The investigations of physical attributes of oceans,including parameters such as heat flow and bathymetry,have garnered substantial attention and are particularly valuable for examining Earth’s thermal structures and...The investigations of physical attributes of oceans,including parameters such as heat flow and bathymetry,have garnered substantial attention and are particularly valuable for examining Earth’s thermal structures and dynamic processes.Nevertheless,classical plate cooling models exhibit disparities when predicting observed heat flow and seafloor depth for extremely young and old lithospheres.Furthermore,a comprehensive analysis of global heat flow predictions and regional ocean heat flow or bathymetry data with physical models has been lacking.In this study,we employed power-law models derived from the singularity theory of fractal density to meticulously fit the latest ocean heat flow and bathymetry.Notably,power-law models offer distinct advantages over traditional plate cooling models,showcasing robust self-similarity,scale invariance,or scaling properties,and providing a better fit to observed data.The outcomes of our singularity analysis concerning heat flow and bathymetry across diverse oceanic regions exhibit a degree of consistency with the global ocean spreading rate model.In addition,we applied the similarity method to predict a higher resolution(0.1°×0.1°)global heat flow map based on the most recent heat flow data and geological/geophysical observables refined through linear correlation analysis.Regions displaying significant disparities between predicted and observed heat flow are closely linked to hydrothermal vent fields and active structures.Finally,combining the actual bathymetry and predicted heat flow with the power-law models allows for the quantitative and comprehensive detection of anomalous regions of ocean subsidence and heat flow,which deviate from traditional plate cooling models.The anomalous regions of subsidence and heat flow show different degrees of anisotropy,providing new ideas and clues for further analysis of ocean topography or hydrothermal circulation of mid-ocean ridges.展开更多
The prediction of bathymetry has advanced significantly with the development of satellite altimetry.However,the majority of its data originate from marine gravity anomaly.In this study,based on the expression of verti...The prediction of bathymetry has advanced significantly with the development of satellite altimetry.However,the majority of its data originate from marine gravity anomaly.In this study,based on the expression of vertical gravity gradient(VGG)of a rectangular prism,the governing equations for determining sea depths to invert bathymetry.The governing equation is solved by linearization through an iterative process,and numerical simulations verify its algorithm and its stability.We also study the processing methods of different interference errors.The regularization method improves the stability of the inversion process for errors.A piecewise bilinear interpolation function roughly replaces the low-frequency error,and numerical simulations show that the accuracy can be improved by 41.2%after this treatment.For variable ocean crust density,simulation simulations verify that the root-mean-square(RMS)error of prediction is approximately 5 m for the sea depth of 6 km if density is chosen as the average one.Finally,two test regions in the South China Sea are predicted and compared with ship soundings data,RMS errors of predictions are 71.1 m and 91.4 m,respectively.展开更多
Understanding the topographic patterns of the seafloor is a very important part of understanding our planet.Although the science involved in bathymetric surveying has advanced much over the decades,less than 20%of the...Understanding the topographic patterns of the seafloor is a very important part of understanding our planet.Although the science involved in bathymetric surveying has advanced much over the decades,less than 20%of the seafloor has been precisely modeled to date,and there is an urgent need to improve the accuracy and reduce the uncertainty of underwater survey data.In this study,we introduce a pretrained visual geometry group network(VGGNet)method based on deep learning.To apply this method,we input gravity anomaly data derived from ship measurements and satellite altimetry into the model and correct the latter,which has a larger spatial coverage,based on the former,which is considered the true value and is more accurate.After obtaining the corrected high-precision gravity model,it is inverted to the corresponding bathymetric model by applying the gravity-depth correlation.We choose four data pairs collected from different environments,i.e.,the Southern Ocean,Pacific Ocean,Atlantic