The rapid processing,analysis,and mining of remote-sensing big data based on intelligent interpretation technology using remote-sensing cloud computing platforms(RS-CCPs)have recently become a new trend.The existing R...The rapid processing,analysis,and mining of remote-sensing big data based on intelligent interpretation technology using remote-sensing cloud computing platforms(RS-CCPs)have recently become a new trend.The existing RS-CCPs mainly focus on developing and optimizing high-performance data storage and intelligent computing for common visual representation,which ignores remote sensing data characteristics such as large image size,large-scale change,multiple data channels,and geographic knowledge embedding,thus impairing computational efficiency and accuracy.We construct a LuoJiaAI platform composed of a standard large-scale sample database(LuoJiaSET)and a dedicated deep learning framework(LuoJiaNET)to achieve state-of-the-art performance on five typical remote sensing interpretation tasks,including scene classification,object detection,land-use classification,change detection,and multi-view 3D reconstruction.The details of the LuoJiaAI application experiment can be found at the white paper for LuoJiaAI industrial application.In addition,LuoJiaAI is an open-source RS-CCP that supports the latest Open Geospatial Consortium(OGC)standards for better developing and sharing Earth Artificial Intelligence(AI)algorithms and products on benchmark datasets.LuoJiaAI narrows the gap between the sample database and deep learning frameworks through a user-friendly data-framework collaboration mechanism,showing great potential in high-precision remote sensing mapping applications.展开更多
A goal-oriented adaptive finite element（FE） method for solving 3D direct current（DC） resistivity modeling problem is presented. The model domain is subdivided into unstructured tetrahedral elements that allow for ...A goal-oriented adaptive finite element（FE） method for solving 3D direct current（DC） resistivity modeling problem is presented. The model domain is subdivided into unstructured tetrahedral elements that allow for efficient local mesh refinement and flexible description of complex models. The elements that affect the solution at each receiver location are adaptively refined according to a goal-oriented posteriori error estimator using dual-error weighting approach. The FE method with adapting mesh can easily handle such structures at almost any level of complexity. The method is demonstrated on two synthetic resistivity models with analytical solutions and available results from integral equation method, so the errors can be quantified. The applicability of the numerical method is illustrated on a resistivity model with a topographic ridge. Numerical examples show that this method is flexible and accurate for geometrically complex situations.展开更多
High Spatial and Spectral Resolution(HSSR)remote-sensing images can provide rich spectral bands and detailed ground information,but there is a relative lack of research on this new type of remote-sensing data.Although...High Spatial and Spectral Resolution(HSSR)remote-sensing images can provide rich spectral bands and detailed ground information,but there is a relative lack of research on this new type of remote-sensing data.Although there are already some HSSR datasets for deep learning model training and testing,the data volume of these datasets is small,resulting in low classification accuracy and weak generalization ability of the trained models.In this paper,an HSSR dataset Luojia-HSSR is constructed based on aerial hyperspectral imagery of southern Shenyang City of Liaoning Province in China.To our knowledge,it is the largest HSSR dataset to date,with 6438 pairs of 256×256 sized samples(including 3480 pairs in the training set,2209 pairs in the test set,and 749 pairs in the validation set),covering area of 161 km2 with spatial resolution 0.75 m,249 Visible and Near-Infrared(VNIR)spectral bands,and corresponding to 23 classes of field-validated ground coverage.It is an ideal experimental data for spatial-spectral feature extraction.Furthermore,a new deep learning model 3D-HRNet for interpreting HSSR images is proposed.The conv-neck in HRNet is modified to better mine the spatial information of the images.Then,a 3D convolution module with attention mechanism is designed to capture the global-local fine spectral information simultaneously.Subsequently,the 3D convolution is inserted into the HRNet to optimize the performance.The experiments show that the 3D-HRNet model has good interpreting ability for the Luojia-HSSR dataset with the Frequency Weighted Intersection over Union(FWIoU)reaching 80.54%,indicating that the Luojia-HSSR dataset constructed in this paper and the proposed 3D-HRnet model have good applicable prospects for processing HSSR remote sensing images.展开更多
