A visual data-mining approach using 3D thoracic CT images for classification between benign and malignant pulmonary nodules

2003 ◽  
Author(s):  
Yoshiki Kawata ◽  
Noboru Niki ◽  
Hironobu Ohamatsu ◽  
Masahiko Kusumoto ◽  
Ryutaro Kakinuma ◽  
...  
Keyword(s):  
Data Mining ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Visual Data ◽  
Visual Data Mining ◽  
Data Mining Approach ◽  
Thoracic Ct

A visual data-mining methodology for seismic facies analysis: Part 2 — Application to 3D seismic data

Geophysics ◽  
2009 ◽  
Vol 74 (1) ◽  
pp. P13-P23 ◽  
Author(s):  
Iván Dimitri Marroquín ◽  
Jean-Jules Brault ◽  
Bruce S. Hart
Keyword(s):  
Data Mining ◽  
Seismic Data ◽  
Clustering Analysis ◽  
Facies Analysis ◽  
Seismic Facies ◽  
Visual Data ◽  
Visual Data Mining ◽  
Data Mining Approach ◽  
Seismic Facies Analysis ◽  
3D Seismic Data

A visual data-mining approach to unsupervised clustering analysis can be an effective tool for visualizing and understanding patterns inherent in seismic data (i.e., seismic facies). The unsupervised clustering analysis is completely data-driven, requiring no external information (e.g., well logs) to guide the seismic-trace classification. We demonstrate the application of the visual data-mining approach to seismic facies analysis on a real 3D seismic data volume. We select two stratigraphic intervals, the first including a Devonian pinnacle reef system and the second containing a Jurassic siliciclastic channel system. Both analyses show major stratigraphic features that can be defined in horizon slices or other types of visualization. However, the visual data-mining approach creates seismic facies maps with improved visual detail, distinguishing seismic trace-shape variability in the data. We also compare the facies maps with those obtained from a commercial package for seismic facies classification. Both approaches created similar facies maps, but the visual strategy better depicts subtle stratigraphic changes in the bodies being imaged, offering insight into the nature of these features.

Visual Data Mining of Log Files

2007 ◽  
pp. 531-535
Author(s):  
Guillermo III Francia ◽  
Monica Trifas ◽  
Dorothy Brown ◽  
Rahjimao Francia ◽  
Chrissy Scott
Keyword(s):  
Data Mining ◽  
Visual Data ◽  
Visual Data Mining ◽  
Log Files
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