- A 3D channel body interpretation via multiple attributes and supervoxel graph cut
- A machine-learning benchmark for facies classification
- A scalable deep learning platform for identifying geologic features from seismic attributes
- A seismic facies classification method based on the convolutional neural network and the probabilistic framework for seismic attributes and spatial classification
- An enhanced fault-detection method based on adaptive spectral decomposition and super-resolution deep learning
- Application of Data Analytics for Production Optimization in Unconventional Reservoirs: A Critical Review
- Applications of supervised deep learning for seismic interpretation and inversion
- Assessment of machine-learning techniques in predicting lithofluid facies logs in hydrocarbon wells
- Attribute selection in seismic facies classification: Application to a Gulf of Mexico 3D seismic survey and the Barnett Shale
- Automated interpretation of top and base salt using deep convolutional networks
- Automated salt-dome detection using an attribute ranking framework with a dictionary-based classifier
- Automatic channel detection using deep learning
- Automatic mapping of the base of aquifer — A case study from Morrill, Nebraska
- Characterizing a turbidite system in Canterbury Basin, New Zealand, using seismic attributes and distance-preserving self-organizing maps
- Convolutional neural networks as aid in core lithofacies classification
- Deep learning applied to seismic attribute computation
- Depositional sequence characterization based on seismic variational mode decomposition
- Descriptive Data Analytics for the Stimulation, Completion Activities, and Wells Productivity in the Marcellus Shale Play
- Distributed collaborative prediction: Results of the machine learning contest
- Eagle Ford Fluid Type Variation and Completion Optimization: A Case for Data Analytics
- Effective glue between geoscience concepts, data, and modeling systems
- Estimating normal moveout velocity using the recurrent neural network
- Facies classification using machine learning
- Full-volume 3D seismic interpretation methods: A new step towards high-resolution seismic stratigraphy
- Geological Facies Prediction Using Computed Tomography in a Machine Learning and Deep Learning Environment
- Geophysical inversion versus machine learning in inverse problems
- Geostatistical seismic inversion for frontier exploration
- High-resolution water-saturation prediction using geostatistical inversion and neural network methods
- Improving seismic fault detection by super-attribute-based classification
- Introduction to this special section: Data analytics and machine learning
- Introduction to this special section: Machine learning applications
- Lithofacies-dependent rock-physics templates of an unconventional shale reservoir on the North Slope, Alaska
- Machine Learning: A Deep Learning Approach for Seismic Structural Evaluation
- Machine learning and geophysical inversion — A numerical study
- Machine learning and learning from machines
- Machine learning as a tool for geologists
- Machine Learning in Rock Facies Classification: An Application of XGBoost
- Machine learning in the interpreter’s toolbox: unsupervised, supervised, and deep learning applications
- Machine learning regressors and their metrics to predict synthetic sonic and mechanical properties
- Machine learning systems open up access to large volumes of valuable information lying dormant in unstructured documents
- Machine Learning: Using Optimized KNN (K-Nearest Neighbors) to Predict the Facies Classifications
- Multiresolution neural networks for tracking seismic horizons from few training images
- Neural networks for geophysicists and their application to seismic data interpretation
- Oilfield Data Analytics: Linking Fracturing Chemistry and Well Productivity
- Open access and open science progression
- Predicting Gas Production Using Machine Learning Methods: A Case Study
- Production Metric Analytics in the Wolfcamp Formation
- Rock typing in the Upper Devonian-Lower Mississippian Woodford Shale Formation, Oklahoma, USA
- Sedimentary environment prediction of grain-size data based on machine learning approach
- Seismic attribute selection and clustering to detect and classify surface waves in multicomponent seismic data by using k-means algorithm
- Seismic attributes and analogs to characterize a large fold in the Taranaki Basin
- Seismic graph analysis to aid seismic interpretation
- Seismic methods for fluid discrimination in areas with complex geologic history — A case example from the Barents Sea
- Seismic structure interpretation based on machine learning: A case study in coal mining
- Spectral similarity fault enhancement
- Statistical approach to neural network imaging of karst systems in 3D seismic reflection data
- The Rise of the Machines, Analytics, and the Digital Oilfield: Artificial Intelligence in the Age of Machine Learning and Cognitive Analytics
- The use of predictive analytics for hydrocarbon exploration in the Denver-Julesburg Basin
- The Western Australia Modeling project — Part 2: Seismic validation
- Three data analytics party tricks
- Tracking 3D seismic horizons with a new hybrid tracking algorithm
- Validated artificial neural networks in determining petrophysical properties: A case study from Colombia
- Visualize geoscience education — Earth Science Week 2015
- Machine Learning in Petroleum Geoscience Constructing EarthNET
- Uncover-ML: a machine learning pipeline for geoscience data analysis
- Machine learning as a tool for geologists
- Is Machine Learning taking productivity in petroleum geoscience on a Moore's Law trajectory?
- Lecture Notes on Machine Learning Methods in Geosciences
- Toward the Geoscience Paper of the Future: Best practices for documenting and sharing research from data to software to provenance
- Machine learning in geosciences and remote sensing
- 70 years of machine learning in geoscience in review
- Machine Learning for the Geosciences: Challenges and Opportunities
- Use of Machine Learning and Artificial Intelligence In Earth Science
olutokijohn / geoscience-ml-papers Goto Github PK
View Code? Open in Web Editor NEWThis project forked from manjunath5496/geoscience-ml-papers
"Trials are medicines which our gracious and wise Physician prescribes because we need them; and he proportions the frequency and weight of them to what the case requires. Let us trust his skill and thank him for his prescription."― Isaac Newton