A Study on Fashion Attribute Analysis Using Spherical K-means Clustering

2021 ◽  
Vol 35 (1) ◽  
pp. 137-159
Author(s):  
SoYoun Bang ◽  
◽  
Miyoung Lee
Keyword(s):  
2007 ◽  
Author(s):  
Srinivasa Rao Narhari ◽  
Nikhil Banik ◽  
Sunil Kumar Singh ◽  
Talal Fahad Al-Adwani

2013 ◽  
Vol 734-737 ◽  
pp. 404-407 ◽  
Author(s):  
Yu Shuang Hu ◽  
Si Miao Zhu

A big tendency in oil industry is underestimating the heterogeneity of the reservoir and overestimating the connectivity, which results in overly optimistic estimates of the capacity. With the development of seismic attributes, we could pick up hidden reservoir lithology and physical property information from the actual seismic data, strengthen seismic data application in actual work, to ensure the objectivity of the results. In this paper, the channel sand body distribution in south eighth district of oilfield Saertu is predicted through seismic data root-mean-square amplitude and frequency division to identify sand body boundaries, predict the distribution area channel sand body characteristics successfully, which consistent with the sedimentary facies distribution. The result proves that seismic attribute analysis has good practicability in channel sand body prediction and sedimentary facies description.


2016 ◽  
Vol 206 (2) ◽  
pp. 1366-1374 ◽  
Author(s):  
Wenke Zhao ◽  
Emanuele Forte ◽  
Renato R. Colucci ◽  
Michele Pipan

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