70 years of machine learning in geoscience in review
JS Dramsch - Advances in geophysics, 2020 - Elsevier
This review gives an overview of the development of machine learning in geoscience. A
thorough analysis of the codevelopments of machine learning applications throughout the …
thorough analysis of the codevelopments of machine learning applications throughout the …
[HTML][HTML] Impact of dataset size and convolutional neural network architecture on transfer learning for carbonate rock classification
Modern geological practices, in both industry and academia, rely largely on a legacy of
observational data at a range of scales. However, widespread ambiguities in the …
observational data at a range of scales. However, widespread ambiguities in the …
A machine learning approach to facies classification using well logs
In this work we describe a machine learning pipeline for facies classification based on
wireline logging measurements. The algorithm has been designed to work even with a …
wireline logging measurements. The algorithm has been designed to work even with a …
A gradient boosting decision tree algorithm combining synthetic minority oversampling technique for lithology identification
K Zhou, J Zhang, Y Ren, Z Huang, L Zhao - Geophysics, 2020 - library.seg.org
Lithology identification based on conventional well-logging data is of great importance for
geologic features characterization and reservoir quality evaluation in the exploration and …
geologic features characterization and reservoir quality evaluation in the exploration and …
Porosity prediction with uncertainty quantification from multiple seismic attributes using random forest
Inferring porosity of subsurface from seismic data is of profound significance to many fields
of Earth science and engineering applications, including but not limited to: hydrocarbon …
of Earth science and engineering applications, including but not limited to: hydrocarbon …
A comparison of deep machine learning and Monte Carlo methods for facies classification from seismic data
Among the large variety of mathematical and computational methods for estimating reservoir
properties such as facies and petrophysical variables from geophysical data, deep machine …
properties such as facies and petrophysical variables from geophysical data, deep machine …
Fluid and lithofacies prediction based on integration of well-log data and seismic inversion: A machine-learning approach
Seismic prediction of fluid and lithofacies distribution is of great interest to reservoir
characterization, geologic model building, and flow unit delineation. Inferring fluids and …
characterization, geologic model building, and flow unit delineation. Inferring fluids and …
Unsupervised seismic random noise attenuation based on deep convolutional neural network
Random noise attenuation is one of the most essential steps in seismic signal processing.
We propose a novel approach to attenuate seismic random noise based on deep …
We propose a novel approach to attenuate seismic random noise based on deep …
Automated well-log processing and lithology classification by identifying optimal features through unsupervised and supervised machine-learning algorithms
The application of specialized machine learning (ML) in petroleum engineering and
geoscience is increasingly gaining attention in the development of rapid and efficient …
geoscience is increasingly gaining attention in the development of rapid and efficient …
Lithology identification from well-log curves via neural networks with additional geologic constraint
Lithology identification is of great importance in reservoir characterization. Recently, many
researchers have applied machine-learning techniques to solve lithology identification …
researchers have applied machine-learning techniques to solve lithology identification …