Ocean and Caribbean Sea,to evaluate the topographic correction results of the model.The experiments show that the coefficient of determination(R~2)reaches 0.834 among the results of the four experimental groups,signifying a high correlation.The standard deviation and normalized root mean square error are also evaluated,and the accuracy of their performance improved by up to 24.2%compared with similar research done in recent years.The evaluation of the R^(2) values at different water depths shows that our model can achieve performance results above 0.90 at certain water depths and can also significantly improve results from mid-water depths when compared to previous research.Finally,the bathymetry corrected by our model is able to show an accuracy improvement level of more than 21%within 1%of the total water depths,which is sufficient to prove that the VGGNet-based method has the ability to perform a gravity-bathymetry correction and achieve outstanding results.展开更多
The utilization of sequence stratigraphic concepts in identifying sands and their spatial continuity in distinct gross depositional settings is key,especially in frontier settings where data paucity is a common challe...The utilization of sequence stratigraphic concepts in identifying sands and their spatial continuity in distinct gross depositional settings is key,especially in frontier settings where data paucity is a common challenge.In the Baka field,onshore Niger Delta,detailed reservoir correlation guided by sequence stratigraphic framework analysis showed the distribution of sand and shale units constituting reservoirseal pairs(RSP)correlatable across the field.Within the 3rd-order packages,it is observed that the lowstand systems tract(LST)and highstand systems tract(HST)contain more RSPs and thicker 4th-and 5th-order sands than the transgressive systems tract(TST).In terms of bathymetry,it is noted that irrespective of systems tracts,the RSP Index(RI)decreases from the proximal shallow/inner shelf settings to the more distal outer shelf areas.Amongst all three systems tracts,intervals interpreted as lowstand prograding complexes contain the best developed sands and highest RSP.Sand development within the LSTs has been controlled by a pronounced growth fault regime accompanied by high subsidence and sedimentation rates.This is linked to the basinward migration of the sands during prolonged sea-level fall,creating significant accommodation space for sand deposition.On the other hand,the TSTs known to mark periods of progressive sea-level rise and landward migration of sandy facies,show thinner sands enclosed in much thicker,laterally extensive,and better-preserved deeper marine shales.Interpreted seismic sections indicate intense growth faulting and channelization that influenced the syn-and postdepositional development of the sand packages across the field.The initial timing of deformation of subregional faults in this area coincides with periods of abrupt falls in sea level.This approach could be useful for predicting sand-prone areas in frontier fields as well as possible reservoir-seal parameters required for some aspects of petroleum system analysis and quick-look volume estimation.展开更多
冰、云和陆地高程卫星2号(ice,cloud and land elevation satellite-2,ICESat-2)搭载了先进地形激光测高系统(advanced topographic laser altimeter system,ATLAS),该系统采用光子计数探测模式,可获取高精度的地表高程信息。ATLAS使用5...冰、云和陆地高程卫星2号(ice,cloud and land elevation satellite-2,ICESat-2)搭载了先进地形激光测高系统(advanced topographic laser altimeter system,ATLAS),该系统采用光子计数探测模式,可获取高精度的地表高程信息。ATLAS使用532 nm波段激光器,具备一定的水深探测能力,为星载数据近岸水深探测提供了新手段。利用ICESat-2ATLAS数据进行测深,关键问题是如何实现不同区域、不同环境、不同密度分布条件下信号光子的自动探测与提取。为解决此问题,提出了一种基于自适应空间滤波的ICESat-2数据测深方法,该方法首先将水面以上、水面和水下区域的原始光子进行分离,随后基于可变椭圆密度滤波核精确提取水面与水底光子,椭圆密度滤波核参数根据不同水深下光子密度的分布特点自适应确定,最终实现浅海水深测量。实验结果表明,所提方法获取的ICESat-2测深结果与机载激光雷达测深结果的相关系数达到0.93,均方根误差为0.51 m,具有较高的测深精度。展开更多
第二代星载激光雷达冰、云和陆地测高卫星(Ice,Cloud,and Land Elevation Satellite-2,ICESat-2)在获取浅海岛礁水深信息方面具有极大潜力。然而受大气散射、太阳辐射和仪器噪声等因素影响,造成获取的ICESat-2星载激光光子中存在大量噪...第二代星载激光雷达冰、云和陆地测高卫星(Ice,Cloud,and Land Elevation Satellite-2,ICESat-2)在获取浅海岛礁水深信息方面具有极大潜力。然而受大气散射、太阳辐射和仪器噪声等因素影响,造成获取的ICESat-2星载激光光子中存在大量噪声。针对上述问题,本文提出一种基于多尺度分析的四叉树星载激光雷达去噪方法。首先,使用高斯核函数和K折交叉验证的方法绘制光子核密度曲线(Kernel Density Estimation,KDE),并设置阈值来分离海面光子和海底光子;其次,利用自适应参数的DBSCAN(Density-Based Spatial Clustering of Applications with Noise)算法去除海底异常光子,获得粗略去噪结果。最后,对海底光子划分窗口,从不同尺度使用预判断四叉树算法提取出精确的海底信号光子。研究选取典型岛礁的ICESat-2卫星数据,通过与实测水深数据对比,决定系数(R^(2))分别达到95%和98%,均方根误差(RMSE)分别达到1.01 m和0.77 m。结果表明,该方法能够准确提取水下地形信息,为浅海水下地形反演奠定基础。展开更多
基金supported by the Guangdong Province Introduced Innovative R&D Team of Big Data-Mathematical Earth Sciences and Extreme Geological Events Team(grant number 2021ZT09H399)the National Natural Science Foundation of China(grant number 42430111,42050103).