Artificial Intelligence(AI)Machine Learning(ML)technologies,particularly Deep Learning(DL),have demonstrated significant potential in the interpretation of Remote Sensing(RS)imagery,covering tasks such as scene classi...Artificial Intelligence(AI)Machine Learning(ML)technologies,particularly Deep Learning(DL),have demonstrated significant potential in the interpretation of Remote Sensing(RS)imagery,covering tasks such as scene classification,object detection,land-cover/land-use classification,change detection,and multi-view stereo reconstruction.Large-scale training samples are essential for ML/DL models to achieve optimal performance.However,the current organization of training samples is ad-hoc and vendor-specific,lacking an integrated approach that can effectively manage training samples from different vendors to meet the demands of various RS AI tasks.This article proposes a solution to address these challenges by designing and implementing LuoJiaSET,a large-scale training sample database system for intelligent interpretation of RS imagery.LuoJiaSET accommodates over five million training samples,providing support for cross-dataset queries and serving as a comprehensive training data store for RS AI model training and calibration.It overcomes challenges related to label semantic categories,structural heterogeneity in label representation,and interoperable data access.展开更多
In this study,a deep learning algorithm was applied to two-dimensional magnetotelluric(MT)data inversion.Compared with the traditional linear iterative inversion methods,the MT inversion method based on convolutional ...In this study,a deep learning algorithm was applied to two-dimensional magnetotelluric(MT)data inversion.Compared with the traditional linear iterative inversion methods,the MT inversion method based on convolutional neural networks(CNN)does not rely on the selection of the initial model parameters and does not fall into the local optima.Although the CNN inversion models can provide a clear electrical interface division,their inversion results may remain prone to abrupt electrical interfaces as opposed to the actual underground electrical situation.To solve this issue,a neural network with a residual network architecture(ResNet-50)was constructed in this study.With the apparent resistivity and phase pseudo-section data as the inputs and with the resistivity parameters of the geoelectric model as the training labels,the modified ResNet-50 model was trained end-to-end for producing samples according to the corresponding production strategy of the study area.Through experiments,the training of the ResNet-50 with the dice loss function effectively solved the issue of over-segmentation of the electrical interface by the cross-entropy function,avoided its abrupt inversion,and overcame the computational inefficiency of the traditional iterative methods.The proposed algorithm was validated against MT data measured from a geothermal field prospect in Huanggang,Hubei Province,which showed that the deep learning method has opened up broad prospects in the field of MT data inversion.展开更多
Ringing artifact degradations always appear in the deconvolution of geophysical data. To address this problem, we propose a postprocessing approach to suppress ringing artifacts that uses a novel anisotropic diffusion...Ringing artifact degradations always appear in the deconvolution of geophysical data. To address this problem, we propose a postprocessing approach to suppress ringing artifacts that uses a novel anisotropic diffusion based on a stationary wavelet transform （SWT） algorithm. In this paper, we discuss the ringing artifact suppression problem and analyze the characteristics of the deconvolu- tion ringing artifact. The deconvolution data containing ringing artifacts are decomposed into different SWT sub- bands for analysis, and a new multiscale adaptive aniso- tropic filter is developed to suppress these degradations. Finally, we demonstrate the performance of the proposed method and describe the experiments in detail.展开更多
With the increase in the coverage area of magnetotelluric data,three-dimensional magnetotelluric modeling in spherical coordinates and its differences with respect to traditional Cartesian modeling have gradually attr...With the increase in the coverage area of magnetotelluric data,three-dimensional magnetotelluric modeling in spherical coordinates and its differences with respect to traditional Cartesian modeling have gradually attracted attention.To fully understand the influence of the Earth’s curvature and map projection deformations on Cartesian modeling,qualitative and quantitative analyses based on realistic three-dimensional models need to be examined.Combined with five representative map projections,a type of model conversion method that transforms the original spherical electrical conductivity model to Cartesian coordinates is described in this study.The apparent resistivity differences between the spherical western United States electrical conductivity model and the corresponding five Cartesian models are then compared.The results show that the cylindrical equal distance map projection has the smallest error.A meridian convergence correction resulting from the deformation of the map projection is introduced to rotate the Cartesian impedance tensor from grid north to geographic north,which reduces differences from the spherical results.On the basis of the magnetotelluric field data,the applicability of the Cartesian coordinate system to western and contiguous United States models is quantitatively evaluated.Precise interpretations of the contiguous United States model were found to require spherical coordinates.展开更多
Compared to well-known geological hazards such as earthquakes, volcanic eruptions, landslides, and mudslides, the concept of “geomagnetic hazards” is much less familiar to people. Geomagnetic hazards are intense dis...Compared to well-known geological hazards such as earthquakes, volcanic eruptions, landslides, and mudslides, the concept of “geomagnetic hazards” is much less familiar to people. Geomagnetic hazards are intense disturbances in the Earth’ s magnetic field caused by solar flares, which can lead to geomagnetic storms. These storms ignite large geomagnetic induction currents and disrupt the global upper atmospheric and ionospheric environment.展开更多
