This paper presents the TDS-1 GNSS reflectometry wind Geophysical Model Function(GMF)response to GPS block types.The observables were extracted from Delay Doppler Maps(DDMs)after taking the receiver antenna gains effe...This paper presents the TDS-1 GNSS reflectometry wind Geophysical Model Function(GMF)response to GPS block types.The observables were extracted from Delay Doppler Maps(DDMs)after taking the receiver antenna gains effects and GNSS-R geometry effects into account.Since the DDM is affected by GPS EffectiveIsotropic Radiated Power(EIRP),we first investigate the sensitivity of observables to the GPS block.Additionally,the observables at high SNRs are more sensitive to wind speed,but the spatial coverage at high signal to noise ratios(SNRs)is lower,while DDMs at low SNRs have the opposite characteristics.To balance the accuracy and spatial coverage,the DDM datasets are divided into two parts:high SNR(>0 dB)and low SNR(>−10 dB and≤0 dB)to develop wind GMF.Then,the influences of GPS block on wind speed retrieval both at high and low SNR is analyzed.Results show that the block types have impacts on wind GMF and the use of a prior GPS block can contribute to a better wind speed retrieval both at high and low SNR.Compared with ASCAT,the Root Mean Square Error(RMSE)value of wind speed retrieval at high and low SNR are 2.19 m/s and 3.13 m/s,respectively,when all TDS data are processed without distinguishing GPS block types.However,if the TDS data are separately processed and used to develop wind GMF through different blocks,both the accuracy and correlation coefficient can be improved to some extent.Finally,the influence of significant height of the swell(Hs)on SNR observables is analyzed,and it is demonstrated that there is no obvious linear or nonlinear relationship between them.展开更多
The geophysical model function （GMF） describes the relationship between a backscattering and a sea surface wind, and enables a wind vector retrieval from backscattering measurements. It is clear that the GMF plays a...The geophysical model function （GMF） describes the relationship between a backscattering and a sea surface wind, and enables a wind vector retrieval from backscattering measurements. It is clear that the GMF plays an important role in an ocean wind vector retrieval. The performance of the existing Ku-band model function QSCAT-1 is considered to be effective at low and moderate wind speed ranges. However, in the conditions of higher wind speeds, the existing algorithms diverge alarmingly, owing to the lack of in situ data required for developing the GMF for the high wind conditions, the QSCAT-1 appears to overestimate the a0, which results in underestimating the wind speeds. Several match-up QuikSCAT and special sensor microwave/imager （SSM/I） wind speed measurements of the typhoons occurring in the west Pacific Ocean are analyzed. The results show that the SSM/I wind exhibits better agreement with the ＂best track＂ analysis wind speed than the QuikSCAT wind retrieved using QSCAT-1. On the basis of this evaluation, a correction of the QSCAT-1 model function for wind speed above 16 m/s is proposed, which uses the collocated SSM/I and QuikSCAT measurements as a training set, and a neural network approach as a multiple nonlinear regression technologytechnology.In order to validate the revised GMF for high winds, the modified GMF was applied to the QuikSCAT observations of Hurricane IOKE. The wind estimated by the QuikSCAT for Typhoon IOKE in 2006 was improved with the maximum wind speed reaching 55 m/s. An error analysis was performed using the wind fields from the Holland model as the surface truth. The results show an improved agreement with the Holland model wind when compared with the wind estimated using the QSCAT-1. However, large bias still existed, indicating that the effects of rain must be considered for further improvement.展开更多
It is one of the most important part to build an accurate gravity model in geophysical exploration.Traditional gravity modelling is usually based on grid method,such as difference method and finite element method wide...It is one of the most important part to build an accurate gravity model in geophysical exploration.Traditional gravity modelling is usually based on grid method,such as difference method and finite element method widely used.Due to self-adaptability lack of division meshes and the difficulty of high-dimensional calculation.展开更多
