displacement and temperature are up to 8.5 mm and 45℃ respectively.Experimental results match theoretical analyses very well.展开更多
The Qinghai-Tibet Plateau plays a very important role in studying severe weather in China and around the globe because of its unique characteristics. Moreover, the surface emissivities of the Qinghai-Tibet Plateau are...The Qinghai-Tibet Plateau plays a very important role in studying severe weather in China and around the globe because of its unique characteristics. Moreover, the surface emissivities of the Qinghai-Tibet Plateau are also important for retrieving surface and atmospheric parameters. In the current study, a retrieval algorithm was developed to retrieve the surface emissivities of the Qinghai-Tibet Plateau. The developed algorithm was derived from the radiative transfer model and was first validated using simulated data from a one-dimensional microwave simulator. The simulated results show good precision. Then, the surface emissivities of the Qinghai-Tibet Plateau were retrieved using brightness temperatures from the advanced microwave-scanning radiometer and atmospheric profile data from the moderate resolution imaging spectroradiometer. Finally, the features of the time and space distribution of the retrieved results were analyzed. In terms of spatial characteristics, a spatial distribution con- sistency was found between the retrieved results and surface coverage types of the Qinghai-Tibet Plateau. In terms of time characteristics, the changes in emissivity, which were within 0.01 for every day, were not evident within a one-month time scale. In addition, surface emissivities are sensitive to rainfall. The reasonability of the retrieved results indicates that the algorithm is feasible. A time-series surface emissivity database on the Qinghai-Tibet Plateau can be built using the developed algorithm, and then other surface or atmospheric parameters would have high retrieval precision to support related geological re- search on the Qinghai-Tibet Plateau.展开更多
基金supported by the China National Petroleum Corporation Scientific research and technology development project(Nos.2013E-38-08)
文摘Multi-wave exploration is an effective means for improving precision in the exploration and development of complex oil and gas reservoirs that are dense and have low permeability. However, convened wave data is characterized by a low signal-to-noise ratio and low resolution, because the conventional deconvolution technology is easily affected by the frequency range limits, and there is limited scope for improving its resolution. The spectral inversion techniques is used to identify λ/8 thin layers and its breakthrough regarding band range limits has greatly improved the seismic resolution. The difficulty associated with this technology is how to use the stable inversion algorithm to obtain a high-precision reflection coefficient, and then to use this reflection coefficient to reconstruct broadband data for processing. In this paper, we focus on how to improve the vertical resolution of the converted PS-wave for multi-wave data processing. Based on previous research, we propose a least squares inversion algorithm with a total variation constraint, in which we uses the total variance as a priori information to solve under-determined problems, thereby improving the accuracy and stability of the inversion. Here, we simulate the Gaussian fitting amplitude spectrum to obtain broadband wavelet data, which we then process to obtain a higher resolution converted wave. We successfully apply the proposed inversion technology in the processing of high-resolution data from the Penglai region to obtain higher resolution convened wave data, which we then verify in a theoretical test. Improving the resolution of converted PS-wave data will provide more accurate data for subsequent velocity inversion and the extraction of reservoir reflection information.
基金Under the auspices of Knowledge Innovation Programs of Chinese Academy of Sciences (No.KZCX2-YW-449,KSCX-YW-09)National Natural Science Foundation of China (No.40971025,40901030,50969003)
文摘Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data have been widely used for large area crop mapping.However,the temporal crop signatures generated from these data were always accompanied by noise.In this study,a denoising method combined with Time series Inverse Distance Weighted (T-IDW) interpolating and Discrete Wavelet Transform (DWT) was presented.The detail crop planting patterns in Hebei Plain,China were classified using denoised time-series MODIS NDVI data at 250 m resolution.The denoising approach improved original MODIS NDVI product significantly in several periods,which may affect the accuracy of classification.The MODIS NDVI-derived crop map of the Hebei Plain achieved satisfactory classification accuracies through validation with field observation,statistical data and high resolution image.The field investigation accuracy was 85% at pixel level.At county-level,for winter wheat,there is relatively more significant correlation between the estimated area derived from satellite data with noise reduction and the statistical area (R2 = 0.814,p < 0.01).Moreover,the MODIS-derived crop patterns were highly consistent with the map generated by high resolution Landsat image in the same period.The overall accuracy achieved 91.01%.The results indicate that the method combining T-IDW and DWT can provide a gain in time-series MODIS NDVI data noise reduction and crop classification.
