Related references
Note: Only part of the references are listed.Interpretable Semisupervised Classification Method Under Multiple Smoothness Assumptions With Application to Lithology Identification
Zerui Li et al.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2021)
Improved well-log classification using semisupervised label propagation and self-training, with comparisons to popular supervised algorithms
Michael W. Dunham et al.
GEOPHYSICS (2020)
Well Logging Based Lithology Identification Model Establishment Under Data Drift: A Transfer Learning Method
Haining Liu et al.
SENSORS (2020)
Beyond Sharing Weights for Deep Domain Adaptation
Artem Rozantsev et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2019)
Logging Lithology Discrimination in the Prototype Similarity Space With Random Forest
Yile Ao et al.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2019)
Formation lithology classification using scalable gradient boosted decision trees
Vikrant A. Dev et al.
COMPUTERS & CHEMICAL ENGINEERING (2019)
Application of a convolutional neural network in permeability prediction: A case study in the Jacksonburg-Stringtown oil field, West Virginia, USA
Zhi Zhong et al.
GEOPHYSICS (2019)
Complex lithology prediction using probabilistic neural network improved by continuous restricted Boltzmann machine and particle swarm optimization
Yufeng Gu et al.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING (2019)
Evaluation of machine learning methods for formation lithology identification: A comparison of tuning processes and model performances
Yunxin Xie et al.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING (2018)
Lithology identification using an optimized KNN clustering method based on entropy-weighed cosine distance in Mesozoic strata of Gaoqing field, Jiyang depression
Xidong Wang et al.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING (2018)
Adversarial Feature Augmentation for Unsupervised Domain Adaptation
Riccardo Volpi et al.
2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2018)
Support vector machine as an alternative method for lithology classification of crystalline rocks
Chengxiang Deng et al.
JOURNAL OF GEOPHYSICS AND ENGINEERING (2017)
Wishart Deep Stacking Network for Fast POLSAR Image Classification
Licheng Jiao et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING (2016)
Domain Invariant Transfer Kernel Learning
Mingsheng Long et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2015)
A Practical Transfer Learning Algorithm for Face Verification
Xudong Cao et al.
2013 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) (2013)
Quantitative log interpretation and uncertainty propagation of petrophysical properties and facies classification from rock-physics modeling and formation evaluation analysis
Dario Grana et al.
GEOPHYSICS (2012)
Domain Adaptation via Transfer Component Analysis
Sinno Jialin Pan et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS (2011)
A Survey on Transfer Learning
Sinno Jialin Pan et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2010)
Bregman Divergence-Based Regularization for Transfer Subspace Learning
Si Si et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2010)
On the Capability of Support Vector Machines to Classify Lithology from Well Logs
A. Al-Anazi et al.
Natural Resources Research (2010)
Predicting formation lithology from log data by using a neural network
Wang Kexiong et al.
PETROLEUM SCIENCE (2008)
Integrating structured biological data by Kernel Maximum Mean Discrepancy
Karsten M. Borgwardt et al.
BIOINFORMATICS (2006)
Share Paper
