Amanote Research

Amanote Research

    RegisterSign In

Different Training Sample Selection Strategies in Unsupervised Seismic Facies Analysis

doi 10.1190/segam2017-17739947.1
Full Text
Open PDF
Abstract

Available in full text

Date

August 17, 2017

Authors
Tao ZhaoKurt Marfurt
Publisher

Society of Exploration Geophysicists


Related search

Unsupervised Seismic-Facies Classification Using Independent-Component Analysis

2018English

Unsupervised Seismic Facies Using Gaussian Mixture Models

Interpretation
GeophysicsGeology
2019English

Seismic Facies Characterization by Monoscale Analysis

Geophysical Research Letters
EarthPlanetary SciencesGeophysics
2001English

Unsupervised Gene Selection Using Biological Knowledge : Application in Sample Clustering

BMC Bioinformatics
BiochemistryApplied MathematicsComputer Science ApplicationsStructural BiologyMolecular Biology
2017English

Distinguishing Different Strategies of Across-Dimension Attentional Selection.

Journal of Experimental Psychology: Human Perception and Performance
ArtsCognitive PsychologyHumanitiesBehavioral NeuroscienceMedicineExperimental
2012English

Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects

2005English

Seismic to Facies Inversion Using Convolved Hidden Markov Model

English

Unsupervised Feature Selection on Data Streams

2015English

Unsupervised Feature Selection Based on Kernel Fisher Discriminant Analysis and Regression Learning

Machine Learning
Artificial IntelligenceSoftware
2018English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2026 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy