Predicting Global Seafloor Topography Using Multi-Source Data

A new one-minute global seafloor topography model was derived from vertical gravity gradient anomalies (VGG), altimetric gravity anomalies, and ship soundings. Ship soundings are used to constrain seafloor topography at wavelengths longer than 200 km and to calibrate the topography to VGG (or gravity) ratios at short wavelengths area by area. VGG ratios are used to predict seafloor topography for wavelength bands of 100–200 km and to suppress the effect of crust isostasy. Gravity anomalies are used to recover seafloor topography at wavelengths shorter than 100 km. The data processing procedure is described in detail in this paper. The accuracy of the model is evaluated using ship soundings and existing models, including General Bathymetric Charts of the Oceans (GEBCO), DTU10, ETOPO1, and SIO V15.1. The results show that, in the discussed regions, the accuracy of the model is better than ETOPO1, GEBCO, and DTU10. Additionally, the model is comparable with V15.1, which is generally believed to have the highest accuracy. In the north-central Pacific Ocean, the accuracy of the model increased by approximately 29.5% compared with the V15.1 model. This indicates that a more accurate seafloor topography model can be formed by combining gravity anomalies, VGG, and ship soundings.     

重力异常垂直梯度中央区效应的精密计算

为提高利用重力异常计算重力异常垂直梯度中央区效应的精度,视中央区为矩形域,将重力异常表示成双三次多项式插值形式,引入非奇异变换,推导出了重力异常垂直梯度中央区效应的精密计算公式。以低纬度区域分辨率为2＇×2＇的重力异常数据为背景场进行了仿真分析,结果表明在解算计算点所在的1个网格的中央区效应时,传统公式与本文导出公式计算结果差值的最大值达数E。该导出公式可为重力异常垂直梯度中央区效应的精密计算提供理论依据。     

联合重力异常和重力垂直梯度异常数据反演皇帝山海域海底地形

海底地形对开展海洋科学调查和研究十分重要。以多波束为主的回声测深技术测量成本高且效率低,几十年来仅实现了全球约20%的海床测绘。对于空白区（特别是深海区域）,可以借助重力异常和重力垂直梯度异常进行回归分析反演得到,但该方法得到的比例因子鲁棒性不强。为了解决这一问题,同时考虑到两种重力数据在表征海底地形长短波长的不同优势,本文结合滑动窗口赋权和稳健回归分析来反演海底地形。在太平洋皇帝山海域（35°～45°N,165°～175°E）的实验结果表明：在船测检核点处,本文构建模型的标准差为61.02 m,相比于单一重力数据反演模型,精度分别提高了14.92%（重力异常）和2.08%（重力垂直梯度异常）,能较好地反映皇帝海山链的地形走势。     

多类资料计算南海重力垂直梯度的方法比较

重力垂直梯度在解决和解释地球表层地质和地球物理问题中的作用日益明显,因而获得其模型和分布是非常必要的。利用测高卫星可以得到空间大范围高精度、高分辨率的垂线偏差、重力异常以及大地水准面数据,利用测高重力资料和地球重力场模型,采用不同方法分别计算了南海海域重力垂直梯度,并对它们进行了比较。     

重力梯度张量计算重力场元

根据体谐函数一阶、二阶水平导数（广义球函数）也是球面正交函数系的性质,详细推导了水平重力梯度边值问题的级数解.根据扰动位与重力场元的微分关系,导出了由水平重力梯度计算重力异常、垂线偏差的公式.完善了全张量重力梯度的有关应用.     

A new approach to the structural features of the Aegean Sea: Cellular neural network

In this study, structural features in the Aegean Sea were investigated by application of Cellular Neural Network (CNN) and Cross-Correlation methods to the gravity anomaly map. CNN is a stochastic image processing technique, which is based on template optimization using neighbourhood relationships of pixels, and probabilistic properties of two-Dimensional (2-D) input data. The performance of CNN can be evaluated by various interesting real applications in geophysics such as edge detection, data enhancement and separation of regional/residual potential anomaly maps. In this study, CNN is used in edge detection of geological bodies closer to the surface, which are masked by other structures with various depths and dimensions. CNN was first tested for (prismatic) synthetic examples and satisfactory results were obtained. Subsequently, CNN/Cross-Correlation maps and bathymetric features were evaluated together to obtain a new structural map for most of the Aegean Sea. In our structural map, the locations of the faults and basins are generally in accordance with the previous maps from restricted areas based on seismic data. In the southern and southeastern parts of the Aegean Sea, E–W trending faults cut NE–SW trending basins and faults, similar to on-shore Western Anatolia. Also, in the western, central and northern parts of the Aegean Sea, all of these structures are truncated by NE-trending faults.