Magnetotelluric(MT)inversion is an illposed problem and the standard way to address it is through regularization,by adding a stabilizing functional to the data objective functional in order to obtain a stable solution...Magnetotelluric(MT)inversion is an illposed problem and the standard way to address it is through regularization,by adding a stabilizing functional to the data objective functional in order to obtain a stable solution.The traditional stabilizing functionals,in which a low-order differential operator is used,yield a smooth solution that may not be appropriate when anomalies occur in block patterns.In some cases the focused imaging of a sharp electrical boundary is necessary.Even though various experiments have used stabilizing functionals that are suitable to obtain a clear and sharp boundary,such as the minimum support(MS)and the minimum gradient support(MGS)functionals,there are still some limitations in practice.In this paper,the minimum support gradient(MSG)is proposed as the stabilizing functional.Under the uniform regularization framework,a regularized inversion with a variety of stabilizing functionals is performed and the inversion results are compared.This study shows that MSG inversion can not only obtain a clearly focused inversion but also a quite stable and robust one.展开更多
This special edition of the Journal of Geodesy and Geoinformation Science(JGGS)presents a curated selection of papers that offer novel techniques in the realm of intelligent interpretation of remote sensing images.In ...This special edition of the Journal of Geodesy and Geoinformation Science(JGGS)presents a curated selection of papers that offer novel techniques in the realm of intelligent interpretation of remote sensing images.In recent years,tremendous efforts have been made across a wide spectrum including theory,models,algorithms,applications and datasets,etc.Such advancements have demonstrated their stunning performance in a variety of remote sensing topics spanning from general challenges like remote image classification and object detection,to highly specialized tasks such as autonomous situation awareness and disaster response.Moreover,such achievements have redefined disciplinary boundaries and also reshaped our education.展开更多
[Objectives]The study aims to discuss the effects of addition of arginine and glutamic acid or soybean phospholipid,vitamin E and yeast selenium in diet on the slaughter performance and meat quality of long(white)...[Objectives]The study aims to discuss the effects of addition of arginine and glutamic acid or soybean phospholipid,vitamin E and yeast selenium in diet on the slaughter performance and meat quality of long(white)×large(York)binary hybrid pigs.[Methods]27 long×large castrated hybrid boars with the body weight of(54.4±0.15)kg were randomly divided into 3 groups,with 3 replicates per group and 3 pigs per replicate.Group A was the control group,in which the pigs were fed basal diet;in group B,0.8%arginine and 0.60%glutamate were added to the basal diet;in group C,75 g of soybean phospholipid,20 g of vitamin E and 8 g of yeast selenium were added to every 100 kg of the basal diet.The trial period was 60 d.After the experiment was ended,one test pig with similar body weight was selected from each replicate for slaughter and meat determination.[Results]The average weight gain and eye muscle area of the pigs in group B were significantly higher than those in group C(P<0.05),and also showed an increasing trend compared with group A,but there was no statistically significant difference(P>0.05);there was no significant difference between group B or C and group A in the average weight gain and eye muscle area(P>0.05).There was no significant difference in other slaughter performance between the three groups(P>0.05).Besides,there was also no significant difference in the content of various amino acids,total amino acids and total umami amino acids between the three groups(P>0.05).The inosine content in the longissimus dorsi muscle and muscle cooking loss of binary hybrid pigs in group C were significantly better than those in group B(P<0.05),and also had a tendency to be better than those in group A,but there was no significant difference(P>0.05);there was no significant difference between group B or C and group A in the inosine content and muscle cooking loss of the pigs(P>0.05).In addition,there was no significant difference in other meat traits and chemical composition of the longissimus dorsi muscle between group B or C and group A(P>0.05).[Conclusions]The addition of arginine and glutamic acid or soybean phospholipid,vitamin E and yeast selenium in diet had no significant effect on the growth rate,slaughter performance and meat traits of long×large binary hybrid pigs.展开更多