2021年7月发射的风云三号E星(FY-3E)是世界首颗民用晨昏轨道气象卫星,其搭载的WindRAD双频测风雷达具有全球海面风场探测能力。本文首先基于FY-3E/WindRAD L1级观测资料,研究了雷达海面后向散射和风场之间的非线性关系,分别建立了适用于...2021年7月发射的风云三号E星(FY-3E)是世界首颗民用晨昏轨道气象卫星,其搭载的WindRAD双频测风雷达具有全球海面风场探测能力。本文首先基于FY-3E/WindRAD L1级观测资料,研究了雷达海面后向散射和风场之间的非线性关系,分别建立了适用于C和Ku波段VV/HH极化的地球物理模式函数(GMF)。随后,结合最大似然估计法(MLE)对WindRAD散射计探测资料进行风场反演。利用海洋浮标、中法海洋卫星散射计(CSCAT)和美国国家环境预报中心(NCEP)模式风场资料对WindRAD反演风场进行验证。结果显示:WindRAD反演风速与浮标风速偏差约为0.2 m s^(-1),均方根误差(RMSE)在1.13~1.44 m s^(-1)之间,优于2 m s^(-1)的业务化应用的风速精度要求;两者风向偏差在1.4°~3.0°之间,RMSE在25.3°~30.1°之间。WindRAD和CSCAT风场具有较好的一致性,风速RMSE在1.37~1.6 m s^(-1)之间,风向RMSE在22.9°~25.9°之间。WindRAD和NCEP模式风速RMSE在1.87~2.23 m s^(-1)之间,风向RMSE在22.4°~27.1°之间。研究表明WindRAD散射计C和Ku波段VV/HH极化反演风场均具有较高的精度,充分显示了WindRAD载荷在全球海面风场探测方面的应用潜力和价值。展开更多
With the efficient and intelligent development of computer-based big data processing,applying machine learning methods to the processing and interpretation of logging data in the field of geophysical well logging has ...With the efficient and intelligent development of computer-based big data processing,applying machine learning methods to the processing and interpretation of logging data in the field of geophysical well logging has broad potential for improving production efficiency.Currently,the Jiyuan Oilfield in the Ordos Basin relies mainly on manual reprocessing and interpretation of old well logging data to identify different fluid types in low-contrast reservoirs,guiding subsequent production work.This study uses well logging data from the Chang 1 reservoir,partitioning the dataset based on individual wells for model training and testing.A deep learning model for intelligent reservoir fluid identification was constructed by incorporating the focal loss function.Comparative validations with five other models,including logistic regression(LR),naive Bayes(NB),gradient boosting decision trees(GBDT),random forest(RF),and support vector machine(SVM),show that this model demonstrates superior identification performance and significantly improves the accuracy of identifying oil-bearing fluids.Mutual information analysis reveals the model's differential dependency on various logging parameters for reservoir fluid identification.This model provides important references and a basis for conducting regional studies and revisiting old wells,demonstrating practical value that can be widely applied.展开更多
The successful launch of the Cyclone Global Navigation Satellite System(CYGNSS)has opened an unprecedented opportunity for rapid observation of Wind Speed(WS)across vast oceanic regions.However,considerable debate per...The successful launch of the Cyclone Global Navigation Satellite System(CYGNSS)has opened an unprecedented opportunity for rapid observation of Wind Speed(WS)across vast oceanic regions.However,considerable debate persists over the choice of input feature parameters for WS retrieval models based on CYGNSS data,and enhancing the accuracy of WS retrieval is a focal point of current research.To address the aforementioned problems,this study establishes a comprehensive CYGNSS wind speed retrieval feature parameter set through an in-depth analysis of CYGNSS data,thereby providing a reference and basis for selecting input features for WS retrieval models.Through this analysis,we identified three crucial observational features:the normalized bistatic radar cross section,leading edge slope,and signal-to-noise ratio.Using these features,we developed a WS retrieval model based on the geophysical model function for CYGNSS data.Furthermore,acknowledging the intrinsic interconnection between wind and wave dynamics,we incorporate significant wave height into the WS retrieval model to further improve the WS retrieval accuracy.Comparative assessments with datasets from the European Centre for Medium-Range Weather Forecasts,the Chinese-French Oceanography Satellite Scatterometer,and buoy WS data underscore the high accuracy of our model,demonstrating its utility as a valuable tool for research in ocean dynamics and marine environmental prediction.展开更多
基金supported by the Funds for Creative Research Groups of China[Grant no.41721003]the National Natural Science Foundation of China[Grant nos.41825009 and 41774034].
