Joint inversion is one of the most effective methods for reducing non-uniqueness for geophysical inversion.The current joint inversion methods can be divided into the structural consistency constraint and petrophysica...Joint inversion is one of the most effective methods for reducing non-uniqueness for geophysical inversion.The current joint inversion methods can be divided into the structural consistency constraint and petrophysical consistency constraint methods,which are mutually independent.Currently,there is a need for joint inversion methods that can comprehensively consider the structural consistency constraints and petrophysical consistency constraints.This paper develops the structural similarity index(SSIM)as a new structural and petrophysical consistency constraint for the joint inversion of gravity and vertical gradient data.The SSIM constraint is in the form of a fraction,which may have analytical singularities.Therefore,converting the fractional form to the subtractive form can solve the problem of analytic singularity and finally form a modified structural consistency index of the joint inversion,which enhances the stability of the SSIM constraint applied to the joint inversion.Compared to the reconstructed results from the cross-gradient inversion,the proposed method presents good performance and stability.The SSIM algorithm is a new joint inversion method for petrophysical and structural constraints.It can promote the consistency of the recovered models from the distribution and the structure of the physical property values.Then,applications to synthetic data illustrate that the algorithm proposed in this paper can well process the synthetic data and acquire good reconstructed results.展开更多
Gravity survey was done at the Eburru area to estimate the source depth locations and delineate the fault boundaries using 3D Euler deconvolution. Gravity data was collected using CG-5 gravimeter. Gravity data reducti...Gravity survey was done at the Eburru area to estimate the source depth locations and delineate the fault boundaries using 3D Euler deconvolution. Gravity data was collected using CG-5 gravimeter. Gravity data reductions were done by applying drift correction, latitude correction, free air correction, Bouguer correction and terrain correction to the observed raw data to obtain complete bouguer anomaly (CBA). The CBA data was transferred to Oasis montaj software for Euler deconvolution processing. The 3D Euler deconvolution was carried out to determine and estimate the depth of the density bodies. Euler deconvolution locates the gravity anomaly source and estimates its depth from the gravity observation level. Euler deconvolution was preferred to other filtering methods in this study as solutions are only determined over identified analytic signal peaks, the window size varies according to anomaly size and the final solution involves only a few more precise depth estimates. The Euler deconvolution was performed using structural indices of 0.5, 1.0 and 2.0. Results from this analysis indicated that the CBA values in this study area range from gravity values of -272 mGal to -286 mGal and residual Bouguer anomaly amplitude range between -3 mGal and 3.4 mGal. The 0.5, 1.0 and 2.0 structural indices generated five solutions at depth range of 433 m - 2269 m, 801 m - 1433 m and 1170 m - 2246 m respectively occurring almost at the same locations on gravity highs. The deep structures were observed to occur in the northern part of the study area, and interpreted to be dense intruding masses likely to be trapped by the overlying cap rock at these depths. These could be geothermal heat sources that can be exploited to generate geothermal energy.展开更多