Cratons have a long history of evolution.In this paper,applications of the magnetotelluric method used in the study of craton lithosphere over the past 30 years were reviewed,examining case studies of cratons in North...Cratons have a long history of evolution.In this paper,applications of the magnetotelluric method used in the study of craton lithosphere over the past 30 years were reviewed,examining case studies of cratons in North America,South America,Asia,Australia,and Africa.The nuclei of the Archean cratons,for example the Kalahari Craton and Rae Craton,are usually characterized by thick and highly resistive lithospheric roots.During or after the formation of the cratons,tectonothermal events,such as collision,mantle plume,and asthenosphere upwelling led to the formation of high-conductivity zones in the craton lithosphere,which could be attributed to the increased hydrogen content(of nominally anhydrous minerals),higher iron content,and formation of graphite films or sulfides along the grain boundary of minerals.These conductive zones are characterized by resistivity discontinuities in craton lithosphere.In particular,the conductive zones include(1)large-scale lithospheric mantle conductors beneath the Slave Craton,Gawler Craton,and central part of North China Craton(Trans-North China Orogen);(2)near-vertical high-conductivity zone associated with the fossil subduction zone beneath the Dharwar Craton and Slave Craton;and(3)regional lateral electrical discontinuities,such as a conductive anomaly under the Bushveld Complex of the Kaapvaal Craton.The eMoho refers to the electrical discontinuity in the crust-mantle boundary.In existing research,this has been detected under the condition of extremely high lithospheric resistivity with only a slight decrease in the lower crust,and in the case of a very thin conductive lower crust or the lack thereof.In the resistivity model,the unique"mushroom-like"lower crust-lithosphere mantle conductor and very thin lower crust layer of the North China Craton may represent lithosphere destruction and/or thinning.We also find that some of the cratons are still not well understood.Therefore,extensive three-dimensional inversion and joint interpretation of geochemical,geophysical,and geologic data are necessary to understand the tectonic evolutionary history of craton lithosphere.展开更多
The accumulated large amount of satellite magnetic data strengthens our capability of resolving the electrical conductivity of Earth’s mantle.To invert these satellite magnetic data,accurate and efficient forward mod...The accumulated large amount of satellite magnetic data strengthens our capability of resolving the electrical conductivity of Earth’s mantle.To invert these satellite magnetic data,accurate and efficient forward modeling solvers are needed.In this study,a new finite-element based forward modeling solver is developed to accurately and efficiently compute the induced electromagnetic field for a realistic 3D Earth.Firstly,the nodal-based finite element method with linear shape function on tetrahedral grid is used to assemble the final system of linear equations for the magnetic vector potential and electric scalar potential.The FGMRES solver with algebraic multigrid(AMG)preconditioner is used to quickly solve the final system of linear equations.The weighted moving least-square method is employed to accurately recover the electromagnetic field from the numerical solutions of magnetic vector and electric scalar potentials.Furthermore,a local mesh refinement technique is employed to improve the accuracy of the estimated electromagnetic field.At the end,two synthetic models are used to verify the accuracy and efficiency of our newly developed forward modeling solver.A realistic 3D Earth model is used to simulate the induced magnetic field at 450 and 200 km altitudes which are the planned flying altitudes of Macao’s geomagnetic satellites.The simulation indicates that(1)the amplitude of the mantle-induced magnetic field can reach 10–30 nT at 450 km altitude,which is 10–30%of the primary magnetic field.The induced magnetic field at 200 km altitude has larger amplitudes.These mantleinduced magnetic fields can be measured by Macao geomagnetic satellites;(2)the amplitude of the ocean-induced magnetic field can reach 5–30 nT at satellite altitudes,which needs to be carefully considered in the interpretation of satellite magnetic data.We are confident that our newly developed forward modeling solver will become a key tool for interpreting satellite magnetic data.展开更多
With the advent of Earth observation satellites,the remote sensing(RS)dataset has experienced exponential growth,significantly enhancing scientific research and applications.By early 2024,the global Earth observation ...With the advent of Earth observation satellites,the remote sensing(RS)dataset has experienced exponential growth,significantly enhancing scientific research and applications.By early 2024,the global Earth observation constellation comprises 1,379 satellites,with projections indicating an increase to 5,500 by 2033.On a daily basis,these satellites produce more than 20 TB of raw data,leading to an accumulation exceeding 500 PB[1].The surge in data volume poses challenges in storage,analysis,and management within the remote sensing domain.Foundation models like ChatGPT,SAM,and CLIP[2],present novel approaches that improve efficiency and drive innovation in remote sensing data processing.Leveraging extensive training datasets,these models demonstrate promise across a range of remote sensing tasks[3–5].展开更多
基金supported by the Chinese National Natural Science Foundation Projects[grant number 41901265]Major Program of the National Natural Science Foundation of China[grant number 92038301]supported in part by the Special Fund of Hubei Luojia Laboratory[grant number 220100028].