文摘The investigations of physical attributes of oceans,including parameters such as heat flow and bathymetry,have garnered substantial attention and are particularly valuable for examining Earth’s thermal structures and dynamic processes.Nevertheless,classical plate cooling models exhibit disparities when predicting observed heat flow and seafloor depth for extremely young and old lithospheres.Furthermore,a comprehensive analysis of global heat flow predictions and regional ocean heat flow or bathymetry data with physical models has been lacking.In this study,we employed power-law models derived from the singularity theory of fractal density to meticulously fit the latest ocean heat flow and bathymetry.Notably,power-law models offer distinct advantages over traditional plate cooling models,showcasing robust self-similarity,scale invariance,or scaling properties,and providing a better fit to observed data.The outcomes of our singularity analysis concerning heat flow and bathymetry across diverse oceanic regions exhibit a degree of consistency with the global ocean spreading rate model.In addition,we applied the similarity method to predict a higher resolution(0.1°×0.1°)global heat flow map based on the most recent heat flow data and geological/geophysical observables refined through linear correlation analysis.Regions displaying significant disparities between predicted and observed heat flow are closely linked to hydrothermal vent fields and active structures.Finally,combining the actual bathymetry and predicted heat flow with the power-law models allows for the quantitative and comprehensive detection of anomalous regions of ocean subsidence and heat flow,which deviate from traditional plate cooling models.The anomalous regions of subsidence and heat flow show different degrees of anisotropy,providing new ideas and clues for further analysis of ocean topography or hydrothermal circulation of mid-ocean ridges.
基金funded jointly by the National Nature Science Funds of China(No.42274010)the Fundamental Research Funds for the Central Universities(Nos.2023000540,2023000407).
文摘The prediction of bathymetry has advanced significantly with the development of satellite altimetry.However,the majority of its data originate from marine gravity anomaly.In this study,based on the expression of vertical gravity gradient(VGG)of a rectangular prism,the governing equations for determining sea depths to invert bathymetry.The governing equation is solved by linearization through an iterative process,and numerical simulations verify its algorithm and its stability.We also study the processing methods of different interference errors.The regularization method improves the stability of the inversion process for errors.A piecewise bilinear interpolation function roughly replaces the low-frequency error,and numerical simulations show that the accuracy can be improved by 41.2%after this treatment.For variable ocean crust density,simulation simulations verify that the root-mean-square(RMS)error of prediction is approximately 5 m for the sea depth of 6 km if density is chosen as the average one.Finally,two test regions in the South China Sea are predicted and compared with ship soundings data,RMS errors of predictions are 71.1 m and 91.4 m,respectively.
