scholarly journals Selecting inputs for modeling using normalized higher order statistics and independent component analysis

2001 ◽  
Vol 12 (3) ◽  
pp. 612-617 ◽  
Author(s):  
A.D. Back ◽  
T.P. Trappenberg
Keyword(s):  
Independent Component Analysis ◽  
Order Statistics ◽  
Component Analysis ◽  
Higher Order ◽  
Independent Component ◽  
Higher Order Statistics

Independent component analysis for reservoir geomorphology and unsupervised seismic facies classification in the Taranaki Basin, New Zealand

2019 ◽  
Vol 7 (3) ◽  
pp. SE19-SE42 ◽  
Author(s):  
David Lubo-Robles ◽  
Kurt J. Marfurt
Keyword(s):  
New Zealand ◽  
Independent Component Analysis ◽  
Order Statistics ◽  
Input Data ◽  
Component Analysis ◽  
Independent Component ◽  
Seismic Facies ◽  
Blue Color ◽  
Independent Components ◽  
Facies Classification

During the past two decades, the number of volumetric seismic attributes has increased to the point at which interpreters are overwhelmed and cannot analyze all of the information that is available. Principal component analysis (PCA) is one of the best-known multivariate analysis techniques that decompose the input data into second-order statistics by maximizing the variance, thus obtaining mathematically uncorrelated components. Unfortunately, projecting the information in the multiple input data volumes onto an orthogonal basis often mixes rather than separates geologic features of interest. To address this issue, we have implemented and evaluated a relatively new unsupervised multiattribute analysis technique called independent component analysis (ICA), which is based on higher order statistics. We evaluate our algorithm to study the internal architecture of turbiditic channel complexes present in the Moki A sands Formation, Taranaki Basin, New Zealand. We input 12 spectral magnitude components ranging from 25 to 80 Hz into the ICA algorithm and we plot 3 of the resulting independent components against a red-green-blue color scheme to generate a single volume in which the colored independent components correspond to different seismic facies. The results obtained using ICA proved to be superior to those obtained using PCA. Specifically, ICA provides improved resolution and separates geologic features from noise. Moreover, with ICA, we can geologically analyze the different seismic facies and relate them to sand- and mud-prone seismic facies associated with axial and off-axis deposition and cut-and-fill architectures.

scholarly journals Independent component analysis based on first-order statistics

2012 ◽  
Vol 92 (8) ◽  
pp. 1779-1784 ◽  
Author(s):  
V. Zarzoso ◽  
R. Martín-Clemente ◽  
S. Hornillo-Mellado
Keyword(s):  
Independent Component Analysis ◽  
Order Statistics ◽  
Component Analysis ◽  
Independent Component ◽  
First Order
Sign in / Sign up

Export Citation Format

Share Document