文摘The rapid processing,analysis,and mining of remote-sensing big data based on intelligent interpretation technology using remote-sensing cloud computing platforms(RS-CCPs)have recently become a new trend.The existing RS-CCPs mainly focus on developing and optimizing high-performance data storage and intelligent computing for common visual representation,which ignores remote sensing data characteristics such as large image size,large-scale change,multiple data channels,and geographic knowledge embedding,thus impairing computational efficiency and accuracy.We construct a LuoJiaAI platform composed of a standard large-scale sample database(LuoJiaSET)and a dedicated deep learning framework(LuoJiaNET)to achieve state-of-the-art performance on five typical remote sensing interpretation tasks,including scene classification,object detection,land-use classification,change detection,and multi-view 3D reconstruction.The details of the LuoJiaAI application experiment can be found at the white paper for LuoJiaAI industrial application.In addition,LuoJiaAI is an open-source RS-CCP that supports the latest Open Geospatial Consortium(OGC)standards for better developing and sharing Earth Artificial Intelligence(AI)algorithms and products on benchmark datasets.LuoJiaAI narrows the gap between the sample database and deep learning frameworks through a user-friendly data-framework collaboration mechanism,showing great potential in high-precision remote sensing mapping applications.
基金supported by the National Natural Science Foundation of China (No. 41204055)the National Basic Research Program of China (No. 2013CB733203)the Opening Project (No. SMIL-2014-06) of Hubei Subsurface Multi-Scale Imaging Lab (SMIL), China University of Geosciences, Wuhan, China
文摘A goal-oriented adaptive finite element（FE） method for solving 3D direct current（DC） resistivity modeling problem is presented. The model domain is subdivided into unstructured tetrahedral elements that allow for efficient local mesh refinement and flexible description of complex models. The elements that affect the solution at each receiver location are adaptively refined according to a goal-oriented posteriori error estimator using dual-error weighting approach. The FE method with adapting mesh can easily handle such structures at almost any level of complexity. The method is demonstrated on two synthetic resistivity models with analytical solutions and available results from integral equation method, so the errors can be quantified. The applicability of the numerical method is illustrated on a resistivity model with a topographic ridge. Numerical examples show that this method is flexible and accurate for geometrically complex situations.
基金supported by the Major Program of the National Natural Science Foundation of China[grant number 92038301]The research was also supported by the National Natural Science Foundation of China[grant number 41971295]+1 种基金the Foundation for Innovative Research Groups of the Natural Science Foundation of Hubei Province[grant number 2020CFA003]the Special Fund of Hubei Luojia Laboratory.
文摘High Spatial and Spectral Resolution(HSSR)remote-sensing images can provide rich spectral bands and detailed ground information,but there is a relative lack of research on this new type of remote-sensing data.Although there are already some HSSR datasets for deep learning model training and testing,the data volume of these datasets is small,resulting in low classification accuracy and weak generalization ability of the trained models.In this paper,an HSSR dataset Luojia-HSSR is constructed based on aerial hyperspectral imagery of southern Shenyang City of Liaoning Province in China.To our knowledge,it is the largest HSSR dataset to date,with 6438 pairs of 256×256 sized samples(including 3480 pairs in the training set,2209 pairs in the test set,and 749 pairs in the validation set),covering area of 161 km2 with spatial resolution 0.75 m,249 Visible and Near-Infrared(VNIR)spectral bands,and corresponding to 23 classes of field-validated ground coverage.It is an ideal experimental data for spatial-spectral feature extraction.Furthermore,a new deep learning model 3D-HRNet for interpreting HSSR images is proposed.The conv-neck in HRNet is modified to better mine the spatial information of the images.Then,a 3D convolution module with attention mechanism is designed to capture the global-local fine spectral information simultaneously.Subsequently,the 3D convolution is inserted into the HRNet to optimize the performance.The experiments show that the 3D-HRNet model has good interpreting ability for the Luojia-HSSR dataset with the Frequency Weighted Intersection over Union(FWIoU)reaching 80.54%,indicating that the Luojia-HSSR dataset constructed in this paper and the proposed 3D-HRnet model have good applicable prospects for processing HSSR remote sensing images.