文摘This paper presents the TDS-1 GNSS reflectometry wind Geophysical Model Function(GMF)response to GPS block types.The observables were extracted from Delay Doppler Maps(DDMs)after taking the receiver antenna gains effects and GNSS-R geometry effects into account.Since the DDM is affected by GPS EffectiveIsotropic Radiated Power(EIRP),we first investigate the sensitivity of observables to the GPS block.Additionally,the observables at high SNRs are more sensitive to wind speed,but the spatial coverage at high signal to noise ratios(SNRs)is lower,while DDMs at low SNRs have the opposite characteristics.To balance the accuracy and spatial coverage,the DDM datasets are divided into two parts:high SNR(>0 dB)and low SNR(>−10 dB and≤0 dB)to develop wind GMF.Then,the influences of GPS block on wind speed retrieval both at high and low SNR is analyzed.Results show that the block types have impacts on wind GMF and the use of a prior GPS block can contribute to a better wind speed retrieval both at high and low SNR.Compared with ASCAT,the Root Mean Square Error(RMSE)value of wind speed retrieval at high and low SNR are 2.19 m/s and 3.13 m/s,respectively,when all TDS data are processed without distinguishing GPS block types.However,if the TDS data are separately processed and used to develop wind GMF through different blocks,both the accuracy and correlation coefficient can be improved to some extent.Finally,the influence of significant height of the swell(Hs)on SNR observables is analyzed,and it is demonstrated that there is no obvious linear or nonlinear relationship between them.
基金The National Natural Science Foundation of China under contract No.41106152the National Science and Technology Support Program under contract No.2013BAD13B01+3 种基金the National High Technology Research and Development Program(863 Program)of China under contract No.2013AA09A505the International Science and Technology Cooperation Program of China under contract No.2011DFA22260the National High Technology Industrialization Project under contract No.[2012]2083the Marine Public Projects of China under contract Nos 201105032,201305032 and 201105002-07
文摘The geophysical model function （GMF） describes the relationship between a backscattering and a sea surface wind, and enables a wind vector retrieval from backscattering measurements. It is clear that the GMF plays an important role in an ocean wind vector retrieval. The performance of the existing Ku-band model function QSCAT-1 is considered to be effective at low and moderate wind speed ranges. However, in the conditions of higher wind speeds, the existing algorithms diverge alarmingly, owing to the lack of in situ data required for developing the GMF for the high wind conditions, the QSCAT-1 appears to overestimate the a0, which results in underestimating the wind speeds. Several match-up QuikSCAT and special sensor microwave/imager （SSM/I） wind speed measurements of the typhoons occurring in the west Pacific Ocean are analyzed. The results show that the SSM/I wind exhibits better agreement with the ＂best track＂ analysis wind speed than the QuikSCAT wind retrieved using QSCAT-1. On the basis of this evaluation, a correction of the QSCAT-1 model function for wind speed above 16 m/s is proposed, which uses the collocated SSM/I and QuikSCAT measurements as a training set, and a neural network approach as a multiple nonlinear regression technologytechnology.In order to validate the revised GMF for high winds, the modified GMF was applied to the QuikSCAT observations of Hurricane IOKE. The wind estimated by the QuikSCAT for Typhoon IOKE in 2006 was improved with the maximum wind speed reaching 55 m/s. An error analysis was performed using the wind fields from the Holland model as the surface truth. The results show an improved agreement with the Holland model wind when compared with the wind estimated using the QSCAT-1. However, large bias still existed, indicating that the effects of rain must be considered for further improvement.
基金provided by China Geological Survey with the project(Nos.DD20190707,DD20190012)the Fundamental Research Funds for China Central public research Institutes with the project(No.JKY202014)
文摘It is one of the most important part to build an accurate gravity model in geophysical exploration.Traditional gravity modelling is usually based on grid method,such as difference method and finite element method widely used.Due to self-adaptability lack of division meshes and the difficulty of high-dimensional calculation.