In this article,structural probabilistic and non-probabilistic reliability have been evaluated and compared under big data condition.Firstly,the big data is collected via structural monitoring and analysis.Big data is...In this article,structural probabilistic and non-probabilistic reliability have been evaluated and compared under big data condition.Firstly,the big data is collected via structural monitoring and analysis.Big data is classified into different types according to the regularities of the distribution of data.The different stresses which have been subjected by the structure are used in this paper.Secondly,the structural interval reliability and probabilistic pre-diction models are established by using the stress-strength interference theory under big data of random loads after the stresses and structural strength are comprehensively considered.Structural reliability is computed by using various stress types,and the minimum reliability is determined as structural reliability.Finally,the advan-tage and disadvantage of the interval reliability method and probability reliability method are shown by using three examples.It has been shown that the proposed methods are feasible and effective.展开更多
A multilevel secure relation hierarchical data model for multilevel secure database is extended from the relation hierarchical data model in single level environment in this paper. Based on the model, an upper lowe...A multilevel secure relation hierarchical data model for multilevel secure database is extended from the relation hierarchical data model in single level environment in this paper. Based on the model, an upper lower layer relationalintegrity is presented after we analyze and eliminate the covert channels caused by the database integrity.Two SQL statements are extended to process polyinstantiation in the multilevel secure environment.The system based on the multilevel secure relation hierarchical data model is capable of integratively storing and manipulating complicated objects ( e.g. , multilevel spatial data) and conventional data ( e.g. , integer, real number and character string) in multilevel secure database.展开更多
利用2007—2016年国际卫星云气候计划(International Satellite Cloud Climatology Project,ISCCP)、云和地球辐射能量系统(Clouds and the Earth s Radiant Energy System,CERES)和中分辨率成像光谱仪(Moderate Resolution Imaging Spe...利用2007—2016年国际卫星云气候计划(International Satellite Cloud Climatology Project,ISCCP)、云和地球辐射能量系统(Clouds and the Earth s Radiant Energy System,CERES)和中分辨率成像光谱仪(Moderate Resolution Imaging Spectroradiometer,MODIS)卫星反演云产品,对比分析了不同数据反演的中国地区云系结构的宏微观特征,并采用复合评价指标定量评估了不同数据之间时间和空间上的一致性。结果表明:三套卫星数据都能较好地反演出中国地区总云量呈南高北低、东高西低、夏高冬低的分布特征,但通过比较时间技巧(Temporal Skill,S_(T))及空间技巧(Spatial Skill,S_(S))复合评价指标及其各项分量发现,与MODIS相比,CERES与ISCCP数据反演的总云量时间序列演变特征明显更为一致,且其评分均有南方优于北方,夏季优于冬季的特征;进一步分析不同高度云量的S_(T)评分发现,CERES和ISCCP两套数据在南方地区的总云量差异主要来自于低云量的绝对偏差,而北方地区的偏差则同时存在于低云和中云;对比分析MODIS和CERES反演的云滴有效半径发现,高云对应的冰相云一致性较高,而中低云相对应的液相云的偏差则有夏季高于冬季的规律。针对夏季液相和冰相云滴粒径及概率密度分析则表明,相比CERES数据,MODIS对夏季液水和冰水粒子的有效半径在不同地区均有不同程度的高估,液(冰)水谱宽则更宽(窄)。展开更多
基金supported by the National Key Research and Development Program(Grant No.2021YFA0716100)the National Key Research and Development Program of China Project(Grant No.2018YFC0603502)+1 种基金the Henan Youth Science Fund Program(Grant No.212300410105)the provincial key R&D and promotion special project of Henan Province(Grant No.222102320279).
文摘Joint inversion is one of the most effective methods for reducing non-uniqueness for geophysical inversion.The current joint inversion methods can be divided into the structural consistency constraint and petrophysical consistency constraint methods,which are mutually independent.Currently,there is a need for joint inversion methods that can comprehensively consider the structural consistency constraints and petrophysical consistency constraints.This paper develops the structural similarity index(SSIM)as a new structural and petrophysical consistency constraint for the joint inversion of gravity and vertical gradient data.The SSIM constraint is in the form of a fraction,which may have analytical singularities.Therefore,converting the fractional form to the subtractive form can solve the problem of analytic singularity and finally form a modified structural consistency index of the joint inversion,which enhances the stability of the SSIM constraint applied to the joint inversion.Compared to the reconstructed results from the cross-gradient inversion,the proposed method presents good performance and stability.The SSIM algorithm is a new joint inversion method for petrophysical and structural constraints.It can promote the consistency of the recovered models from the distribution and the structure of the physical property values.Then,applications to synthetic data illustrate that the algorithm proposed in this paper can well process the synthetic data and acquire good reconstructed results.