基金supported by the National Natural Science Foundation of China[grant number 42071354]supported by the Fundamental Research Funds for the Central Universities[grant number 2042022dx0001]supported by the Fundamental Research Funds for the Central Universities[grant number WUT:223108001].
文摘Artificial Intelligence(AI)Machine Learning(ML)technologies,particularly Deep Learning(DL),have demonstrated significant potential in the interpretation of Remote Sensing(RS)imagery,covering tasks such as scene classification,object detection,land-cover/land-use classification,change detection,and multi-view stereo reconstruction.Large-scale training samples are essential for ML/DL models to achieve optimal performance.However,the current organization of training samples is ad-hoc and vendor-specific,lacking an integrated approach that can effectively manage training samples from different vendors to meet the demands of various RS AI tasks.This article proposes a solution to address these challenges by designing and implementing LuoJiaSET,a large-scale training sample database system for intelligent interpretation of RS imagery.LuoJiaSET accommodates over five million training samples,providing support for cross-dataset queries and serving as a comprehensive training data store for RS AI model training and calibration.It overcomes challenges related to label semantic categories,structural heterogeneity in label representation,and interoperable data access.
基金co-funded by the National Natural Science Foundation of China(No.42220104002,42174095,U1812402,and 41630317)the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(No.GLAB2022ZR10)the Fundamental Research Funds for the Central Universities,China University of Geosciences(Wuhan).
文摘In this study,a deep learning algorithm was applied to two-dimensional magnetotelluric(MT)data inversion.Compared with the traditional linear iterative inversion methods,the MT inversion method based on convolutional neural networks(CNN)does not rely on the selection of the initial model parameters and does not fall into the local optima.Although the CNN inversion models can provide a clear electrical interface division,their inversion results may remain prone to abrupt electrical interfaces as opposed to the actual underground electrical situation.To solve this issue,a neural network with a residual network architecture(ResNet-50)was constructed in this study.With the apparent resistivity and phase pseudo-section data as the inputs and with the resistivity parameters of the geoelectric model as the training labels,the modified ResNet-50 model was trained end-to-end for producing samples according to the corresponding production strategy of the study area.Through experiments,the training of the ResNet-50 with the dice loss function effectively solved the issue of over-segmentation of the electrical interface by the cross-entropy function,avoided its abrupt inversion,and overcame the computational inefficiency of the traditional iterative methods.The proposed algorithm was validated against MT data measured from a geothermal field prospect in Huanggang,Hubei Province,which showed that the deep learning method has opened up broad prospects in the field of MT data inversion.
文摘Ringing artifact degradations always appear in the deconvolution of geophysical data. To address this problem, we propose a postprocessing approach to suppress ringing artifacts that uses a novel anisotropic diffusion based on a stationary wavelet transform （SWT） algorithm. In this paper, we discuss the ringing artifact suppression problem and analyze the characteristics of the deconvolu- tion ringing artifact. The deconvolution data containing ringing artifacts are decomposed into different SWT sub- bands for analysis, and a new multiscale adaptive aniso- tropic filter is developed to suppress these degradations. Finally, we demonstrate the performance of the proposed method and describe the experiments in detail.
基金the National Natural Science Foundation of China(Nos.42220104002,42104073,and 41630317).
文摘With the increase in the coverage area of magnetotelluric data,three-dimensional magnetotelluric modeling in spherical coordinates and its differences with respect to traditional Cartesian modeling have gradually attracted attention.To fully understand the influence of the Earth’s curvature and map projection deformations on Cartesian modeling,qualitative and quantitative analyses based on realistic three-dimensional models need to be examined.Combined with five representative map projections,a type of model conversion method that transforms the original spherical electrical conductivity model to Cartesian coordinates is described in this study.The apparent resistivity differences between the spherical western United States electrical conductivity model and the corresponding five Cartesian models are then compared.The results show that the cylindrical equal distance map projection has the smallest error.A meridian convergence correction resulting from the deformation of the map projection is introduced to rotate the Cartesian impedance tensor from grid north to geographic north,which reduces differences from the spherical results.On the basis of the magnetotelluric field data,the applicability of the Cartesian coordinate system to western and contiguous United States models is quantitatively evaluated.Precise interpretations of the contiguous United States model were found to require spherical coordinates.
文摘Compared to well-known geological hazards such as earthquakes, volcanic eruptions, landslides, and mudslides, the concept of “geomagnetic hazards” is much less familiar to people. Geomagnetic hazards are intense disturbances in the Earth’ s magnetic field caused by solar flares, which can lead to geomagnetic storms. These storms ignite large geomagnetic induction currents and disrupt the global upper atmospheric and ionospheric environment.
