Multi-Azimuth Approach of Depth Imaging for Marine Towed Streamer Data

Author(s):  
O. Litvyakova ◽  
A. Sakharov ◽  
A. Bodrov ◽  
B. Esinov ◽  
A. Korolev
Keyword(s):  
2017 ◽  
Vol 57 (2) ◽  
pp. 767
Author(s):  
Weidi Koh

The acquisition and depth imaging of almost 20400 km2 of broadband seismic data in the Great Australian Bight has created an excellent dataset fit for quantitative interpretation. This new dataset was derived from a merge of 12400 km2 of 2011 vintage conventional streamer data in an almost seamless manner with 8000 km2 of 2014 vintage dual-sensor streamer data. The Ceduna Sub-basin is the main depocentre of the Bight Basin. It lies adjacent to the continental shelf and slope and is covered by two broad bathymetric terraces in water depths ranging from <200 to >4000 m. A potentially prospective Late Jurassic syn-rift to Late Cretaceous post-rift sedimentary succession (fluvial to paralic sediments) >15 km thick is imaged with remarkable quality and resolution. Features of particular interest include large stacked fan and channel systems, as well as simple, structurally closed formations. Careful survey design and execution optimised efficiency, enabling each survey to be acquired in less than one season. Particular attention was given to amplitude versus offset and phase compliance, including customised flows to overcome a paucity of well control in this frontier area. Optimised preprocessing, velocity model building and survey merging were applied to ensure structural and depth integrity in the final images. Regional and targeted mapping and quantitative interpretation results testify to the value of the multifaceted geophysical and geological disciplines used in the overall project execution.


2014 ◽  
Author(s):  
Karam Mohamed Hafez ◽  
Pradip Kumar Mukherjee ◽  
Mudavakkat Anandan ◽  
Aisha Al-Ghareeb ◽  
Wael Abdel Alim Zahran ◽  
...  
Keyword(s):  

2021 ◽  
Vol 40 (4) ◽  
pp. 1-12
Author(s):  
Clara Callenberg ◽  
Zheng Shi ◽  
Felix Heide ◽  
Matthias B. Hullin

Author(s):  
Mingde Yao ◽  
Zhiwei Xiong ◽  
Lizhi Wang ◽  
Dong Liu ◽  
Xuejin Chen
Keyword(s):  

2010 ◽  
Vol 20 (1) ◽  
pp. 120-128 ◽  
Author(s):  
Md. Zia Uddin ◽  
Tae-Seong Kim ◽  
Jeong Tai Kim

Smart homes that are capable of home healthcare and e-Health services are receiving much attention due to their potential for better care of the elderly and disabled in an indoor environment. Recently the Center for Sustainable Healthy Buildings at Kyung Hee University has developed a novel indoor human activity recognition methodology based on depth imaging of a user’s activities. This system utilizes Independent Component Analysis to extract spatiotemporal features from a series of depth silhouettes of various activities. To recognise the activities from the spatiotemporal features, trained Hidden Markov Models of the activities would be used. In this study, this technique has been extended to recognise human gaits (including normal and abnormal). Since this system could be of great significance for the caring of the elderly, to promote and preserve their health and independence, the gait recognition system would be considered a primary function of the smart system for smart homes. The indoor gait recognition system is trained to detect abnormal gait patterns and generate warnings. The system works in real-time and is aimed to be installed at smart homes. This paper provides the information for further development of the system for their application in the future.


Sign in / Sign up

Export Citation Format

Share Document