scholarly journals A detailed research on the structural characteristics of Hoang Sa and Truong Sa archipelagos - East Vietnam Sea based on gravity data analysis

2019 ◽  
Vol 19 (3B) ◽  
pp. 163-175
Author(s):  
Nguyen Kim Dung ◽  
Do Duc Thanh ◽  
Hoang Van Vuong ◽  
Do Huy Cuong ◽  
Tran Tuan Dung ◽  
...  
Keyword(s):  
Subduction Zones ◽  
Structural Characteristics ◽  
Gravity Data ◽  
Gravity Gradient ◽  
Coordinate Frame ◽  
Large Area ◽  
Gravity Gradient Tensor ◽  
Structural Block ◽  
New Information ◽  
Different Levels

The Hoang Sa and Truong Sa archipelagos are the two archipelagos located in the East Vietnam Sea. In the geographic coordinate frame, the Hoang Sa archipelago is located more northward than the Truong Sa. Up to now many publications have discussed in detail structures of these archipelagos in terms of international and domestic scientific journals, the scientific workshop reports, as well as the outcome reports obtained from the research projects of different levels, such as state and ministry level projects. However the block characteristics of the two archipelago regions are still in controversy. By application of the new technique (Curvature Gravity Gradient Tensor - CGGT) for analysis and collection of the related available data, some new information about structural characteristics of the two blocks, such as their spatial distribution, the penetration of their boundaries and fault systems was obtained. According to the results, block characteristic is clearly reflected as a unique structural unit for Hoang Sa archipelago, which occupies a large area restricted mostly by the geographic coordinate frame: 111.2oE–113.2oE and 15.75oN–17.25oN.  Here a large negative Hoang Sa structural block with the density less than 2.67 g/cm3 develops directly on a more negative regional structure. Unlike Hoang Sa block, the Truong Sa archipelago is not presented as a unique block. Its structure is divided into 3 main smaller blocks distributed along different directions. The first north - south structural block consists of a number of islands and sandbars: Dinh Ba, Song Tu Dong island, Song Tu Tay island, Thi Tu island, Ba Binh island, Ca Nham sandbar, Loai Ta island and Son Ca island, Nam Yet island, Truong Sa Lon island, Sinh Ton island, Ba Bau and Binh Nguyen island. The second structural block along the northeast - southwest direction includes the following islands and sandbars: Da Lat, Truong Sa island, Da Tay, Da Dong, Chau Vinh. The remaining Phan Vinh island, Toc Tan sandbar, Nui Le, Ky Van, Tham Hiem sandbar and Kieu Ngua sandbar are distributed in the third structural block. In addition, all the 3 blocks are the negative structures. In terms of geological structural boundaries: The estimated depth of the boundaries (uplifts, subduction zones, or faults,...) on Hoang Sa archipelago only reaches a maximum of 20 km. Meanwhile, that on Truong Sa archipelago is possibly over 20 km.

Street-Level Imagery and Deep Learning for Characterization of Exposed Buildings

2021 ◽  
Author(s):  
Patrick Aravena Pelizari ◽  
Christian Geiß ◽  
Elisabeth Schoepfer ◽  
Torsten Riedlinger ◽  
Paula Aguirre ◽  
...  
Keyword(s):  
Deep Learning ◽  
Structural Characteristics ◽  
High Accuracy ◽  
Risk Modeling ◽  
Sources Of Information ◽  
Large Area ◽  
Image Domain ◽  
Street Level ◽  
Building Characteristics

<p>Knowledge on the key structural characteristics of exposed buildings is crucial for accurate risk modeling with regard to natural hazards. In risk assessment this information is used to interlink exposed buildings with specific representative vulnerability models and is thus a prerequisite to implement sound risk models. The acquisition of such data by conventional building surveys is usually highly expensive in terms of labor, time, and money. Institutional data bases such as census or tax assessor data provide alternative sources of information. Such data, however, are often inappropriate, out-of-date, or not available. Today, the large-area availability of systematically collected street-level data due to global initiatives such as Google Street View, among others, offers new possibilities for the collection of <em>in-situ</em> data. At the same time, developments in machine learning and computer vision – in deep learning in particular – show high accuracy in solving perceptual tasks in the image domain. Thereon, we explore the potential of an automatized and thus efficient collection of vulnerability related building characteristics. To this end, we elaborated a workflow where the inference of building characteristics (e.g., the seismic building structural type, the material of the lateral load resisting system or the building height) from geotagged street-level imagery is tasked to a custom-trained Deep Convolutional Neural Network. The approach is applied and evaluated for the earthquake-prone Chilean capital Santiago de Chile. Experimental results are presented and show high accuracy in the derivation of addressed target variables. This emphasizes the potential of the proposed methodology to contribute to large-area collection of <em>in-situ</em> information on exposed buildings.</p>

Speed and accuracy improvements in standard algorithm for prismatic gravitational field

2020 ◽  
Vol 222 (3) ◽  
pp. 1898-1908
Author(s):  
Toshio Fukushima
Keyword(s):  
Gravitational Field ◽  
Exact Formula ◽  
New Method ◽  
Gravity Gradient ◽  
Gradient Tensor ◽  
Gravity Gradient Tensor ◽  
Triple Difference ◽  
Transcendental Functions ◽  
Arctangent Function ◽  
Speed And Accuracy

SUMMARY By utilizing the addition theorems of the arctangent function and the logarithm, we developed a new expression of Bessel’s exact formula to compute the prismatic gravitational field using the triple difference of certain analytic functions. The use of the new expression is fast since the number of transcendental functions required is significantly reduced. The numerical experiments show that, in computing the gravitational potential, the gravity vector, and the gravity gradient tensor of a uniform rectangular parallelepiped, the new method runs 2.3, 2.3 and 3.7 times faster than Bessel’s method, respectively. Also, the new method achieves a slight increase in the computing precision. Therefore, the new method can be used in place of Bessel’s method in any situation. The same approach is applicable to the geomagnetic field computation.

Noise filtering of full-gravity gradient tensor data

2013 ◽  
Vol 10 (3) ◽  
pp. 241-250 ◽  
Author(s):  
Yuan Yuan ◽  
Da-Nian Huang ◽  
Qing-Lu Yu ◽  
Mei-Xia Geng
Keyword(s):  
Gravity Gradient ◽  
Noise Filtering ◽  
Gradient Tensor ◽  
Gravity Gradient Tensor ◽  
Tensor Data
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