基金the National Natural Science Foundation of China(No.41630317)the National Key Research and Development Program of China(No.2017YFC0602405).
文摘Magnetotelluric(MT)inversion is an illposed problem and the standard way to address it is through regularization,by adding a stabilizing functional to the data objective functional in order to obtain a stable solution.The traditional stabilizing functionals,in which a low-order differential operator is used,yield a smooth solution that may not be appropriate when anomalies occur in block patterns.In some cases the focused imaging of a sharp electrical boundary is necessary.Even though various experiments have used stabilizing functionals that are suitable to obtain a clear and sharp boundary,such as the minimum support(MS)and the minimum gradient support(MGS)functionals,there are still some limitations in practice.In this paper,the minimum support gradient(MSG)is proposed as the stabilizing functional.Under the uniform regularization framework,a regularized inversion with a variety of stabilizing functionals is performed and the inversion results are compared.This study shows that MSG inversion can not only obtain a clearly focused inversion but also a quite stable and robust one.
文摘This special edition of the Journal of Geodesy and Geoinformation Science(JGGS)presents a curated selection of papers that offer novel techniques in the realm of intelligent interpretation of remote sensing images.In recent years,tremendous efforts have been made across a wide spectrum including theory,models,algorithms,applications and datasets,etc.Such advancements have demonstrated their stunning performance in a variety of remote sensing topics spanning from general challenges like remote image classification and object detection,to highly specialized tasks such as autonomous situation awareness and disaster response.Moreover,such achievements have redefined disciplinary boundaries and also reshaped our education.
基金Supported by Self-funded Project of Agricultural Science and Technology of Guangxi(Z2022114).
文摘[Objectives]The study aims to discuss the effects of addition of arginine and glutamic acid or soybean phospholipid,vitamin E and yeast selenium in diet on the slaughter performance and meat quality of long(white)×large(York)binary hybrid pigs.[Methods]27 long×large castrated hybrid boars with the body weight of(54.4±0.15)kg were randomly divided into 3 groups,with 3 replicates per group and 3 pigs per replicate.Group A was the control group,in which the pigs were fed basal diet;in group B,0.8%arginine and 0.60%glutamate were added to the basal diet;in group C,75 g of soybean phospholipid,20 g of vitamin E and 8 g of yeast selenium were added to every 100 kg of the basal diet.The trial period was 60 d.After the experiment was ended,one test pig with similar body weight was selected from each replicate for slaughter and meat determination.[Results]The average weight gain and eye muscle area of the pigs in group B were significantly higher than those in group C(P<0.05),and also showed an increasing trend compared with group A,but there was no statistically significant difference(P>0.05);there was no significant difference between group B or C and group A in the average weight gain and eye muscle area(P>0.05).There was no significant difference in other slaughter performance between the three groups(P>0.05).Besides,there was also no significant difference in the content of various amino acids,total amino acids and total umami amino acids between the three groups(P>0.05).The inosine content in the longissimus dorsi muscle and muscle cooking loss of binary hybrid pigs in group C were significantly better than those in group B(P<0.05),and also had a tendency to be better than those in group A,but there was no significant difference(P>0.05);there was no significant difference between group B or C and group A in the inosine content and muscle cooking loss of the pigs(P>0.05).In addition,there was no significant difference in other meat traits and chemical composition of the longissimus dorsi muscle between group B or C and group A(P>0.05).[Conclusions]The addition of arginine and glutamic acid or soybean phospholipid,vitamin E and yeast selenium in diet had no significant effect on the growth rate,slaughter performance and meat traits of long×large binary hybrid pigs.
