Geophysical data processing and interpretation in an area of complex structure — the southern Taranaki Boundary Fault Zone

1991 ◽  
Vol 22 (2) ◽  
pp. 271-276
Author(s):  
J. I. Montalbetti ◽  
D. J. Norris
Keyword(s):  
Data Processing ◽  
Fault Zone ◽  
Complex Structure ◽  
Geophysical Data ◽  
Boundary Fault

A Preliminary Interpretation of Deep Seismic Reflection and other Geophysical Data from the Darling Fault Zone, Western Australia

1993 ◽  
Vol 24 (3-4) ◽  
pp. 711-717 ◽  
Author(s):  
M.F. Middleton ◽  
A. Long ◽  
S.A. Wilde ◽  
M. Dentith ◽  
B.A. Evans
Keyword(s):  
Fault Zone ◽  
Western Australia ◽  
Seismic Reflection ◽  
Geophysical Data ◽  
Deep Seismic Reflection

Research on Transverse Faults in the Longmenshan Fault Zone, China

2014 ◽  
Vol 1010-1012 ◽  
pp. 1380-1386
Author(s):  
Wei Feng Wang ◽  
Chuan Hua Zhu ◽  
Yan Bin Qing ◽  
Xin Jian Shan
Keyword(s):  
Remote Sensing ◽  
Fault Zone ◽  
Field Studies ◽  
Geophysical Data ◽  
Remote Sensing Images ◽  
Longmenshan Fault Zone ◽  
Geomorphic Features ◽  
Longmenshan Fault ◽  
Regional Tectonic

The Longmenshan fault zone has been a research hotspot, but fewer scholars have paid attention to its transverse faults. According to the analysis of regional tectonic, seismic activities, geomorphic features, remote sensing images, and deep geophysical data, combined with field studies, the existence, distribution and type of the transverse faults in the Longmenshan fault zone were demonstrated. Research shows that there are 9 transverse faults that lie parallel to each other approximately at ~50km intervals in the Longmenshan fault zone. And transverse faults can be divided into regional transverse faults and localized transverse faults with NW strike, nearly EW strike and nearly SN strike.

scholarly journals Optimizing the performance of hadoop clusters through efficient cluster management techniques

2018 ◽  
Vol 7 (2.31) ◽  
pp. 19 ◽  
Author(s):  
K S. Shraddha Bollamma ◽  
S Manishankar ◽  
M V. Vishnu
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Complex Structure ◽  
Optimal Solution ◽  
Vital Role ◽  
Energy Utilization ◽  
Suitable Model ◽  
Big Data Processing ◽  
Cluster Architecture ◽  
Hadoop Clusters

The necessity for processing the huge data has become a critical task in the age of Internet, even though data processing has evolved into a next generation level still data processing and information extraction has many problems to solve. With the increase in data size retrieving useful information with a given span of time is a herculean task. The most optimal solution that has been adopted is usage of distributed computing environment supporting data processing involving suitable model architecture with large complex structure. Although processing has achieved good amount of improvement, efficiency, energy utilization and accuracy has been compromised. The research aims to propose an efficient environment for data processing with optimized energy utilization and increased performance. Hadoop environment common and popular among big data processing platform has been chosen as base for enhancement. Creating a multi node Hadoop cluster architecture on top of which an efficient cluster monitor is setup and an algorithm to manage efficiency of the cluster is formulated. Cluster monitor is incorporated with Zoo keeper, Yarn (Node and resource manager). Zoo keeper does the monitoring of cluster nodes of the distributed system and identifies critical performance problems. Yarn plays a vital role in managing the resources efficiently and controlling the nodes with the help of hybrid scheduler algorithm. Thus this integrated platform helps in monitoring the distributed cluster as well as improving the performance of the overall Big Data processing.   

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