基金The National Key R&D Program of China under contract Nos 2022YFC3003800,2020YFC1521700 and 2020YFC1521705the National Natural Science Foundation of China under contract No.41830540+3 种基金the Open Fund of the East China Coastal Field Scientific Observation and Research Station of the Ministry of Natural Resources under contract No.OR-SECCZ2022104the Deep Blue Project of Shanghai Jiao Tong University under contract No.SL2020ZD204the Special Funding Project for the Basic Scientific Research Operation Expenses of the Central Government-Level Research Institutes of Public Interest of China under contract No.SZ2102the Zhejiang Provincial Project under contract No.330000210130313013006。
文摘Understanding the topographic patterns of the seafloor is a very important part of understanding our planet.Although the science involved in bathymetric surveying has advanced much over the decades,less than 20%of the seafloor has been precisely modeled to date,and there is an urgent need to improve the accuracy and reduce the uncertainty of underwater survey data.In this study,we introduce a pretrained visual geometry group network(VGGNet)method based on deep learning.To apply this method,we input gravity anomaly data derived from ship measurements and satellite altimetry into the model and correct the latter,which has a larger spatial coverage,based on the former,which is considered the true value and is more accurate.After obtaining the corrected high-precision gravity model,it is inverted to the corresponding bathymetric model by applying the gravity-depth correlation.We choose four data pairs collected from different environments,i.e.,the Southern Ocean,Pacific Ocean,Atlantic Ocean and Caribbean Sea,to evaluate the topographic correction results of the model.The experiments show that the coefficient of determination(R~2)reaches 0.834 among the results of the four experimental groups,signifying a high correlation.The standard deviation and normalized root mean square error are also evaluated,and the accuracy of their performance improved by up to 24.2%compared with similar research done in recent years.The evaluation of the R^(2) values at different water depths shows that our model can achieve performance results above 0.90 at certain water depths and can also significantly improve results from mid-water depths when compared to previous research.Finally,the bathymetry corrected by our model is able to show an accuracy improvement level of more than 21%within 1%of the total water depths,which is sufficient to prove that the VGGNet-based method has the ability to perform a gravity-bathymetry correction and achieve outstanding results.
基金sponsored by the Shell Petroleum Development Company of Nigeria Limited(SPDC).
文摘The utilization of sequence stratigraphic concepts in identifying sands and their spatial continuity in distinct gross depositional settings is key,especially in frontier settings where data paucity is a common challenge.In the Baka field,onshore Niger Delta,detailed reservoir correlation guided by sequence stratigraphic framework analysis showed the distribution of sand and shale units constituting reservoirseal pairs(RSP)correlatable across the field.Within the 3rd-order packages,it is observed that the lowstand systems tract(LST)and highstand systems tract(HST)contain more RSPs and thicker 4th-and 5th-order sands than the transgressive systems tract(TST).In terms of bathymetry,it is noted that irrespective of systems tracts,the RSP Index(RI)decreases from the proximal shallow/inner shelf settings to the more distal outer shelf areas.Amongst all three systems tracts,intervals interpreted as lowstand prograding complexes contain the best developed sands and highest RSP.Sand development within the LSTs has been controlled by a pronounced growth fault regime accompanied by high subsidence and sedimentation rates.This is linked to the basinward migration of the sands during prolonged sea-level fall,creating significant accommodation space for sand deposition.On the other hand,the TSTs known to mark periods of progressive sea-level rise and landward migration of sandy facies,show thinner sands enclosed in much thicker,laterally extensive,and better-preserved deeper marine shales.Interpreted seismic sections indicate intense growth faulting and channelization that influenced the syn-and postdepositional development of the sand packages across the field.The initial timing of deformation of subregional faults in this area coincides with periods of abrupt falls in sea level.This approach could be useful for predicting sand-prone areas in frontier fields as well as possible reservoir-seal parameters required for some aspects of petroleum system analysis and quick-look volume estimation.
文摘第二代星载激光雷达冰、云和陆地测高卫星(Ice,Cloud,and Land Elevation Satellite-2,ICESat-2)在获取浅海岛礁水深信息方面具有极大潜力。然而受大气散射、太阳辐射和仪器噪声等因素影响,造成获取的ICESat-2星载激光光子中存在大量噪声。针对上述问题,本文提出一种基于多尺度分析的四叉树星载激光雷达去噪方法。首先,使用高斯核函数和K折交叉验证的方法绘制光子核密度曲线(Kernel Density Estimation,KDE),并设置阈值来分离海面光子和海底光子;其次,利用自适应参数的DBSCAN(Density-Based Spatial Clustering of Applications with Noise)算法去除海底异常光子,获得粗略去噪结果。最后,对海底光子划分窗口,从不同尺度使用预判断四叉树算法提取出精确的海底信号光子。研究选取典型岛礁的ICESat-2卫星数据,通过与实测水深数据对比,决定系数(R^(2))分别达到95%和98%,均方根误差(RMSE)分别达到1.01 m和0.77 m。结果表明,该方法能够准确提取水下地形信息,为浅海水下地形反演奠定基础。