基金supported by the National Natural Science Foundation of China(Grant Nos.41630317 and 41474055)the National Key Research and Development Program of China(Grant No.2017YFC0602405)。
文摘Cratons have a long history of evolution.In this paper,applications of the magnetotelluric method used in the study of craton lithosphere over the past 30 years were reviewed,examining case studies of cratons in North America,South America,Asia,Australia,and Africa.The nuclei of the Archean cratons,for example the Kalahari Craton and Rae Craton,are usually characterized by thick and highly resistive lithospheric roots.During or after the formation of the cratons,tectonothermal events,such as collision,mantle plume,and asthenosphere upwelling led to the formation of high-conductivity zones in the craton lithosphere,which could be attributed to the increased hydrogen content(of nominally anhydrous minerals),higher iron content,and formation of graphite films or sulfides along the grain boundary of minerals.These conductive zones are characterized by resistivity discontinuities in craton lithosphere.In particular,the conductive zones include(1)large-scale lithospheric mantle conductors beneath the Slave Craton,Gawler Craton,and central part of North China Craton(Trans-North China Orogen);(2)near-vertical high-conductivity zone associated with the fossil subduction zone beneath the Dharwar Craton and Slave Craton;and(3)regional lateral electrical discontinuities,such as a conductive anomaly under the Bushveld Complex of the Kaapvaal Craton.The eMoho refers to the electrical discontinuity in the crust-mantle boundary.In existing research,this has been detected under the condition of extremely high lithospheric resistivity with only a slight decrease in the lower crust,and in the case of a very thin conductive lower crust or the lack thereof.In the resistivity model,the unique"mushroom-like"lower crust-lithosphere mantle conductor and very thin lower crust layer of the North China Craton may represent lithosphere destruction and/or thinning.We also find that some of the cratons are still not well understood.Therefore,extensive three-dimensional inversion and joint interpretation of geochemical,geophysical,and geologic data are necessary to understand the tectonic evolutionary history of craton lithosphere.
基金supported by the National Natural Science Foundation of China(Grant Nos.72088101,41922027,41830107,41811530010)Innovation-Driven Project of Central South University(Grant No.2020CX0012)+1 种基金the National Natural Science Foundation of Hunan Province of China(Grant No.2019JJ20032)Macao Foundation and the pre-research project on Civil Aerospace Technologies funded by China’s National Space Administration(Grant Nos.D020308,D020303).
文摘The accumulated large amount of satellite magnetic data strengthens our capability of resolving the electrical conductivity of Earth’s mantle.To invert these satellite magnetic data,accurate and efficient forward modeling solvers are needed.In this study,a new finite-element based forward modeling solver is developed to accurately and efficiently compute the induced electromagnetic field for a realistic 3D Earth.Firstly,the nodal-based finite element method with linear shape function on tetrahedral grid is used to assemble the final system of linear equations for the magnetic vector potential and electric scalar potential.The FGMRES solver with algebraic multigrid(AMG)preconditioner is used to quickly solve the final system of linear equations.The weighted moving least-square method is employed to accurately recover the electromagnetic field from the numerical solutions of magnetic vector and electric scalar potentials.Furthermore,a local mesh refinement technique is employed to improve the accuracy of the estimated electromagnetic field.At the end,two synthetic models are used to verify the accuracy and efficiency of our newly developed forward modeling solver.A realistic 3D Earth model is used to simulate the induced magnetic field at 450 and 200 km altitudes which are the planned flying altitudes of Macao’s geomagnetic satellites.The simulation indicates that(1)the amplitude of the mantle-induced magnetic field can reach 10–30 nT at 450 km altitude,which is 10–30%of the primary magnetic field.The induced magnetic field at 200 km altitude has larger amplitudes.These mantleinduced magnetic fields can be measured by Macao geomagnetic satellites;(2)the amplitude of the ocean-induced magnetic field can reach 5–30 nT at satellite altitudes,which needs to be carefully considered in the interpretation of satellite magnetic data.We are confident that our newly developed forward modeling solver will become a key tool for interpreting satellite magnetic data.
基金supported by the Key Research and Development Program of Hubei Province(2023BAB173)the State Key Laboratory of Geo-Information Engineering(SKLGIE2021-M-3-1)+2 种基金the National Natural Science Foundation of China(41901265)Major Program of the National Natural Science Foundation of China(92038301)supported in part by the Special Fund of Hubei Luojia Laboratory(220100028).
文摘With the advent of Earth observation satellites,the remote sensing(RS)dataset has experienced exponential growth,significantly enhancing scientific research and applications.By early 2024,the global Earth observation constellation comprises 1,379 satellites,with projections indicating an increase to 5,500 by 2033.On a daily basis,these satellites produce more than 20 TB of raw data,leading to an accumulation exceeding 500 PB[1].The surge in data volume poses challenges in storage,analysis,and management within the remote sensing domain.Foundation models like ChatGPT,SAM,and CLIP[2],present novel approaches that improve efficiency and drive innovation in remote sensing data processing.Leveraging extensive training datasets,these models demonstrate promise across a range of remote sensing tasks[3–5].