文摘Gravity survey was done at the Eburru area to estimate the source depth locations and delineate the fault boundaries using 3D Euler deconvolution. Gravity data was collected using CG-5 gravimeter. Gravity data reductions were done by applying drift correction, latitude correction, free air correction, Bouguer correction and terrain correction to the observed raw data to obtain complete bouguer anomaly (CBA). The CBA data was transferred to Oasis montaj software for Euler deconvolution processing. The 3D Euler deconvolution was carried out to determine and estimate the depth of the density bodies. Euler deconvolution locates the gravity anomaly source and estimates its depth from the gravity observation level. Euler deconvolution was preferred to other filtering methods in this study as solutions are only determined over identified analytic signal peaks, the window size varies according to anomaly size and the final solution involves only a few more precise depth estimates. The Euler deconvolution was performed using structural indices of 0.5, 1.0 and 2.0. Results from this analysis indicated that the CBA values in this study area range from gravity values of -272 mGal to -286 mGal and residual Bouguer anomaly amplitude range between -3 mGal and 3.4 mGal. The 0.5, 1.0 and 2.0 structural indices generated five solutions at depth range of 433 m - 2269 m, 801 m - 1433 m and 1170 m - 2246 m respectively occurring almost at the same locations on gravity highs. The deep structures were observed to occur in the northern part of the study area, and interpreted to be dense intruding masses likely to be trapped by the overlying cap rock at these depths. These could be geothermal heat sources that can be exploited to generate geothermal energy.
基金The work described in this paper was supported in part by the Foundation from the Science Foundation,Guizhou,China(Qian Kehe[2018]1055)Research Foundation for Talented Scholars in Ningxia Normal University.
文摘In this article,structural probabilistic and non-probabilistic reliability have been evaluated and compared under big data condition.Firstly,the big data is collected via structural monitoring and analysis.Big data is classified into different types according to the regularities of the distribution of data.The different stresses which have been subjected by the structure are used in this paper.Secondly,the structural interval reliability and probabilistic pre-diction models are established by using the stress-strength interference theory under big data of random loads after the stresses and structural strength are comprehensively considered.Structural reliability is computed by using various stress types,and the minimum reliability is determined as structural reliability.Finally,the advan-tage and disadvantage of the interval reliability method and probability reliability method are shown by using three examples.It has been shown that the proposed methods are feasible and effective.
文摘A multilevel secure relation hierarchical data model for multilevel secure database is extended from the relation hierarchical data model in single level environment in this paper. Based on the model, an upper lower layer relationalintegrity is presented after we analyze and eliminate the covert channels caused by the database integrity.Two SQL statements are extended to process polyinstantiation in the multilevel secure environment.The system based on the multilevel secure relation hierarchical data model is capable of integratively storing and manipulating complicated objects ( e.g. , multilevel spatial data) and conventional data ( e.g. , integer, real number and character string) in multilevel secure database.
文摘利用2007—2016年国际卫星云气候计划(International Satellite Cloud Climatology Project,ISCCP)、云和地球辐射能量系统(Clouds and the Earth s Radiant Energy System,CERES)和中分辨率成像光谱仪(Moderate Resolution Imaging Spectroradiometer,MODIS)卫星反演云产品,对比分析了不同数据反演的中国地区云系结构的宏微观特征,并采用复合评价指标定量评估了不同数据之间时间和空间上的一致性。结果表明:三套卫星数据都能较好地反演出中国地区总云量呈南高北低、东高西低、夏高冬低的分布特征,但通过比较时间技巧(Temporal Skill,S_(T))及空间技巧(Spatial Skill,S_(S))复合评价指标及其各项分量发现,与MODIS相比,CERES与ISCCP数据反演的总云量时间序列演变特征明显更为一致,且其评分均有南方优于北方,夏季优于冬季的特征;进一步分析不同高度云量的S_(T)评分发现,CERES和ISCCP两套数据在南方地区的总云量差异主要来自于低云量的绝对偏差,而北方地区的偏差则同时存在于低云和中云;对比分析MODIS和CERES反演的云滴有效半径发现,高云对应的冰相云一致性较高,而中低云相对应的液相云的偏差则有夏季高于冬季的规律。针对夏季液相和冰相云滴粒径及概率密度分析则表明,相比CERES数据,MODIS对夏季液水和冰水粒子的有效半径在不同地区均有不同程度的高估,液(冰)水谱宽则更宽(窄)。