文摘2021年7月发射的风云三号E星(FY-3E)是世界首颗民用晨昏轨道气象卫星,其搭载的WindRAD双频测风雷达具有全球海面风场探测能力。本文首先基于FY-3E/WindRAD L1级观测资料,研究了雷达海面后向散射和风场之间的非线性关系,分别建立了适用于C和Ku波段VV/HH极化的地球物理模式函数(GMF)。随后,结合最大似然估计法(MLE)对WindRAD散射计探测资料进行风场反演。利用海洋浮标、中法海洋卫星散射计(CSCAT)和美国国家环境预报中心(NCEP)模式风场资料对WindRAD反演风场进行验证。结果显示:WindRAD反演风速与浮标风速偏差约为0.2 m s^(-1),均方根误差(RMSE)在1.13~1.44 m s^(-1)之间,优于2 m s^(-1)的业务化应用的风速精度要求;两者风向偏差在1.4°~3.0°之间,RMSE在25.3°~30.1°之间。WindRAD和CSCAT风场具有较好的一致性,风速RMSE在1.37~1.6 m s^(-1)之间,风向RMSE在22.9°~25.9°之间。WindRAD和NCEP模式风速RMSE在1.87~2.23 m s^(-1)之间,风向RMSE在22.4°~27.1°之间。研究表明WindRAD散射计C和Ku波段VV/HH极化反演风场均具有较高的精度,充分显示了WindRAD载荷在全球海面风场探测方面的应用潜力和价值。
基金supported by a project of the Shaanxi Youth Science and Technology Star(2021KJXX-87)public welfare geological survey projects of Shaanxi Institute of Geologic Survey(20180301,201918 and 202103)。
文摘With the efficient and intelligent development of computer-based big data processing,applying machine learning methods to the processing and interpretation of logging data in the field of geophysical well logging has broad potential for improving production efficiency.Currently,the Jiyuan Oilfield in the Ordos Basin relies mainly on manual reprocessing and interpretation of old well logging data to identify different fluid types in low-contrast reservoirs,guiding subsequent production work.This study uses well logging data from the Chang 1 reservoir,partitioning the dataset based on individual wells for model training and testing.A deep learning model for intelligent reservoir fluid identification was constructed by incorporating the focal loss function.Comparative validations with five other models,including logistic regression(LR),naive Bayes(NB),gradient boosting decision trees(GBDT),random forest(RF),and support vector machine(SVM),show that this model demonstrates superior identification performance and significantly improves the accuracy of identifying oil-bearing fluids.Mutual information analysis reveals the model's differential dependency on various logging parameters for reservoir fluid identification.This model provides important references and a basis for conducting regional studies and revisiting old wells,demonstrating practical value that can be widely applied.
基金The Fund of Key Laboratory of Space Ocean Remote Sensing and Application,Ministry of Natural Resources under contract No.2023CFO016the National Natural Science Foundation of China under contract No.61931025the Key Program of Joint Fund of the National Natural Science Foundation of China and Shandong Province under contract No.U22A20586.
文摘The successful launch of the Cyclone Global Navigation Satellite System(CYGNSS)has opened an unprecedented opportunity for rapid observation of Wind Speed(WS)across vast oceanic regions.However,considerable debate persists over the choice of input feature parameters for WS retrieval models based on CYGNSS data,and enhancing the accuracy of WS retrieval is a focal point of current research.To address the aforementioned problems,this study establishes a comprehensive CYGNSS wind speed retrieval feature parameter set through an in-depth analysis of CYGNSS data,thereby providing a reference and basis for selecting input features for WS retrieval models.Through this analysis,we identified three crucial observational features:the normalized bistatic radar cross section,leading edge slope,and signal-to-noise ratio.Using these features,we developed a WS retrieval model based on the geophysical model function for CYGNSS data.Furthermore,acknowledging the intrinsic interconnection between wind and wave dynamics,we incorporate significant wave height into the WS retrieval model to further improve the WS retrieval accuracy.Comparative assessments with datasets from the European Centre for Medium-Range Weather Forecasts,the Chinese-French Oceanography Satellite Scatterometer,and buoy WS data underscore the high accuracy of our model,demonstrating its utility as a valuable tool for research in ocean dynamics and marine environmental prediction.