基金supported by the National Basic Research Program of China (Grant No.2006CB403705)Key Projects in the National Science & Technology Pillar Program in the Eleventh Five-year Plan Period (Grant No.2008BAC40B01 and 2007BAC03A01)LASG Free Exploration Fund
文摘Dust aerosol optical depth (AOD) and its ac-companying shortwave radiative forcing (RF) are usually simulated by numerical models.Here,by using 9 months of Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol product data in combination with Clouds and the Earth's Radiant Energy System Single Scanner Footprint (CERES/SSF) data,dust AOD and its shortwave RF were estimated over the cloud-free north-west (NW) Pacific Ocean in the springs of 2004,2005,and 2006.The results showed that in this region,the mean dust AOD and its shortwave RF were 0.10 and 5.51 W m 2,respectively.In order to validate the dust AOD de-rived by MODIS,results from the Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model were also used here.The correlation coefficient between the monthly averaged dust AOD derived by MODIS measurements and the model simulation results was approximately 0.53.Since the estimates of the dust AOD and its shortwave RF obtained in this study are based mainly on satellite data,they offer a good reference for numerical models.
基金supported by the National High Technology Research and Development Program of China (No.2007AA03Z413)the National Natural Science Foundation of China (No.60727004)+3 种基金the Shaanxi Province "13115" Major Scientific and Technological Innovation Works Special Project (No.708087)the Major Science and Technology Project of Ministry of Education of China (No.Z08119)the Innovation Foundation of the Petro China (No. 2008D-5006-03-08)the Shaanxi Provincial Department of Education Project (No.09JS041)
文摘A novel fiber Bragg grating(FBG) sensor with simultaneous sensing of displacement and temperature is presented.The FBG is affixed on the cantilever inclinedly.The midpoint of FBG exactly coincides with the zero strain layer of a rectangular beam.The vertical displacement can be measured by the broadened bandwidth of FBG as the bandwidth is insensitive to temperature,while the temperature can be measured by the center wavelength shift as the wavelength shift is insensitive to vertical displacement.With 0.1 nm spectral resolution of the analyzer,sensitivities of bandwidth-displacement and center wavelength-temperature are 0.48 nm/mm and 0.05 nm/℃,resolutions are 0.2 mm and 2.0 ℃,and sensing ranges of displacement and temperature are up to 8.5 mm and 45℃ respectively.Experimental results match theoretical analyses very well.
基金supported by National Natural Science Foundation of China(Grant Nos. 41101314 and 40930530)State Key Laboratory of Remote Sensing Open Fund (Grant No. OFSLRSS201104)+2 种基金Institute of Plateau Meteorology Open Fund (Grant No. LPM2011018)Digital Earth Key Laboratory of CAS Open Fund (Grant No. 2010LDE008)Chinese Academy of Meteorological Science Special Fund (Grant No. 2008Z003)
文摘The Qinghai-Tibet Plateau plays a very important role in studying severe weather in China and around the globe because of its unique characteristics. Moreover, the surface emissivities of the Qinghai-Tibet Plateau are also important for retrieving surface and atmospheric parameters. In the current study, a retrieval algorithm was developed to retrieve the surface emissivities of the Qinghai-Tibet Plateau. The developed algorithm was derived from the radiative transfer model and was first validated using simulated data from a one-dimensional microwave simulator. The simulated results show good precision. Then, the surface emissivities of the Qinghai-Tibet Plateau were retrieved using brightness temperatures from the advanced microwave-scanning radiometer and atmospheric profile data from the moderate resolution imaging spectroradiometer. Finally, the features of the time and space distribution of the retrieved results were analyzed. In terms of spatial characteristics, a spatial distribution con- sistency was found between the retrieved results and surface coverage types of the Qinghai-Tibet Plateau. In terms of time characteristics, the changes in emissivity, which were within 0.01 for every day, were not evident within a one-month time scale. In addition, surface emissivities are sensitive to rainfall. The reasonability of the retrieved results indicates that the algorithm is feasible. A time-series surface emissivity database on the Qinghai-Tibet Plateau can be built using the developed algorithm, and then other surface or atmospheric parameters would have high retrieval precision to support related geological re- search on the Qinghai-Tibet Plateau.
